@Preamble{
"\ifx \undefined \TM \def \TM {${}^{\sc TM}$} \fi"
}
@String{ack-nhfb = "Nelson H. F. Beebe,
University of Utah,
Department of Mathematics, 110 LCB,
155 S 1400 E RM 233,
Salt Lake City, UT 84112-0090, USA,
Tel: +1 801 581 5254,
FAX: +1 801 581 4148,
e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|,
\path|beebe@computer.org| (Internet),
URL: \path|https://www.math.utah.edu/~beebe/|"}
@String{j-INT-J-IMAGE-GRAPHICS = "International Journal of Image and Graphics
(IJIG)"}
@Article{Magnenat-Thalmann:2001:DSC,
author = "N. Magnenat-Thalmann and P. Volino and L. Moccozet",
title = "Designing and Simulating Clothes",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "1",
pages = "1--??",
month = jan,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:38 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hong:2001:IST,
author = "P. Hong and Z. Wen and T. S. Huang",
title = "{iFACE}: a {$3$D} Synthetic Talking Face",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "1",
pages = "19--??",
month = jan,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:38 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nakamae:2001:OPR,
author = "E. Nakamae",
title = "An Overview of Photo-Realism for Outdoor Scenes",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "1",
pages = "27--??",
month = jan,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:38 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2001:CIB,
author = "J. Li and H.-Y. Shum and Y.-Q. Zhang",
title = "On the Compression of Image Based Rendering Scene: a
Comparison among Block, Reference and Wavelet Coders",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "1",
pages = "45--??",
month = jan,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:38 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hanjalic:2001:RAV,
author = "A. Hanjalic and R. L. Lagendijk and J. Biemond",
title = "Recent Advances in Video Content Analysis: From Visual
Features to Semantic Video Segments",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "1",
pages = "63--??",
month = jan,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:38 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Feng:2001:CBR,
author = "D. Feng",
title = "Content-Based Retrieval of Multimedia Information",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "1",
pages = "83--??",
month = jan,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:38 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wayman:2001:FBA,
author = "J. L. Wayman",
title = "Fundamentals of Biometric Authentication
Technologies",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "1",
pages = "93--??",
month = jan,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:38 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bouvier:2001:TTO,
author = "E. Bouvier and E. Gobbetti",
title = "{TOM}: Totally Ordered Mesh a Multiresolution
Structure for Time Critical Graphics Applications",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "1",
pages = "115--??",
month = jan,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:38 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shu:2001:APV,
author = "W. Shu and G. Rong and Z. Bian and D. Zhang",
title = "Automatic Palmprint Verification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "1",
pages = "135--??",
month = jan,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:38 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Luo:2001:RCD,
author = "Y. Luo and R. Galli and D. S{\'a}nchez and A. Bennasar
and J. Forn{\'e}s and J. C. Serra and J. M. Hu{\'e}scar
and J. Gay{\`a}",
title = "A Remote Cooperative Design System Using Interactive
{$3$D} Graphics",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "1",
pages = "153--??",
month = jan,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:38 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pal:2001:FIP,
author = "S. K. Pal",
title = "Fuzzy Image Processing and Recognition: Uncertainty
Handling and Applications",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "169--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yan:2001:HFI,
author = "H. Yan",
title = "Human Face Image Processing Techniques",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "197--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gavrilova:2001:CLI,
author = "M. Gavrilova and J. Rokne",
title = "Computing Line Intersections",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "217--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chiang:2001:SVC,
author = "T. Chiang and Y.-Q. Zhang",
title = "Stereoscopic Video Coding Using a Fast and Robust
Affine Motion Search",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "231--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Oh:2001:ESQ,
author = "K.-M. Oh and J.-D. Choi and C.-S. Lee and C.-J. Park
and E.-T. Lee",
title = "An Efficient and Simple Quad Edge Conversion of
Polygonal Mainfold Objects",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "251--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Heng:2001:INV,
author = "P.-A. Heng and H. Sun and K.-W. Chen and T.-T. Wong",
title = "Interactive Navigation of Virtual Vessel Tracking with
{$3$D} Intelligent Scissors",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "273--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cai:2001:MPP,
author = "J. Cai and Z.-Q. Liu",
title = "{Markov} Process in Pattern Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "287--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tadamura:2001:FRW,
author = "K. Tadamura and X. Qin and G. Jiao and E. Nakamae",
title = "Fast Rendering Water Surface for Outdoor Scenes",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "313--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pan:2001:LDM,
author = "Z. Pan and M. Zhang and K. Zhou and C. Cheng and J.
Shi",
title = "Level of Detail and Multi-Resolution Modeling for
Virtual Prototyping",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "329--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Amin:2001:PSC,
author = "A. Amin and R. Shiu",
title = "Page Segmentation and Classification Utilizing
Bottom-Up Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "345--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rodriguez:2001:GAD,
author = "W. Rodriguez and M. Last and A. Kandel and H. Bunke",
title = "Geometric Approach to Data Mining",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "2",
pages = "363--??",
month = apr,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Worring:2001:IRC,
author = "M. Worring and T. Gevers",
title = "Interactive Retrieval of Color Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "3",
pages = "387--??",
month = jul,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jaimes:2001:LSV,
author = "A. Jaimes and S.-F. Chang",
title = "Learning Structured Visual Detectors from User Input
at Multiple Levels",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "3",
pages = "415--??",
month = jul,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ngo:2001:RAC,
author = "C.-W. Ngo and T.-C. Pong and H.-J. Zhang",
title = "Recent Advances in Content-Based Video Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "3",
pages = "445--??",
month = jul,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lienhart:2001:RTD,
author = "R. Lienhart",
title = "Reliable Transition Detection in Videos: a Survey
and Practitioner's Guide",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "3",
pages = "469--??",
month = jul,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dimitrova:2001:VCU,
author = "N. Dimitrova and L. Agnihotri and G. Wei",
title = "Video Classification Using Object Tracking",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "3",
pages = "487--??",
month = jul,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lin:2001:VCR,
author = "T. Lin and H. J. Zhang and Q.-Y. Shi",
title = "Video Content Representation for Shot Retrieval and
Scene Extraction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "3",
pages = "507--??",
month = jul,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pereira:2001:MSM,
author = "F. Pereira and R. Koenen",
title = "{MPEG-7}: a Standard for Multimedia Content
Description",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "3",
pages = "527--??",
month = jul,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wu:2001:MTD,
author = "P. Wu and Y. Choi and Y. M. Ro and C. S. Won",
title = "{MPEG-7} Texture Descriptors",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "3",
pages = "547--??",
month = jul,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Feb 26 12:00:39 MST 2002",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2001:AIG,
author = "W. Liu and T. Xin and Y. Xu and H. Shum and H.
Zhong",
title = "Artistic Image Generation by Deviation Mapping",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "4",
pages = "565--??",
month = oct,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2001:DCS,
author = "G. Li and X. Li and H. Li",
title = "Discrete Clothoid Spline Surfaces on Open Meshes",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "4",
pages = "575--??",
month = oct,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Prasad:2001:TTC,
author = "M. V. N. K. Prasad and K. K. Shukla",
title = "Tree Triangular Coding Image Compression Algorithms",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "4",
pages = "591--??",
month = oct,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Parker:2001:RSD,
author = "J. R. Parker and J. Pivovarov",
title = "Recognizing Symbols by Drawing Them",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "4",
pages = "605--??",
month = oct,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2001:EQD,
author = "B. P. Kumar and P. Gupta and C. J. Hwang",
title = "An Efficient Quadtree Datastructure for Neighbor
Finding Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "4",
pages = "619--??",
month = oct,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gavrilova:2001:TAC,
author = "M. L. Gavrilova and M. H. Alsuwaiyel",
title = "Two Algorithms for Computing the {Euclidean} Distance
Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "4",
pages = "635--??",
month = oct,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Stetten:2001:AFC,
author = "G. D. Stetten and R. Drezek",
title = "Active {Fourier} Contour Applied to Real Time {$3$D}
Ultrasound of the Heart",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "4",
pages = "647--??",
month = oct,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tamburo:2001:GOP,
author = "R. J. Tamburo and G. D. Stetten",
title = "Gradient-Oriented Profiles for Boundary
Parameterization and Their Application to Core Atoms
Towards Shape Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "4",
pages = "659--??",
month = oct,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Suri:2001:MSG,
author = "J. Suri and D. Wu and L. Reden and J. Gao and S. Singh
and S. Laxminarayan",
title = "Modeling Segmentation Via Geometric Deformable
Regularizers, {PDE} and Level Sets in Still and Motion
Imagery: a Revisit",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "4",
pages = "681--??",
month = oct,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2001:AI,
author = "Anonymous",
title = "Author Index",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "1",
number = "4",
pages = "735--??",
month = oct,
year = "2001",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lee:2002:AIV,
author = "J. Lee",
title = "{ABSolute}: An Information Visualization System for
Decision Support in Sourcing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "1",
pages = "1--??",
month = jan,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kitts:2002:LSM,
author = "B. Kitts and K. Hetherington-Young and M. Vrieze",
title = "Large-Scale Mining, Discovery and Visualization of
{WWW} User Clickpaths",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "1",
pages = "21--??",
month = jan,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Unwin:2002:DMG,
author = "A. R. Unwin and H. Hofmann and A. F. X. Wilhelm",
title = "Direct Manipulation Graphics for Data Mining",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "1",
pages = "49--??",
month = jan,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Fischer:2002:AID,
author = "S. Fischer and H. Bunke",
title = "Automatic Identification of Diatoms Using Visual
Human-Interpretable Features",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "1",
pages = "67--??",
month = jan,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pratt:2002:SPC,
author = "K. B. Pratt and E. Fink",
title = "Search for Patterns in Compressed Time Series",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "1",
pages = "89--??",
month = jan,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Last:2002:PBA,
author = "M. Last and A. Kandel",
title = "Perception-Based Analysis of Engineering Experiments
in the Semiconductor Industry",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "1",
pages = "107--??",
month = jan,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Poulet:2002:FVV,
author = "F. Poulet",
title = "Full-View: a Visual Data-Mining Environment",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "1",
pages = "127--??",
month = jan,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Solka:2002:VFA,
author = "J. L. Solka and C. E. Priebe and B. T. Clark",
title = "A Visualization Framework for the Analysis of
Hyperdimensional Data",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "1",
pages = "145--??",
month = jan,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ren:2002:MDT,
author = "Y. Ren and C. S. Chua and Y. K. Ho",
title = "Motion Detection from Time-Varied Background",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "2",
pages = "163--??",
month = apr,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kanatani:2002:MSS,
author = "K. Kanatani",
title = "Motion Segmentation by Subspace Separation: Model
Selection and Reliability Evaluation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "2",
pages = "179--??",
month = apr,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2002:SVW,
author = "L. Wang and K. L. Chan and X.-J. Xiong",
title = "A Sub-Vector Weighting Scheme for Image Retrieval with
Relevance Feedback",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "2",
pages = "199--??",
month = apr,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Adams:2002:FBA,
author = "B. Adams and C. Dorai and S. Venkatesh",
title = "Finding the Beat: An Analysis of the Rhythmic Elements
of Motion Pictures",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "2",
pages = "215--??",
month = apr,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Luo:2002:EG,
author = "B. Luo and E. Hancock and R. Wilson",
title = "Eigenspaces for Graphs",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "2",
pages = "247--??",
month = apr,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hild:2002:RSS,
author = "M. Hild and K. Nishijima",
title = "Reconstruction of {$3$D} Space Structure with a
Rotational Imaging System",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "2",
pages = "269--??",
month = apr,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2002:PAB,
author = "Y. Liu and C.-K. Wu and H.-T. Tsui",
title = "A Practical Approach for {$3$D} Building Modeling from
Uncalibrated Video Sequences",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "2",
pages = "287--??",
month = apr,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tamaki:2002:CDI,
author = "T. Tamaki and T. Yamamura and N. Ohnishi",
title = "Correcting Distortion of Image by Image Registration
with the Implicit Function Theorem",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "2",
pages = "309--??",
month = apr,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Iwata:2002:DOH,
author = "A. Iwata and K. Kato and K. Yamamoto",
title = "The Detection of Obstacles by the Horizon View
Camera",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "2",
pages = "331--??",
month = apr,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tissainayagam:2002:PMA,
author = "P. Tissainayagam and D. Suter",
title = "Performance Measures for Assessing Contour Trackers",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "2",
pages = "343--??",
month = apr,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2002:ROM,
author = "Yaming Wang and George Baciu",
title = "Robust object matching using a modified version of the
{Hausdorff} measure",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "3",
pages = "361--??",
month = jul,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:36:43 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{ElBadawy:2002:SBI,
author = "Ossama {El Badawy} and Mohamed Kamel",
title = "Shape-based image retrieval applied to trademark
images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "3",
pages = "375--??",
month = jul,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:36:52 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2002:OOB,
author = "Taehyung Wang and Phillip C. Y. Sheu",
title = "An object-oriented {BSP} tree algorithm for hidden
surface removal",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "3",
pages = "395--??",
month = jul,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:37:19 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kate:2002:TAA,
author = "Rohit Jaivant Kate and Prem Kalra and Subhashis
Banerjee",
title = "Towards an automatic approach for view-dependent
geometry",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "3",
pages = "413--??",
month = jul,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:37:26 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bao:2002:IRB,
author = "Paul Bao and Sung-Wai Hong",
title = "Image restoration based on generalized finite automata
encoded edge preserving regularization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "3",
pages = "425--??",
month = jul,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:37:33 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhang:2002:IER,
author = "Y. J. Zhang",
title = "Image engineering and related publications",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "3",
pages = "441--??",
month = jul,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ablameyko:2002:CSI,
author = "S. Ablameyko and V. Bereishik and M. Homenko and D.
Lagunovsky and N. Paramonova and O. Patsko",
title = "A complete system for interpretation of color maps",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "3",
pages = "453--??",
month = jul,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:37:39 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{You:2002:SSB,
author = "Jane You and David Zhang",
title = "Smart sensor: an on-board image processing system for
real-time remote sensing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "3",
pages = "481--??",
month = jul,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:37:44 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2002:E,
author = "Zhi-Qiang Liu",
title = "Editorial",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "501--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pu:2002:NNB,
author = "Her-Chang Pu and Chin-Teng Lin",
title = "A neural-network-based image resolution enhancement
scheme for image resizing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "503--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:37:57 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2002:ASS,
author = "Zhi-Qiang Liu",
title = "Adaptive subspace self-organizing map and its
applications in face recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "519--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shang:2002:RFS,
author = "Changjing Shang and Qiang Shen",
title = "Rough feature selection for neural network based image
classification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "541--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:05 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Schenker:2002:FCG,
author = "Adam Schenker and Mark Last and Horst Bunke and
Abraham Kandel",
title = "Fuzzy clustering with genetically adaptive scaling",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "557--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:10 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Miyamoto:2002:FMM,
author = "Sadaaki Miyamoto and Arnold C. Alanzado",
title = "Fuzzy $c$-means and mixture distribution models in the
presence of noise clusters",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "573--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:11 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wachs:2002:CFS,
author = "Juan Wachs and Helman Stern and Mark Last",
title = "Color face segmentation using a fuzzy min-max neural
network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "587--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:11 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Soodamani:2002:FHT,
author = "R. Soodamani and Z. Q. Liu",
title = "A fuzzy {Hough} transform approach to shape
description",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "603--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:11 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Loia:2002:FRC,
author = "Vincenzo Loia and Witold Pedrycz and Salvatore
Sessa",
title = "Fuzzy relation calculus in the compression and
decompression of fuzzy relations",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "617--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:12 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ke:2002:MST,
author = "Shih-Hao Ke and Tsu-Tian Lee",
title = "A multi-scale two-step fast search algorithm for block
motion estimation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "633--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:12 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kameyama:2002:CRM,
author = "Keisuke Kameyama and Kazuo Toraichi and Yukio
Kosugi",
title = "Constructive relaxation matching involving dynamical
model switching and its application to shape matching",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "655--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:12 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2002:AIV,
author = "Anonymous",
title = "Author index volume 2 (2002)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "2",
number = "4",
pages = "669--??",
month = oct,
year = "2002",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2003:Ea,
author = "Anonymous",
title = "Editorial",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "1",
pages = "1--??",
month = jan,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lang:2003:FSS,
author = "Christian A. Lang and Ambuj K. Singh",
title = "Faster Similarity Search for Multimedia Data Via Query
Transformations",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "1",
pages = "3--??",
month = jan,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:13 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Park:2003:SBS,
author = "Sanghyun Park and Wesley W. Chu",
title = "Similarity-Based Subsequence Search in Image Sequence
Databases",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "1",
pages = "31--??",
month = jan,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:13 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Oria:2003:VPV,
author = "Vincent Oria and M. Tamer {\"O}zsu",
title = "Views or Points of View on Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "1",
pages = "55--??",
month = jan,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:13 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chen:2003:FAF,
author = "Longbin Chen and Baogang Hu and Lei Zhang and Mingjing
Li and Hongjiang Zhang",
title = "Face Annotation for Family Photo Album Management",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "1",
pages = "81--??",
month = jan,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:14 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Prabhakar:2003:MLJ,
author = "Sunil Prabhakar and Rahul Chari",
title = "Minimizing Latency and Jitter for Large-Scale
Multimedia Repositories Through Prefix Caching",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "1",
pages = "95--??",
month = jan,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:14 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2003:CBI,
author = "Zhiyong Wang and Zheru Chi and Dagan Feng and Ah Chung
Tsoi",
title = "Content-Based Image Retrieval with Relevance Feedback
Using Adaptive Processing of Tree-Structure Image
Representation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "1",
pages = "119--??",
month = jan,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:14 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cucchiara:2003:SVT,
author = "Rita Cucchiara and Costantino Grana and Andrea
Prati",
title = "Semantic Video Transcoding Using Classes of
Relevance",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "1",
pages = "145--??",
month = jan,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Doulamis:2003:ECB,
author = "Anastasios Doulamis and Nikolaos Doulamis and Theodora
Varvarigou",
title = "Efficient Content-Based Image Retrieval Using Fuzzy
Organization and Optimal Relevance Feedback",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "1",
pages = "171--??",
month = jan,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tangelder:2003:PMR,
author = "Johan W. H. Tangelder and Remco C. Veltkamp",
title = "Polyhedral Model Retrieval Using Weighted Point Sets",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "1",
pages = "209--??",
month = jan,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2003:Eb,
author = "Anonymous",
title = "Editorial",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "2",
pages = "231--??",
month = apr,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yilmaz:2003:IBI,
author = "Ula{\c{s}} Yilmaz and Adem Yasar M{\"u}layim and
Volkan Atalay",
title = "An Image-Based Inexpensive {$3$D} Scanner",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "2",
pages = "235--??",
month = apr,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{El-Sana:2003:VDR,
author = "Jihad El-Sana and Neta Sokolovsky",
title = "View-Dependent Rendering for Large Polygonal Models
over Networks",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "2",
pages = "265--??",
month = apr,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cai:2003:SPA,
author = "Kangying Cai and Wencheng Wang and Guangzheng Fei and
Enhua Wu",
title = "A Single-Pass Approach to Adaptive Simplification of
Out-of-Core Models",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "2",
pages = "291--??",
month = apr,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Xu:2003:FSB,
author = "Weiwei Xu and Zhigeng Pan and Mingmin Zhang",
title = "Footprint Sampling-Based Motion Editing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "2",
pages = "311--??",
month = apr,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2003:NRB,
author = "Wenyu Liu and Hua Li and Guangxi Zhu",
title = "Non-Rigid Body Interpolation Based on Generalized
Morphologic Morphing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "2",
pages = "325--??",
month = apr,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Arya:2003:PFA,
author = "Ali Arya and Babak Hamidzadeh",
title = "Personalized Face Animation in Showface System",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "2",
pages = "345--??",
month = apr,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Geng:2003:PUI,
author = "Weidong Geng and Wolfgang Strauss and Monika
Fleischmann and Vladimir Elistratov and Marina Kolesnik",
title = "Perceptual User Interface in Virtual Shopping
Environment",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "2",
pages = "365--??",
month = apr,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wan:2003:OTA,
author = "Huagen Wan and Shuming Gao and Qunsheng Peng and Yiyu
Cai",
title = "Optimization Techniques for Assembly Planning of
Complex Models in Large-Scale Virtual Environments",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "2",
pages = "379--??",
month = apr,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2003:Ec,
author = "Anonymous",
title = "Editorial",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "3",
pages = "399--??",
month = jul,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ailisto:2003:RFI,
author = "Heikki Ailisto and Mikko Lindholm and Pauli
Tikkanen",
title = "A Review of Fingerprint Image Enhancement Methods",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "3",
pages = "401--??",
month = jul,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tico:2003:RAS,
author = "Marius Tico and Pauli Kuosmanen",
title = "A Remote Authentication System Using Fingerprints",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "3",
pages = "425--??",
month = jul,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Konvalinka:2003:VSF,
author = "Ira Konvalinka",
title = "Verification Speed in Fingerprint-based Biometric
Systems",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "3",
pages = "447--??",
month = jul,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Miao:2003:SHF,
author = "Jun Miao and Hong Liu and Wen Gao and Hongming Zhang
and Gang Deng and Xilin Chen",
title = "A System for Human Face and Facial Feature Location",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "3",
pages = "461--??",
month = jul,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2003:MBA,
author = "Zhi-Qiang Liu and Jessica Y. Guo",
title = "A Model-based Approach to Hair Region Segmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "3",
pages = "481--??",
month = jul,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sun:2003:DSC,
author = "Jun Sun and Wenyuan Wang and Qing Zhuo and Chengyuan
Ma",
title = "Discriminatory Sparse Coding and Its Application to
Face Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "3",
pages = "503--??",
month = jul,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Schuckers:2003:UBB,
author = "Michael E. Schuckers",
title = "Using the Beta-Binomial Distribution to Assess
Performance of a Biometric Identification Device",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "3",
pages = "523--??",
month = jul,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2003:Ed,
author = "Anonymous",
title = "Editorial",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "4",
pages = "531--??",
month = oct,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Takizawa:2003:RML,
author = "Hotaka Takizawa and Kanae Shigemoto and Shinji
Yamamoto and Tohru Matsumoto and Yukio Tateno and
Takeshi Iinuma and Mitsuomi Matsumoto",
title = "A Recognition Method of Lung Nodule Shadows in {X}-Ray
{CT} Images Using {$3$D} Object Models",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "4",
pages = "533--??",
month = oct,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pratikakis:2003:RMD,
author = "Ioannis Pratikakis and Christian Barillot and Pierre
Hellier and Etienne Memin",
title = "Robust Multiscale Deformable Registration of {$3$D}
Ultrasound Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "4",
pages = "547--??",
month = oct,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Haraguchi:2003:TDR,
author = "Ryo Haraguchi and Naozo Sugimoto and Shigeru Eiho and
Yoshio Ishida",
title = "Three Dimensional Reconstruction of Coronary Arteries
by Using Registration and Texture-Mapping onto
Epicardial Surface on Nuclear {$3$D} Image",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "4",
pages = "567--??",
month = oct,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2003:EDE,
author = "Jiahui Wang and Hideo Saito and Shinji Ozawa and
Tomohiro Kuwahara and Toyonobu Yamashita and Motoji
Takahashi",
title = "Extraction of Dermo-Epidermal Surface from {$3$D}
Volumetric Images of Human Skin",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "4",
pages = "589--??",
month = oct,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Owada:2003:ECC,
author = "Shigeru Owada and Yoshihisa Shinagawa and Frank
Nielsen",
title = "Enumeration of Contour Correspondence",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "4",
pages = "609--??",
month = oct,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Oshiro:2003:KGO,
author = "Osamu Oshiro and Kumi Kamada and Masataka Imura and
Kunihiro Chihara and Eiji Toyota and Yasuo Ogasawara
and Fumihiko Kajiya",
title = "Kidney Glomerulus Observation in Interactive {VR}
Space",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "4",
pages = "629--??",
month = oct,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gunaratne:2003:EAF,
author = "Pujitha Gunaratne and Yukio Sato",
title = "Estimation of Asymmetry in Facial Actions for the
Analysis of Motion Dysfunction Due to Paralysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "4",
pages = "639--??",
month = oct,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hussain:2003:FSF,
author = "Muhammad Hussain and Yoshihiro Okada and Koichi
Niijima",
title = "Fast, Simple, Feature Preserving and Memory Efficient
Simplification of Triangle Meshes",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "4",
pages = "653--??",
month = oct,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2003:AIV,
author = "Anonymous",
title = "Author Index Volume 3 (2003)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "3",
number = "4",
pages = "671--??",
month = oct,
year = "2003",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bennamoun:2004:E,
author = "Mohammed Bennamoun",
title = "Editorial",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "1",
pages = "1--??",
month = jan,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jan 27 07:06:41 MST 2004",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hu:2004:SCR,
author = "Zhencheng Hu and Keiichi Uchimura",
title = "Solution of camera registration problem via
{$3$D}--{$2$D} parameterized model matching for on-road
navigation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "1",
pages = "3--??",
month = jan,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kanatani:2004:ATC,
author = "Kenichi Kanatani and Yasushi Kanazawa",
title = "Automatic thresholding for correspondence detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "1",
pages = "21--??",
month = jan,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kanatani:2004:ADC,
author = "Kenichi Kanatani and Naoya Ohta",
title = "Automatic detection of circular objects by ellipse
growing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "1",
pages = "35--??",
month = jan,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mamic:2004:ASS,
author = "G. Mamic and M. Bennamoun",
title = "Automated spline surface modeling and matching for
recognition of free-form objects",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "1",
pages = "51--??",
month = jan,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bhanu:2004:MLA,
author = "Bir Bhanu and Grinnell {Jones III}",
title = "Multiple look angle {SAR} recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "1",
pages = "85--??",
month = jan,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Toulminet:2004:FAS,
author = "Gwena{\"e}lle Toulminet and St{\'e}phane Mousset and
Abdelaziz Bensrhair",
title = "Fast and accurate stereo vision-based estimation of
{$3$D} position and axial motion of road obstacles",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "1",
pages = "99--??",
month = jan,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Roy:2004:MCU,
author = "Micha{\"e}l Roy and Sebti Foufou and Fr{\'e}d{\'e}ric
Truchetet",
title = "Mesh comparison using attribute deviation metric",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "1",
pages = "127--??",
month = jan,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:16 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Meegama:2004:FAP,
author = "Ravinda G. N. Meegama and Jagath C. Rajapakse",
title = "Fully Automated Peeling Technique for {T1}-Weighted,
High-Quality {MR} Head Scans",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "2",
pages = "141--??",
month = apr,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 06 07:38:17 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lukac:2004:PBO,
author = "Rastislav Lukac",
title = "Performance Boundaries of Optimal Weighted Median
Filters",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "2",
pages = "157--??",
month = apr,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gatos:2004:FIM,
author = "Basilios Gatos and Stavros J. Perantonis and Nikos
Papamarkos and Ioannis Andreadis",
title = "Fast Implementation of Morphological Operations Using
Binary Image Block Decomposition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "2",
pages = "183--??",
month = apr,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Palacios:2004:HBC,
author = "Rafael Palacios and Amar Gupta and Patrick S. Wang",
title = "Handwritten Bank Check Recognition of Courtesy
Amounts",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "2",
pages = "203--??",
month = apr,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sarkar:2004:GAB,
author = "Biswajit Sarkar and Lokendra Kumar Singh and Debranjan
Sarkar",
title = "A Genetic Algorithm-Based Approach for Detection of
Significant Vertices for Polygonal Approximation of
Digital Curves",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "2",
pages = "223--??",
month = apr,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mari:2004:CFF,
author = "Jean-Luc Mari and Jean Sequeira",
title = "Closed Free-Form Surface Geometrical Modeling a New
Approach with Global and Local Characterization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "2",
pages = "241--??",
month = apr,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Babu:2004:SGM,
author = "R. Venkatesh Babu and K. R. Ramakrishnan",
title = "Sprite Generation from {MPEG} Video Using Motion
Information",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "2",
pages = "263--??",
month = apr,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Alhichri:2004:AIR,
author = "Haikel S. Alhichri and Mohamed Kamel",
title = "Automatic Image Registration Using Virtual Circles",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "2",
pages = "281--??",
month = apr,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sarfraz:2004:SAC,
author = "Muhammad Sarfraz",
title = "Some Algorithms for Curve Design and Automatic Outline
Capturing of Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "2",
pages = "301--??",
month = apr,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Koyama:2004:VPR,
author = "Kazuhiro Koyama and Yoshiaki Tomizawa and Minoru
Okada",
title = "Vectorization and Precise Refractions In Beam
Tracing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "2",
pages = "325--??",
month = apr,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2004:Ea,
author = "Anonymous",
title = "Editorial",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "3",
pages = "341--??",
month = jul,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Abd-Almageed:2004:ADM,
author = "Wael Abd-Almageed and Christopher E. Smith",
title = "Active Deformable Models Using Density Estimation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "3",
pages = "343--??",
month = jul,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Giraldi:2004:ISI,
author = "Gilson A. Giraldi and Antonio A. F. Oliveira",
title = "Invariant Snakes and Initialization of Deformable
Models",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "3",
pages = "363--??",
month = jul,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shao:2004:APS,
author = "Fan Shao and Keck Voon Ling and Wan Sing Ng",
title = "Automatic {$3$D} Prostate Surface Detection from
{TRUS} with Level Sets",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "3",
pages = "385--??",
month = jul,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tohka:2004:DMA,
author = "Jussi Tohka and Jouni M. Mykk{\"a}nen",
title = "Deformable Mesh for Automated Surface Extraction from
Noisy Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "3",
pages = "405--??",
month = jul,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pujol:2004:TSS,
author = "Oriol Pujol and Petia Radeva",
title = "Texture Segmentation By Statistical Deformable
Models",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "3",
pages = "433--??",
month = jul,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yazdi:2004:IFP,
author = "Mehran Yazdi and Andre Zaccarin",
title = "Inter-Frame Prediction of Medical and Videophone
Sequences: a Deformable Triangle-Based Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "3",
pages = "453--??",
month = jul,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tsechpenakis:2004:PBB,
author = "Gabriel Tsechpenakis and Nicolas Tsapatsoulis and
Stefanos Kollias",
title = "Probabilistic Boundary-Based Contour Tracking with
Snakes In Natural Cluttered Video Sequences",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "3",
pages = "469--??",
month = jul,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dornaika:2004:FFF,
author = "F. Dornaika and J. Ahlberg",
title = "Face and Facial Feature Tracking Using Deformable
Models",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "3",
pages = "499--??",
month = jul,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2004:Eb,
author = "Anonymous",
title = "Editorial",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "4",
pages = "533--??",
month = oct,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2004:DCI,
author = "Yongmei Michelle Wang and Jingdan Zhang and Zhunping
Zhang and Baining Guo",
title = "Directional Coherence Interpolation for
Three-Dimensional Gray-Level Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "4",
pages = "535--??",
month = oct,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Theobalt:2004:CFT,
author = "Christian Theobalt and Marcus A. Magnor and Pascal
Sch{\"u}ler and Hans-Peter Seidel",
title = "Combining {$2$D} Feature Tracking and Volume
Reconstruction for Online Video-Based Human Motion
Capture",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "4",
pages = "563--??",
month = oct,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Matsushita:2004:LSI,
author = "Yasuyuki Matsushita and Stephen Lin and Heung-Yeung
Shum and Xin Tong and Sing Bing Kang",
title = "Lighting and Shadow Interpolation Using Intrinsic
Lumigraphs",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "4",
pages = "585--??",
month = oct,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yu:2004:SCS,
author = "Jingyi Yu and Leonard McMillan and Steven Gortler",
title = "Surface Camera ({SCAM}) Light Field Rendering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "4",
pages = "605--??",
month = oct,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2004:UAR,
author = "Ruigang Yang and Marc Pollefeys and Hua Yang and Greg
Welch",
title = "A Unified Approach To Real-Time, Multi-Resolution,
Multi-Baseline {$2$D} View Synthesis and {$3$D} Depth
Estimation Using Commodity Graphics Hardware",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "4",
pages = "627--??",
month = oct,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pajarola:2004:DFD,
author = "Renato Pajarola and Miguel Sainz and Yu Meng",
title = "{DMesh}: Fast Depth-Image Meshing and Warping",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "4",
pages = "653--??",
month = oct,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Brown:2004:IGR,
author = "Michael S. Brown and W. Brent Seales",
title = "Incorporating Geometric Registration with {PC}-Cluster
Rendering for Flexible Tiled Displays",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "4",
pages = "683--??",
month = oct,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sun:2004:IBT,
author = "Jing Sun and George Baciu and Xiaobo Yu and Mark
Green",
title = "Image-Based Template Generation of Road Networks for
Virtual Maps",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "4",
pages = "701--??",
month = oct,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2004:AIV,
author = "Anonymous",
title = "Author Index (Volume 4)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "4",
number = "4",
pages = "721--??",
month = oct,
year = "2004",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 6 06:44:13 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2005:I,
author = "Anonymous",
title = "Introduction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "1",
pages = "1--??",
month = jan,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 12 05:16:34 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Voloshynovskiy:2005:ITD,
author = "Sviatoslav Voloshynovskiy and Frederic Deguillaume and
Oleksiy Koval and Thierry Pun",
title = "Information-Theoretic Data-Hiding: Recent Achievements
and Open Problems",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "1",
pages = "5--??",
month = jan,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 12 05:16:34 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lee:2005:IWR,
author = "Choong-Hoon Lee and Heung-Kyu Lee and Youngho Suh",
title = "Image Watermarking Resistant to Combined Geometric and
Removal Attacks",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "1",
pages = "37--??",
month = jan,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 12 05:16:34 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lu:2005:BIW,
author = "Haiping Lu and Yun Q. Shi and Alex C. Kot and Lihui
Chen",
title = "Binary Image Watermarking Through Blurring and Biased
Binarization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "1",
pages = "67--??",
month = jan,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 12 05:16:34 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Echizen:2005:PAV,
author = "Isao Echizen and Yasuhiro Fujii and Takaaki Yamada and
Satoru Tezuka and Hiroshi Yoshiura",
title = "Perceptually Adaptive Video Watermarking Using Motion
Estimation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "1",
pages = "89--??",
month = jan,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 12 05:16:34 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2005:DBV,
author = "Hongmei Liu and Jiwu Huang and Yun Q. Shi",
title = "{DWT}-Based Video Data Hiding Robust to {MPEG}
Compression and Frame Loss",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "1",
pages = "111--??",
month = jan,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 12 05:16:34 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sun:2005:CSS,
author = "Qibin Sun and Shuiming Ye and Ching-Yung Lin and
Shih-Fu Chang",
title = "A Crypto Signature Scheme for Image Authentication
over Wireless Channel",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "1",
pages = "135--??",
month = jan,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 12 05:16:34 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Piva:2005:SRA,
author = "Alessandro Piva and Franco Bartolini and Roberto
Caldelli",
title = "Self Recovery Authentication of Images in the {DWT}
Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "1",
pages = "149--??",
month = jan,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 12 05:16:34 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sallee:2005:MBM,
author = "Phil Sallee",
title = "Model-Based Methods for Steganography and
Steganalysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "1",
pages = "167--??",
month = jan,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 12 05:16:34 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gang:2005:CNI,
author = "Litao Gang and Ali N. Akansu",
title = "Cover Noise Interference Suppression in Multimedia
Data Hiding",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "1",
pages = "191--??",
month = jan,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 12 05:16:34 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cheriet:2005:SFB,
author = "Mohamed Cheriet and Jean-Christophe Demers and Sylvain
Deblois",
title = "Shock Filter-Based Diffusion Fields --- Application to
Grayscale Character Image Processing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "209--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Amin:2005:RST,
author = "Adnan Amin and Sue Wu",
title = "A Robust System for Thresholding and Skew Detection in
Mixed Text\slash Graphics Documents",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "247--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dawoud:2005:NSN,
author = "Amer Dawoud and Mohamed Kamel",
title = "Natural Skeletonization: New Approach for the
Skeletonization of Handwritten Characters",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "267--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chi:2005:DIB,
author = "Zheru Chi and Qing Wang",
title = "Document Image Binarization with Feedback for
Improving Character Segmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "281--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Brown:2005:DRC,
author = "Michael S. Brown and Yau-Chat Tsoi",
title = "Distortion Removal for Camera-Imaged Print Materials
Using Boundary Interpolation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "311--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lelandais:2005:STL,
author = "S. Lelandais and L. Boutte and J. Plantier",
title = "Shape from Texture: Local Scales and Vanishing Line
Computation to Improve Results for Macrotextures",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "329--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2005:AEM,
author = "Xiuying Wang and David Dagan Feng",
title = "Automatic Elastic Medical Image Registration Based On
Image Intensity",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "351--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhao:2005:HPR,
author = "Jianhui Zhao and Ling Li and Kwoh Chee Keong",
title = "Human Posture Reconstruction and Animation from
Monocular Images Based on Genetic Algorithms",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "371--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bing:2005:EPR,
author = "Cheng Bing and Wang Ying and Zheng Nanning and Bian
Zhengzhong",
title = "An Efficient {$3$D} Plenoptic Representation for
Approximating a Path of Motion to a Curved Line",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "397--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2005:PVR,
author = "Wencheng Wang and Hanqiu Sun and Enhua Wu",
title = "Projective Volume Rendering by Excluding Occluded
Voxels",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "413--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kang:2005:NVE,
author = "Hyung W. Kang",
title = "Nonphotorealistic Virtual Environment Navigation From
Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "2",
pages = "433--??",
month = apr,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Feb 7 16:17:59 MST 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ratschek:2005:SHM,
author = "Helmut Ratschek and Jon Rokne",
title = "{SCCI}-Hybrid Methods for {$2$D} Curve Tracing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "3",
pages = "447--479",
month = jul,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467805001859",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 5 06:13:03 MDT 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001859.pdf",
abstract = "A hybrid method for plotting 2-dimensional curves,
defined implicitly by equations of the form f(x,y) = 0
is presented. The method is extremely robust and
reliable and consists of Space Covering techniques,
Continuation principles and Interval analysis (i.e.
SCCI). The space covering, based on iterated
subdivision, guarantees that no curve branches or
isolated curve parts or even points are lost (which can
happen if grid methods are used). The continuation
method is initiated in a subarea as soon as it is
proven that the subarea contains only one smooth curve.
Such a subarea does not need to be subdivided further
so that the computation is accelerated as far as
possible with respect to the subdivision process. The
novelty of the SCCI-hybrid method is the intense use of
the implicit function theorem for controlling the steps
of the method. Although the implicit function theorem
has a rather local nature, it is empowered with global
properties by evaluating it in an interval environment.
This means that the theorem can provide global
information about the curve in a subarea such as
existence, non-existence, uniqueness of the curve or
even the presence of singular points. The information
gained allows the above-mentioned control of the
subarea and the decision of its further processing,
i.e. deleting it, subdividing it, switching to the
continuation method or preparing the plotting of the
curve in this subarea. The curves can be processed
mathematically in such a manner, that the derivation of
the plotted curve from the exact curve is as small as
desired (modulo the screen resolution).",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
keywords = "SCCI (Space Covering techniques, Continuation
principles and Interval analysis)",
}
@Article{Hicks:2005:AMC,
author = "B. J. Hicks and G. Mullineux and A. J. Medland",
title = "Automatic Model Creation for Kinematic Analysis and
Optimization of Engineering Systems",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "3",
pages = "481--499",
month = jul,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467805001860",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 5 06:13:03 MDT 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001860.pdf",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hong:2005:RPE,
author = "Jin-Hyuk Hong and Eun-Kyung Yun and Sung-Bae Cho",
title = "A Review of Performance Evaluation for Biometrics
Systems",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "3",
pages = "501--536",
month = jul,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467805001872",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 5 06:13:03 MDT 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001872.pdf",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bhowmick:2005:DMS,
author = "Partha Bhowmick and Arijit Bishnu and Bhargab Bikram
Bhattacharya and Malay Kumar Kundu and C. A. Murthy and
Tinku Acharya",
title = "Determination of Minutiae Scores for Fingerprint Image
Applications",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "3",
pages = "537--571",
month = jul,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467805001896",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 5 06:13:03 MDT 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001896.pdf",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pan:2005:FRR,
author = "Gang Pan and Zhaohui Wu",
title = "{$3$D} Face Recognition from Range Data",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "3",
pages = "573--593",
month = jul,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467805001884",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 5 06:13:03 MDT 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001884.pdf",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kharma:2005:PCC,
author = "Nawwaf Kharma and Ching Y. Suen and Pei F. Guo",
title = "{Palmprints}: a Cooperative Co-Evolutionary
Algorithm for Clustering Hand Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "3",
pages = "595--616",
month = jul,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467805001902",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 5 06:13:03 MDT 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001902.pdf",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Drago:2005:LAC,
author = "Fr{\'e}d{\'e}ric Drago and Norishige Chiba",
title = "Locally Adaptive Chromatic Restoration of Digitally
Acquired Paintings",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "3",
pages = "617--637",
month = jul,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467805001914",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 5 06:13:03 MDT 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001914.pdf",
abstract = "This article presents a semi-automatic procedure to
restore the visual appearance of aged paintings
converted to a digital form. The innovative
implementation of an image-processing algorithm based
on the Retinex theory of human vision alleviates layers
of yellowed varnish and dust, restores chromatic
balance and contrast, and recovers some of the original
painted details. This virtual cleaning of artwork is
totally non-intrusive and can be applied automatically
to color images of paintings or ancient illustrations.
Cleaned virtual reproductions help art historians and
restorers in their research and classification work,
and also show the artwork in good condition to a wide
audience while avoiding an always costly and dangerous
manual restoration.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Noyer:2005:SME,
author = "Jean-Charles Noyer and Christophe Boucher and Mohammed
Benjelloun",
title = "{$3$D} Structure and Motion Estimation from Range and
Intensity Images Using Particle Filtering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "3",
pages = "639--661",
month = jul,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467805001926",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 5 06:13:03 MDT 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001926.pdf",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Taylor-Hell:2005:SAR,
author = "Julia F. Taylor-Hell and Gladimir V. G. Baranoski and
Jon G. Rokne",
title = "State of the Art in the Realistic Modeling of Plant
Venation Systems",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "3",
pages = "663--678",
month = jul,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467805001938",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 5 06:13:03 MDT 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001938.pdf",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Marchesotti:2005:VPU,
author = "Luca Marchesotti and Carlo Regazzoni and Carlo
Bonamico and Fabio Lavagetto",
title = "Video Processing and Understanding Tools for Augmented
Multisensor Perception and Mobile User Interaction in
Smart Spaces",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "3",
pages = "679--698",
month = jul,
year = "2005",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780500194X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 5 06:13:03 MDT 2005",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S021946780500194X.pdf",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhou:2005:COV,
author = "Jianlong Zhou and Andreas D{\"o}ring and Klaus D.
T{\"o}nnies",
title = "Control of Object Visibility in Volume Rendering ---
a Distance-Based Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "699--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2005:NMA,
author = "Qiang Wang and Hongbo Chen and Xiaorong Xu and Haiyan
Liu",
title = "A Newly Modified Algorithm of {Hough Transform} for
Line Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "715--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhu:2005:FMR,
author = "En Zhu and Jian-Ping Yin and Guo-Min Zhang and
Chun-Feng Hu",
title = "Fingerprint Minutiae Relationship Representation and
Matching Based on Curve Coordinate System",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "729--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hu:2005:LBR,
author = "Yu-Chen Hu",
title = "Low Bit-Rate Image Compression Schemes Based on Vector
Quantization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "745--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2005:MSM,
author = "Yong-Jin Liu and Kai Tang and Ajay Joneja and Matthew
Ming-Fai Yuen",
title = "Multiresolution Shape Modeling and Editing in Reverse
Engineering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "765--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Singh:2005:FHS,
author = "Chandan Singh and Ekta Walia",
title = "Fast Hybrid Shading: an Application of Finite Element
Methods in {$3$D} Rendering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "789--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rahman:2005:ODE,
author = "M. Masudur Rahman and Seiji Ishikawa",
title = "Overcoming Dress Effect in Eigenspace",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "811--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rahman:2005:HPR,
author = "M. Masudur Rahman and Seiji Ishikawa",
title = "Human Posture Recognition: Eigenspace Tuning by a Mean
Eigenspace",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "825--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chen:2005:TSC,
author = "Zhe Chen and David Dagan Feng and Weidong Cai",
title = "Temporal and Spatial Compression of Dynamic Positron
Emission Tomography in Sinogram Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "839--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Deng:2005:PBS,
author = "Yuhui Deng and Frank Wang and Jiangling Zhang and Dan
Feng and Fang Wang and Hong Jiang",
title = "Push the Bottleneck of Streaming Media System from
Streaming Media Server to Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "859--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2005:AIV,
author = "Anonymous",
title = "Author Index (Volume 5)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "5",
number = "4",
pages = "871--??",
month = oct,
year = "2005",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kim:2006:VPC,
author = "Jaeho Kim and Hyungseok Kim and Kwangyun Wohn",
title = "Visibility Preprocessing for Complex {$3$D} Scenes
Using Hardware-Visibility Queries",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "1",
pages = "1--??",
month = jan,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Xu:2006:NCW,
author = "Qing Xu and Wei Wang and Shiqiang Bao",
title = "A New Computational Way to {Monte Carlo} Global
Illumination",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "1",
pages = "23--??",
month = jan,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2006:PMW,
author = "Li Li and Zhigeng Pan and David Zhang",
title = "A Public Mesh Watermarking Algorithm Based on Addition
Property of {Fourier Transform}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "1",
pages = "35--??",
month = jan,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cao:2006:DRD,
author = "Weiqun Cao and Hendrik Gaertner and Hannes Guddat and
Andreas M. Straube and Stefan Conrad and Ernst Kruijff
and Dirk Langenberg",
title = "Design Review in a Distributed Collaborative Virtual
Environment",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "1",
pages = "45--??",
month = jan,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tan:2006:IVE,
author = "Jiacheng Tan and Gordon J. Clapworthy and Igor R.
Belousov",
title = "The Integration of a Virtual Environment and {$3$D}
Modeling Tools in a Networked Robot System",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "1",
pages = "65--??",
month = jan,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Favier:2006:DAI,
author = "Pierre-Alexandre Favier and Pierre {De Loor}",
title = "From Decision to Action: Intentionality, a Guide for
the Specification of Intelligent Agent's Behavior",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "1",
pages = "87--??",
month = jan,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2006:IST,
author = "Ajay Kumar and David Zhang",
title = "Integrating Shape and Texture for Hand Verification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "1",
pages = "101--??",
month = jan,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zheng:2006:SBA,
author = "Qing-Fang Zheng and Wei Zeng and Wei-Qiang Wang and
Wen Gao",
title = "Shape-Based Adult Image Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "1",
pages = "115--??",
month = jan,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhan:2006:FER,
author = "Yongzhao Zhan and Jingfu Ye and Dejiao Niu and Peng
Cao",
title = "Facial Expression Recognition Based on {Gabor} Wavelet
Transformation and Elastic Templates Matching",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "1",
pages = "125--??",
month = jan,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2006:AMS,
author = "Xuelong Li and Yuan Yuan and Dacheng Tao",
title = "Artistic Mosaic Series Generation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "1",
pages = "139--??",
month = jan,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2006:DLL,
author = "Zhenlan Wang and Chee-Kong Chui and Yiyu Cai and
Chuan-Heng Ang and Swee-Hin Teoh",
title = "Dynamic Linear Level Octree-Based Volume Rendering
Methods for Interactive Microsurgical Simulation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "2",
pages = "155--??",
month = apr,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Fang:2006:MSI,
author = "Shiaofen Fang and Marwan Adada",
title = "Multi-Scale Iso-Surface Extraction for Volume
Visualization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "2",
pages = "173--??",
month = apr,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lakshmipathy:2006:TBI,
author = "Jagannathan Lakshmipathy and Wieslaw L. Nowinski and
Eric A. Wernert",
title = "Template-Based Isocontouring",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "2",
pages = "187--??",
month = apr,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Heng:2006:HNM,
author = "Pheng-Ann Heng and Tien-Tsin Wong and Ka-Man Leung and
Yim-Pan Chui and Hanqiu Sun",
title = "A Haptic Needle Manipulation Simulator for {Chinese}
Acupuncture Learning and Training",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "2",
pages = "205--??",
month = apr,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Guan:2006:VEC,
author = "Y. Q. Guan and Y. Y. Cai and M. Opas and Z. W. Xiong
and Y. T. Lee",
title = "A {VR} Enhanced Collaborative System for {$3$D}
Confocal Microscopic Image Processing and
Visualization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "2",
pages = "231--??",
month = apr,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lu:2006:BNS,
author = "Baifang Lu and Zhaowei Fan and Jianmin Zheng and Lin
Li",
title = "Bio-Native Shape Modeling and Virtual Reality for Bio
Education",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "2",
pages = "251--??",
month = apr,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Djemal:2006:AAC,
author = "Khalifa Djemal and William Puech and Bruno Rossetto",
title = "Automatic Active Contours Propagation in a Sequence of
Medical Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "2",
pages = "267--??",
month = apr,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Marcialis:2006:DLF,
author = "Gian Luca Marcialis and Fabio Roli",
title = "Decision-Level Fusion of {PCA} and {LDA}-Based Face
Recognition Algorithms",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "2",
pages = "293--??",
month = apr,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gong:2006:RTI,
author = "Minglun Gong and Yee-Hong Yang",
title = "{Rayset}: a Taxonomy for Image-Based Rendering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "3",
pages = "313--??",
month = jul,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Knopf:2006:FSR,
author = "George K. Knopf and Archana P. Sangole",
title = "Freeform Surface Reconstruction from Scattered Points
Using a Deformable Spherical Map",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "3",
pages = "341--??",
month = jul,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{AlAghbari:2006:RBS,
author = "Zaher {Al Aghbari}",
title = "Region-Based Semantic Image Classification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "3",
pages = "357--??",
month = jul,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tizhoosh:2006:RCA,
author = "Hamid R. Tizhoosh and Graham W. Taylor",
title = "Reinforced Contrast Adaptation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "3",
pages = "377--??",
month = jul,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhou:2006:SAR,
author = "Hong Zhou and Ray Seyfarth",
title = "Semi Automatic Registration of Partially Overlapped
Aerial Images Via Pattern Search Method",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "3",
pages = "393--??",
month = jul,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2006:OSV,
author = "Bin Li and David Zhang and Kuanquan Wang",
title = "Online Signature Verification by Combining Shape
Contexts and Local Features",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "3",
pages = "407--??",
month = jul,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{ElRube:2006:MRS,
author = "Ibrahim {El Rub{\'e}} and Naif Alajlan and Mohamed S.
Kamel and Maher Ahmed and George H. Freeman",
title = "{Mtar}: a Robust {$2$D} Shape Representation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "3",
pages = "421--??",
month = jul,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhong:2006:HDS,
author = "Yongmin Zhong and Bijan Shirinzadeh and Gursel Alici
and Julian Smith",
title = "Haptic Deformation Simulation with {Poisson}
Equation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "3",
pages = "445--??",
month = jul,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bao:2006:RTS,
author = "Paul Bao and Xiaohu Ma and Wan-Chi Siu",
title = "Real-Time Seamless Texture Synthesis Based on Patch
Quantization Clustering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "3",
pages = "475--??",
month = jul,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Fang:2006:NMF,
author = "Xianyong Fang and Zhigeng Pan and Gaoqi He and Li
Li",
title = "A New Method of Feature Based Image Mosaic",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "3",
pages = "497--??",
month = jul,
year = "2006",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 23 08:55:54 MDT 2006",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ng:2006:IFN,
author = "Geok See Ng and Sevki Erdogan and Daming Shi and Abdul
Wahab",
title = "Insight of Fuzzy Neural Systems in the Application of
Handwritten Digits Classification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "511--532",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467806002410",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cai:2006:DIW,
author = "Weiting Cai and Malek Adjouadi",
title = "Design and Implementation of Wavelet-Domain Video
Compression Using Multiresolution Motion Estimation and
Compensation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "533--549",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467806002471",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Faudot:2006:SVV,
author = "Dominique Faudot and Gilles Gesquiere",
title = "Study of Volume Variation of Implicit Objects",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "551--568",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467806002483",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Regentova:2006:ISU,
author = "Emma Regentova and Dongsheng Yao and Shahram Latifi
and Jun Zheng",
title = "Image Segmentation Using Ncut in the Wavelet Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "569--582",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467806002458",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yu:2006:CIR,
author = "Shengsheng Yu and Chaobing Huang and Jingli Zhou",
title = "Color Image Retrieval Based on Color-Texture-Edge
Feature Histograms",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "583--598",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467806002392",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chow:2006:FRR,
author = "S. K. Chow and K. L. Chan",
title = "Fast and Realistic Rendering of Deformable Virtual
Characters Using Impostor and Stencil Buffer",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "599--624",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467806002409",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Skala:2006:LAV,
author = "Vaclav Skala",
title = "Length, Area and Volume Computation in Homogeneous
Coordinates",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "625--639",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467806002422",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2006:RTM,
author = "Xiaoying Li and Enhua Wu",
title = "Relief Texture Mapping on Field Programmable Gate
Array",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "641--655",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780600246X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hardy:2006:HII,
author = "Alexandre Hardy and Willi-Hans Steeb",
title = "Harmonic Interpolation for Image Compression",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "657--675",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467806002434",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bi:2006:NNR,
author = "Dong-Liang Bi and Wei Guo and Ai-Dong Xu",
title = "A New Noise Removing Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "677--687",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467806002446",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2006:AIV,
author = "Anonymous",
title = "Author Index (Volume 6)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "6",
number = "4",
pages = "689--691",
month = oct,
year = "2006",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467806002446",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liang:2007:GEM,
author = "Jerome Zhengrong Liang and Hongbing Lu and Dimitris N.
Metaxas and Joseph M. Reinhardt",
title = "Guest Editorial: Medical Imaging Informatics --- An
Information Processing from Image Formation to
Visualization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "1--15",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002568",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hawkins:2007:ESU,
author = "William G. Hawkins",
title = "On the Equivalence of Stable and Unstable Forms of the
Inverse Circular Harmonic Transform Solution for the
{Radon} Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "17--33",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002519",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kao:2007:EAA,
author = "Chien-Min Kao and Yu Zou and Seungryong Cho and
Xiaochuan Pan",
title = "An Exact Analytic Approach to {$3$D} {PET} Image
Reconstruction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "35--54",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002520",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gao:2007:FVO,
author = "Xin Gao and Yuanmei Wang and Cishen Zhang",
title = "Fuzzy Vector Objective Optimization Algorithm for
Image Reconstruction from Incomplete Projections",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "55--69",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002532",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Huang:2007:AIE,
author = "Qiu Huang and Gengsheng L. Zeng and Grant T.
Gullberg",
title = "An Analytical Inversion of the $180^\circ$ Exponential
{Radon} Transform With a Numerically Generated Kernel",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "71--85",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002544",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Fan:2007:FAR,
author = "Yi Fan and Hongbing Lu and Chongyang Hao and Zhengrong
Liang and Zhiming Zhou",
title = "Fast Analytical Reconstruction of Gated Cardiac
{SPECT} with Non-Uniform Attenuation Compensation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "87--104",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002556",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2007:ISE,
author = "Zhenguo Wang and Christopher S. D. Lee and Wayne C.
Waltzer and Zhijia Yuan and Yingtian Pan",
title = "Interpixel-Shifted Endoscopic Optical Coherence
Tomography for in Vivo Bladder Cancer Diagnosis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "105--117",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780700257X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lei:2007:MRM,
author = "Tianhu Lei and Felix W. Wehrli",
title = "Magnetic Resonance ({MR}) Image Analysis --- a
Statistical Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "119--141",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002581",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cui:2007:DMF,
author = "Yunfeng Cui and Jing Bai and Yingmao Chen and Jiahe
Tian",
title = "A Digital Model Framework of Metabolic System Based on
Visible Human Data Set",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "143--157",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002593",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cai:2007:CVC,
author = "Wenli Cai and Gordon J. Harris and Hiroyuki Yoshida",
title = "Computation of Vesselness in {CTA} Images for Fast and
Interactive Vessel Segmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "159--176",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780700260X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Papaleo:2007:ASR,
author = "Laura Papaleo",
title = "An Approach to Surface Reconstruction Using Uncertain
Data",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "177--194",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002611",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tian:2007:DEV,
author = "Yun Tian and Chongyang Hao and Yi Wang and Guiqing He
and Jun Wei and Haitiao Zhao and Benhua Zhao",
title = "Dynamic Extraction for {VOI} from {CT} Images Based on
Volume Rendering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "1",
pages = "195--209",
month = jan,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002623",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2007:SMI,
author = "Xuelong Li and Jing Li and Dacheng Tao and Yuan
Yuan",
title = "A Similarity Metric in Image Searching",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "2",
pages = "211--225",
month = apr,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002635",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tao:2007:RCO,
author = "Ji Tao and Yap-Peng Tan and Wenmiao Lu",
title = "Robust Color Object Tracking with Application to
People Monitoring",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "2",
pages = "227--254",
month = apr,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002647",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Werghi:2007:LTD,
author = "Naoufel Werghi and Yijun Xiao and Paul Siebert",
title = "Labelling of Three Dimensional Human Body Scans: a
Topological Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "2",
pages = "255--272",
month = apr,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002659",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Livny:2007:DAP,
author = "Yotam Livny and Neta Sokolovsky and Jihad El-Sana",
title = "Dual Adaptive Paths for Multiresolution Hierarchies",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "2",
pages = "273--290",
month = apr,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002726",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Abdelwahab:2007:FCD,
author = "Ahmed A. Abdelwahab and Nora S. Muharram",
title = "A Fast Codebook Design Algorithm Based on a Fuzzy
Clustering Methodology",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "2",
pages = "291--302",
month = apr,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002714",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ayed:2007:TOB,
author = "Mohamed Ali Ben Ayed and Amine Samet and Nouri
Masmoudi",
title = "Toward an Optimal Block Motion Estimation Algorithm
for {H.264\slash AVC}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "2",
pages = "303--320",
month = apr,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002660",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Djebali:2007:CBM,
author = "M. Djebali and M. Melkemi and K. Melkemi and N.
Sapidis",
title = "Coiflet Based Methods for Range Image Processing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "2",
pages = "321--351",
month = apr,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002672",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhang:2007:ADM,
author = "Liang Zhang and Qingping Lin and Robert Gay and
Guangbin Huang and Norman Neo",
title = "An Autonomous Decentralized Multi-Server Framework for
Large Scale Collaborative Virtual Environments",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "2",
pages = "353--375",
month = apr,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002684",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nagendraswamy:2007:NMR,
author = "H. S. Nagendraswamy and D. S. Guru",
title = "A New Method of Representing and Matching Two
Dimensional Shapes",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "2",
pages = "377--405",
month = apr,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002696",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Verma:2007:CSI,
author = "Nishchal K. Verma and M. Hanmandlu",
title = "Color Segmentation Via Improved Mountain Clustering
Technique",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "2",
pages = "407--426",
month = apr,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002702",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Xu:2007:MCM,
author = "Xinyu Xu and Baoxin Li",
title = "Multiple Class Multiple-Instance Learning and Its
Application to Image Categorization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "3",
pages = "427--444",
month = jul,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780700274X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cheng:2007:OBP,
author = "Jun Cheng and Ronald Chung and Edmund Y. Lam and
Kenneth S. M. Fung and Yangsheng Xu",
title = "Optimization of Bit-Pairing Codification with Learning
for {$3$D} Reconstruction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "3",
pages = "445--462",
month = jul,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002763",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Song:2007:LLD,
author = "Mingli Song and Huiqiong Wang and Chun Chen",
title = "Local {Laplacian} Detail Learning for Face Aging
Manipulation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "3",
pages = "463--480",
month = jul,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002775",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2007:NBA,
author = "Weihai Li and Yuan Yuan",
title = "A New Blind Attack Procedure for {DCT}-Based Image
Encryption with Spectrum Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "3",
pages = "481--496",
month = jul,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002787",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhou:2007:IRD,
author = "Huiyu Zhou and Tangwei Liu and Faquan Lin and Yusheng
Pang and Ji Wu",
title = "Image Restoration and Detail Preservation by
{Bayesian} Estimation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "3",
pages = "497--514",
month = jul,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002738",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2007:TDI,
author = "Chunsheng Liu and Tianxu Zhang and Biyin Zhang",
title = "Turbulence Degraded Images Restoration Based on
Improved Multiframe Iterative Loops and Data Mining",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "3",
pages = "515--527",
month = jul,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002799",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2007:BCR,
author = "Kongqiao Wang and Yanming Zou and Hao Wang",
title = "{$1$D} Bar Code Reading on Camera Phones",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "3",
pages = "529--550",
month = jul,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002805",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shen:2007:ESQ,
author = "Jialie Shen and John Shepherd and Anne H. H. Ngu",
title = "An Empirical Study of Query Effectiveness Improvement
Via Multiple Visual Feature Integration",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "3",
pages = "551--581",
month = jul,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002751",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Xu:2007:FRG,
author = "Dong Xu and Dacheng Tao and Xuelong Li and Shuicheng
Yan",
title = "Face Recognition --- a Generalized Marginal {Fisher}
Analysis Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "3",
pages = "583--591",
month = jul,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002817",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2007:MLI,
author = "Anonymous",
title = "Machine Learning in Image and Graphics",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "3",
pages = "v--v",
month = jul,
year = "2007",
CODEN = "????",
DOI = "",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Brunn:2007:CRU,
author = "Meru Brunn and Mario Costa Sousa and Faramarz F.
Samavati",
title = "Capturing and Re-Using Artistic Styles with Reverse
Subdivision-Based Multiresolution Methods",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "593--615",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002829",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Curran:2007:APD,
author = "Kevin Curran and Neil McCaughley and Xuelong Li",
title = "Addressing the Problems of Detecting Faces with Neural
Networks",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "617--640",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002830",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Park:2007:FMO,
author = "Chan Jong Park and Kwang Yun Wohn",
title = "Fusion of the Magnetic and Optical Information for
Motion Capturing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "641--662",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002842",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Khare:2007:DCW,
author = "Ashish Khare and Uma Shanker Tiwary",
title = "{Daubechies} Complex Wavelet Transform Based Technique
for Denoising of Medical Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "663--687",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002854",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bougleux:2007:SDS,
author = "S{\'e}bastien Bougleux and Mahmoud Melkemi and
Abderrahim Elmoataz",
title = "Structure Detection from a {$3$D} Set of Points with
Anisotropic Alpha-Shapes",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "689--708",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002866",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ablameyko:2007:RED,
author = "Sergey V. Ablameyko and Seiichi Uchida",
title = "Recognition of Engineering Drawing Entities: Review of
Approaches",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "709--733",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002878",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hosny:2007:ECL,
author = "Khalid M. Hosny",
title = "Efficient Computation of {Legendre} Moments for Gray
Level Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "735--747",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780700288X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sanchez:2007:CFT,
author = "Danmary Sanchez and Malek Adjouadi and Nolan R. Altman
and Daniel Sanchez and Byron Bernal",
title = "Comprehensive {$3$D} Fiber Tracking As a New
Visualization System in Brain Studies",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "749--765",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002891",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2007:KGR,
author = "Jing Li and Yuan Yuan",
title = "Kernel {GBDA} for Relevance Feedback in Image
Retrieval",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "767--776",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467807002908",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhang:2007:LGB,
author = "Wenchao Zhang and Shiguang Shan and Xilin Chen and Wen
Gao",
title = "Local {Gabor} Binary Patterns Based on Mutual
Information for Face Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "777--793",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780700291X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2007:AIV,
author = "Anonymous",
title = "Author Index (Volume 7)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "7",
number = "4",
pages = "795--797",
month = oct,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780700291X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Feng:2008:TFM,
author = "Guiyu Feng and David Zhang and Jian Yang and Dewen
Hu",
title = "A Theoretical Framework for Matrix-Based Feature
Extraction Algorithms with Its Application to Image
Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "1",
pages = "1--23",
month = jan,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808002940",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lu:2008:NRM,
author = "Jianming Lu and Ling Wang and Yeqiu Li and Takashi
Yahagi",
title = "Noise Removal for Medical {X}-Ray Images in
Multiwavelet Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "1",
pages = "25--46",
month = jan,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808002952",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Manjunath:2008:DSD,
author = "A. V. N. Manjunath and K. G. Hemantha and S.
Noushath",
title = "Document Skew Detection --- a Novel Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "1",
pages = "47--59",
month = jan,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808002964",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2008:VBT,
author = "Shiueng-Bien Yang",
title = "Variable-Branch Tree-Structured Residual Vector
Quantization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "1",
pages = "61--80",
month = jan,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808002976",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Courty:2008:ANR,
author = "Nicolas Courty and Pierre Hellier",
title = "Accelerating {$3$D} Non-Rigid Registration Using
Graphics Hardware",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "1",
pages = "81--98",
month = jan,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808002988",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zou:2008:RCM,
author = "Jie Zou",
title = "Rose Curve Model and an Analytical Solution for
Estimating Its Parameters",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "1",
pages = "99--108",
month = jan,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780800299X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ho:2008:RSI,
author = "Charlotte Yuk-Fan Ho and Tai-Chiu Hsung and Daniel
Pak-Kong Lun and Bingo Wing-Kuen Ling and Peter
Kwong-Shun Tam and Wan-Chi Siu",
title = "Regularity Scalable Image Coding Based on Wavelet
Singularity Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "1",
pages = "109--134",
month = jan,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003003",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Fabrizio:2008:ASP,
author = "Jonathan Fabrizio and Jean Devars",
title = "An Analytical Solution to the Perspective-{$n$}-Point
Problem for Common Planar Camera and for Catadioptric
Sensor",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "1",
pages = "135--155",
month = jan,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003015",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chang:2008:LFE,
author = "Kuan-Tsung Chang and Tian-Yuan Shih",
title = "Linear Features Extraction with an Orientation
Constrained Probabilistic {Hough} Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "1",
pages = "157--168",
month = jan,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003027",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Didier:2008:HCP,
author = "Jean-Yves Didier and Fakhr-Eddine Ababsa and Malik
Mallem",
title = "Hybrid Camera Pose Estimation Combining Square
Fiducials Localization Technique and Orthogonal
Iteration Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "1",
pages = "169--188",
month = jan,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003039",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:01 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ge:2008:PBA,
author = "Jinghua Ge and Daniel J. Sandin and Tom Peterka and
Robert Kooima and Javier I. Girado and Andrew Johnson",
title = "A Point-Based Asynchronous Remote Visualization
Framework for Real-Time Virtual Reality",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "2",
pages = "189--207",
month = apr,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003040",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Peng:2008:RNP,
author = "Haoyu Peng and Hua Xiong and Zhen Liu and Jiaoying
Shi",
title = "Research of Nested Parallel Pipelines on Parallel
Graphics Rendering System",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "2",
pages = "209--222",
month = apr,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003052",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhang:2008:HAP,
author = "Fan Zhang and Hanqiu Sun and Leilei Xu and Kitlun
Lee",
title = "Hardware-Accelerated Parallel-Split Shadow Maps",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "2",
pages = "223--241",
month = apr,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003064",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ahlborn:2008:DIF,
author = "Benjamin A. Ahlborn and Oliver Kreylos and Sohail
Shafii and Bernd Hamann and Oliver G. Staadt",
title = "Design and Implementation of a Foveal Projection
Display",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "2",
pages = "243--263",
month = apr,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003076",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhong:2008:RDB,
author = "Yongmin Zhong and Bijan Shirinzadeh and Julian
Smith",
title = "Reaction-Diffusion Based Deformable Object
Simulation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "2",
pages = "265--280",
month = apr,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003088",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Baciu:2008:GSW,
author = "George Baciu and Liang Ma and Jinlian Hu",
title = "Generating Seams and Wrinkles for Virtual Clothing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "2",
pages = "281--297",
month = apr,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780800309X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Scherff:2008:IPI,
author = "Phillip-Christoph Scherff and George Baciu and Jinlian
Hu",
title = "Intuitive Parameterized Input Interface for
Proportional Reshaping of Human Bodies",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "2",
pages = "299--325",
month = apr,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003106",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2008:P,
author = "Anonymous",
title = "Preface",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "2",
pages = "vii--vii",
month = apr,
year = "2008",
CODEN = "????",
DOI = "",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Leung:2008:UID,
author = "Man-Kang Leung and Chi-Wing Fu",
title = "A User Interface Design for Acquiring Statistics from
Video",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "3",
pages = "327--349",
month = jul,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780800312X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Agarwal:2008:DWS,
author = "Rashmi Agarwal and M. S. Santhanam",
title = "Digital Watermarking in the Singular Vector Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "3",
pages = "351--368",
month = jul,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003131",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2008:VTA,
author = "Tao Yang and Jing Li and Quan Pan and Yong-Mei
Cheng",
title = "Visual Tracking with Automatic Confident Region
Extraction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "3",
pages = "369--381",
month = jul,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003143",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chan:2008:VBG,
author = "K. L. Chan",
title = "Video-Based Gait Analysis by Silhouette {Chamfer}
Distance and {Kalman} Filter",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "3",
pages = "383--418",
month = jul,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003155",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yau:2008:VSR,
author = "Wai Chee Yau and Dinesh Kant Kumar and Sridhar
Poosapadi Arjunan",
title = "Visual Speech Recognition Using Dynamic Features and
Support Vector Machines",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "3",
pages = "419--437",
month = jul,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003167",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sun:2008:BWN,
author = "Shusen Sun and Zhigeng Pan and Tae-Wan Kim",
title = "Blind Watermarking of Non-Uniform {B}-Spline
Surfaces",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "3",
pages = "439--454",
month = jul,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003179",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Snidaro:2008:THM,
author = "Lauro Snidaro and Gian Luca Foresti and Luca
Chittaro",
title = "Tracking Human Motion from Monocular Sequences",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "3",
pages = "455--471",
month = jul,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003180",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lezoray:2008:GBO,
author = "O. Lezoray and C. Meurie and A. Elmoataz",
title = "Graph-Based Ordering Scheme for Color Image
Filtering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "3",
pages = "473--493",
month = jul,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003192",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Coli:2008:FSR,
author = "Pietro Coli and Gian Luca Marcialis and Fabio Roli",
title = "Fingerprint Silicon Replicas: Static and Dynamic
Features for Vitality Detection Using an Optical
Capture Device",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "4",
pages = "495--512",
month = oct,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003209",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hussain:2008:WBE,
author = "Muhammad Hussain and Turghunjan Abdukirim and
Yoshihiro Okada",
title = "Wavelet-Based Edge Detection in Digital Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "4",
pages = "513--533",
month = oct,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003210",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhu:2008:MSS,
author = "Dengming Zhu and Zhaoqi Wang and Yingping Zhang",
title = "Motion Synthesis from the Semantic Signals",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "4",
pages = "535--550",
month = oct,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003222",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kampke:2008:AGR,
author = "Thomas K{\"a}mpke",
title = "Automatic Generation of {$3$D} Radar Display Views",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "4",
pages = "551--572",
month = oct,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003234",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Thakoor:2008:AVO,
author = "Ninad Thakoor and Jean X. Gao",
title = "Automatic Video Object Extraction with Camera in
Motion",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "4",
pages = "573--600",
month = oct,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003246",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Xin:2008:IST,
author = "Binjie Xin and Jinlian Hu and George Baciu",
title = "An Imaging System for Textile Surface Profile Based on
Silhouette Image Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "4",
pages = "601--613",
month = oct,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003258",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Skala:2008:ICP,
author = "Vaclav Skala",
title = "Intersection Computation in Projective Space Using
Homogeneous Coordinates",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "4",
pages = "615--628",
month = oct,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780800326X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ou:2008:LIT,
author = "Chien-Min Ou and Hui-Ya Li and Wen-Jyi Hwang and
Mei-Hwa Liu",
title = "Layered Image Transmission with Quality
Pre-Specifiable {JPEG2000}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "4",
pages = "629--641",
month = oct,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003271",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2008:NAS,
author = "Jing Li and Tao Yang and Quan Pan and Yong-Mei Cheng
and Jun Hou",
title = "A Novel Algorithm for Speeding Up Keypoint Detection
and Matching",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "4",
pages = "643--661",
month = oct,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003283",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2008:AIV,
author = "Anonymous",
title = "Author Index (Volume 8)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "8",
number = "4",
pages = "663--665",
month = oct,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467808003283",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lao:2009:ORA,
author = "Yuanwei Lao and Yuan F. Zheng",
title = "Optimal Rate Allocation for Logo Watermarking",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "1",
pages = "1--25",
month = jan,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003319",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Porta:2009:NVM,
author = "Marco Porta",
title = "New Visualization Modes for Effective Image
Presentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "1",
pages = "27--49",
month = jan,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003320",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chen:2009:IRB,
author = "Huawei Chen and Ichiro Hagiwara and A. Kiet Tieu",
title = "Image Reconstruction Based on Combination of Wavelet
Decomposition, Inpainting and Texture Synthesis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "1",
pages = "51--65",
month = jan,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003332",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Vasuki:2009:EAC,
author = "S. Vasuki and L. Ganesan",
title = "An Efficient Approach to Color Image Segmentation
Using Intermediate Features of Maximum Overlap Wavelet
Transform in Peak Finding Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "1",
pages = "67--76",
month = jan,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003344",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Arivazhagan:2009:TCU,
author = "S. Arivazhagan and L. Ganesan",
title = "Texture Characterization Using {WSFS} and {WCFS}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "1",
pages = "77--100",
month = jan,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003356",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Miyamoto:2009:FAM,
author = "Kentaro Miyamoto and Tetsuo Kamina and Tetsuo Sugiyama
and Keisuke Kameyama and Kazuo Toraichi and Yasuhiro
Ohmiya",
title = "A Function Approximation Method for Images with
Grading Regions",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "1",
pages = "101--119",
month = jan,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003307",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zongqing:2009:NFB,
author = "Lu Zongqing and Liao Qingmin and Pei Jihong",
title = "A Nonlinear Filtering Based Optical Flow Computation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "1",
pages = "121--132",
month = jan,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003368",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ng:2009:RFM,
author = "Ada N. Y. Ng and Edmund Y. Lam and Ronald Chung and
Kenneth S. M. Fung and W. H. Leung",
title = "Reference-Free Machine Vision Inspection of
Semiconductor Die Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "1",
pages = "133--152",
month = jan,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780900337X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2009:JSN,
author = "Xingyuan Wang and Wenjing Song and Lixian Zou",
title = "{Julia} Set of the {Newton} Method for Solving Some
Complex Exponential Equation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "2",
pages = "153--169",
month = apr,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003381",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nikam:2009:COP,
author = "Shankar Bhausaheb Nikam and Suneeta Agarwal",
title = "Co-Occurrence Probabilities and Wavelet-Based Spoof
Fingerprint Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "2",
pages = "171--199",
month = apr,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003393",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhang:2009:GOM,
author = "Shixue Zhang and Enhua Wu",
title = "Generation of Optimal Multiresolution Models for
Deforming Mesh Sequence",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "2",
pages = "201--215",
month = apr,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780900340X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Stylianou:2009:IBF,
author = "Georgios Stylianou and Andreas Lanitis",
title = "Image Based {$3$D} Face Reconstruction: a Survey",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "2",
pages = "217--250",
month = apr,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003411",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2009:UAU,
author = "Ajay Kumar and David Zhang",
title = "User Authentication Using Fusion of Face and
Palmprint",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "2",
pages = "251--270",
month = apr,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003423",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Goh:2009:TSI,
author = "Hock-Ann Goh and Chee-Way Chong and Rosli Besar and
Fazly Salleh Abas and Kok-Swee Sim",
title = "Translation and Scale Invariants of {Hahn} Moments",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "2",
pages = "271--285",
month = apr,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003435",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Aiger:2009:GBA,
author = "Dror Aiger and Klara Kedem",
title = "A {GPU}-Based Algorithm for Approximately Finding the
Largest Common Point Set in the Plane Under Similarity
Transformation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "2",
pages = "287--298",
month = apr,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003459",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Singh:2009:NWM,
author = "Vipula Singh and Navin Rajpal and K. Srikanta
Murthy",
title = "A Neuro-Wavelet Model Using Fuzzy Vector Quantization
for Efficient Image Compression",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "2",
pages = "299--320",
month = apr,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003447",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jindal:2009:STC,
author = "Manish Kumar Jindal and Gurpreet Singh Lehal and
Rajendra Kumar Sharma",
title = "On Segmentation of Touching Characters and Overlapping
Lines in Degraded Printed {Gurmukhi} Script",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "3",
pages = "321--353",
month = jul,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003460",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gao:2009:FRS,
author = "Xinbo Gao and Jinxiu Li and Bing Xiao",
title = "A Face Recognition Scheme Based on Embedded Hidden
{Markov} Model and Selective Ensemble Strategy",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "3",
pages = "355--367",
month = jul,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003472",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Maurer:2009:PVL,
author = "Mauricio Rafael Maurer and Helio Pedrini and Marco
Antonio Ferreira Randi",
title = "Processing and Visualization of Light Microscope
Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "3",
pages = "369--388",
month = jul,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003484",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kamath:2009:ICS,
author = "Chandrika Kamath and Abel Gezahegne and Paul Miller",
title = "Identification of Coherent Structures in
Three-Dimensional Simulations of a Fluid-Mix Problem",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "3",
pages = "389--410",
month = jul,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003502",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Seddik:2009:IWB,
author = "Hassen Seddik and Mounir Sayadi and Farhat Fnaiech and
Mohamed Cheriet",
title = "Image Watermarking Based on the {Hessenberg}
Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "3",
pages = "411--433",
month = jul,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003514",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2009:DEO,
author = "Gaobo Yang and Weiwei Chen and Xiao Jing Wang and
Zhaoyang Zhang",
title = "Dense Estimation of Optical Flow Field Within the
{MPEG-2} Compressed Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "3",
pages = "435--448",
month = jul,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003526",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bhatnagar:2009:RRW,
author = "Gaurav Bhatnagar and Balasubramanian Raman",
title = "Robust Reference-Watermarking Scheme Using Wavelet
Packet Transform and Bidiagonal-Singular Value
Decomposition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "3",
pages = "449--477",
month = jul,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003538",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Huang:2009:FPH,
author = "Bo Huang and Naimin Li",
title = "Fungiform Papillae Hyperplasia ({FPH}) Identification
by Tongue Texture Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "3",
pages = "479--494",
month = jul,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003496",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shi:2009:SBI,
author = "Weiren Shi and Zuojin Li and Xin Shi and Zhi Zhong",
title = "A Survey of Biologically Inspired Image Processing for
Objects Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "4",
pages = "495--510",
month = oct,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946780900354X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wong:2009:PPP,
author = "Alexander Wong",
title = "{PECSI}: a Practical Perceptually-Enhanced Compression
Framework for Still Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "4",
pages = "511--529",
month = oct,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003551",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zheng:2009:MTM,
author = "Liying Zheng and Kuifeng Liu and Lei Yu",
title = "Multilevel Thresholding Method Based on Normalized
Cut",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "4",
pages = "531--540",
month = oct,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003563",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dinesh:2009:NPA,
author = "R. Dinesh and D. S. Guru",
title = "Non-Parametric Adaptive Approach for the Detection of
Dominant Points on Boundary Curves Based on
Non-Symmetric Region of Support",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "4",
pages = "541--557",
month = oct,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003575",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Vyas:2009:GTI,
author = "Vibha S. Vyas and Priti P. Rege",
title = "Geometric Transform Invariant Texture Analysis with
Modified {Chebyshev} Moments Based Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "4",
pages = "559--574",
month = oct,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003587",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Singh:2009:IIC,
author = "Satish Kumar Singh and Shishir Kumar",
title = "Improved Image Compression Based on Feed-Forward
Adaptive Downsampling Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "4",
pages = "575--589",
month = oct,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003605",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chang:2009:CSD,
author = "Jian Chang and Xiaosong Yang and Jian J. Zhang",
title = "Continuous Skeleton-Driven Skinning --- a General
Approach For Modeling Skin Deformation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "4",
pages = "591--608",
month = oct,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003599",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2009:MCM,
author = "J. Wang and N. V. Patel and W. I. Grosky and F.
Fotouhi",
title = "Moving Camera Moving Object Segmentation in Compressed
Video Sequences",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "4",
pages = "609--627",
month = oct,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003617",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2009:AIV,
author = "Anonymous",
title = "Author Index (Volume 9)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "9",
number = "4",
pages = "629--631",
month = oct,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467809003617",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hossain:2010:VIC,
author = "Md. Shafaeat Hossain and Khandaker Abir Rahman and Md.
Hasanuzzaman and M. A. Bhuyian and H. Ueno",
title = "Video Image Clustering Based on Human Face and Shirt
Color",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "1",
pages = "1--19",
month = jan,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003639",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Heidary:2010:SSD,
author = "Kaveh Heidary and H. John Caulfield",
title = "Spectral Sensitivity Design for Optical Sensors",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "1",
pages = "21--39",
month = jan,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003640",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2010:CSF,
author = "Xiaoping Wang and Shenglan Liu and Liyan Zhang",
title = "Constructing Surface Features Through Deformation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "1",
pages = "41--56",
month = jan,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003652",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jlassi:2010:DBV,
author = "Hejer Jlassi and Kamel Hamrouni",
title = "Detection of Blood Vessels in Retinal Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "1",
pages = "57--72",
month = jan,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003664",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2010:EPP,
author = "Yong-Jin Liu",
title = "On the Evaluation of Progressive Point-Sampled
Geometry",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "1",
pages = "73--91",
month = jan,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003676",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nigam:2010:FPB,
author = "Chhabi Nigam and R. Venkatesh Babu and S. Kumar Raja
and K. R. Ramakrishnan",
title = "Fragmented Particles-Based Robust Object Tracking with
Feature Fusion",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "1",
pages = "93--112",
month = jan,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003688",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{El-Sattar:2010:NPC,
author = "Hussein Karam Hussein Abd El-Sattar",
title = "A New Plot\slash Character-Based Interactive System
for Story-Based Virtual Reality Applications",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "1",
pages = "113--133",
month = jan,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781000369X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bourouis:2010:SMB,
author = "Sami Bourouis and Kamel Hamrouni",
title = "{$3$D} Segmentation of {MRI} Brain Using Level Set and
Unsupervised Classification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "1",
pages = "135--154",
month = jan,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003706",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ali:2010:VFS,
author = "Wajid Ali and Tangui Morvan and Petter Risholm and Ole
Jakob Elle and Eigil Samset",
title = "A Visualization and Fusion System for Image Guided
{RFA} Procedures",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "2",
pages = "155--174",
month = apr,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003718",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nesme:2010:AIA,
author = "Matthieu Nesme and Fran{\c{c}}ois Faure and Yohan
Payan",
title = "Accurate Interactive Animation of Deformable Models At
Arbitrary Resolution",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "2",
pages = "175--202",
month = apr,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781000372X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{DeVisser:2010:DNG,
author = "Hans {De Visser} and Josh Passenger and David Conlan
and Christoph Russ and David Hellier and Mario Cheng
and Oscar Acosta and S{\'e}bastien Ourselin and Olivier
Salvado",
title = "Developing a Next Generation Colonoscopy Simulator",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "2",
pages = "203--217",
month = apr,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003731",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sherstyuk:2010:SAS,
author = "Andrei Sherstyuk and Anton Treskunov and Benjamin
Berg",
title = "Semi-Automatic Surface Scanner for Medical Tangible
User Interfaces",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "2",
pages = "219--233",
month = apr,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003743",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nguyen:2010:TBR,
author = "Van-Hanh Nguyen and Frederic Merienne and Jean-Luc
Martinez",
title = "Training Based on Real-Time Motion Evaluation for
Functional Rehabilitation in Virtual Environment",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "2",
pages = "235--250",
month = apr,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003755",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kaur:2010:HED,
author = "Amandeep Kaur and Chandan Singh",
title = "A Hybrid Edge Detector Using Fuzzy Logic and
Mathematical Morphology",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "2",
pages = "251--272",
month = apr,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003767",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2010:DTC,
author = "Zhaohui Yang and Naimin Li",
title = "Detection of Tongue Crack Based on Distant Gradient
and Prior Knowledge",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "2",
pages = "273--288",
month = apr,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003779",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wu:2010:SBM,
author = "Jie Wu and Jiabi Chen and Xuelong Zhang and Jinghai
Chen",
title = "The Segmentation of Brain {MR} Images Using
Reformative Expectation--Maximization Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "2",
pages = "289--297",
month = apr,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003780",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Aouat:2010:MDN,
author = "Saliha Aouat and Slimane Larabi",
title = "Matching Descriptors of Noisy Outline Shapes",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "3",
pages = "299--325",
month = jul,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003792",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Karthigaikumar:2010:PPV,
author = "P. Karthigaikumar and K. Baskaran",
title = "Partially Pipelined {VLSI} Implementation of
{Blowfish} Encryption\slash Decryption Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "3",
pages = "327--341",
month = jul,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003809",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Soderstrom:2010:RBH,
author = "Ulrik S{\"o}derstr{\"o}m and Haibo Li",
title = "Representation Bound for Human Facial Mimic with the
Aid of Principal Component Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "3",
pages = "343--363",
month = jul,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003810",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gowda:2010:ANI,
author = "Rahul Gowda and Shalin M. Mehta and Yue Yang and
Baoxin Li",
title = "Adaptive Nonlinear Image Enhancement of {Gaussian}
Degraded Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "3",
pages = "365--393",
month = jul,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003822",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Matungka:2010:EII,
author = "Rittavee Matungka and Yuan F. Zheng and Robert L.
Ewing",
title = "Efficient Invariant Image Registration Utilizing
Pre-Shifted Logarithmic Spiral",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "3",
pages = "395--421",
month = jul,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003834",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dinesh:2010:CTS,
author = "R. Dinesh and D. S. Guru",
title = "Concept of Triangular Spatial Relationship and
{B}-Tree for Partially Occluded Object Recognition: an
Efficient and Robust Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "3",
pages = "423--448",
month = jul,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003846",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bai:2010:HPT,
author = "Xiaoliang Bai and Shusheng Zhang",
title = "Hierarchical Parameterization of Triangular Mesh with
a Boundary Polygon Triangulation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "3",
pages = "449--466",
month = jul,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003858",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Miyazaki:2010:CDM,
author = "Ryuji Miyazaki and Koichi Harada",
title = "Creating the Displacement Mapped Low-Level Mesh and
Its Application for {CG} Software",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "3",
pages = "467--480",
month = jul,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781000386X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 31 08:38:02 MDT 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Treuillet:2010:OIV,
author = "Sylvie Treuillet and Eric Royer",
title = "Outdoor\slash Indoor Vision-Based Localization for
Blind Pedestrian Navigation Assistance",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "4",
pages = "481--496",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003937",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Dec 9 21:06:32 MST 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jovanova:2010:OAS,
author = "Blagica Jovanova and Ivica Arsov and Marius Preda and
Fran{\c{c}}oise Preteux",
title = "Online Animation System for Practicing Cued Speech",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "4",
pages = "497--512",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003925",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Dec 9 21:06:32 MST 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Durette:2010:SRT,
author = "Barth{\'e}l{\'e}my Durette and Jeanny H{\'e}rault and
David Alleysson",
title = "Simulation of the Retina: a Tool for Visual
Prostheses",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "4",
pages = "513--529",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003949",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Dec 9 21:06:32 MST 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dramas:2010:AVB,
author = "Florian Dramas and Simon J. Thorpe and Christophe
Jouffrais",
title = "Artificial Vision for the Blind: a Bio-Inspired
Algorithm for Objects and Obstacles Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "4",
pages = "531--544",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003871",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Dec 9 21:06:32 MST 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pissaloux:2010:IMO,
author = "Edwige Pissaloux and Yong Chen and Ramiro Velazquez",
title = "Image Matching Optimization Via Vision and Inertial
Data Fusion: Application to Navigation of the Visually
Impaired",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "4",
pages = "545--558",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003913",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Dec 9 21:06:32 MST 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kulkarni:2010:ITU,
author = "Shivali D. Kulkarni and Ameya K. Naik and Nitin S.
Nagori",
title = "{$2$D} Image Transmission Using Bandwidth Efficient
Mapping Technique",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "4",
pages = "559--573",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003883",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Dec 9 21:06:32 MST 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nassar:2010:NFE,
author = "Hamed Nassar and Ghada El-Taweel and Eman Mahmoud",
title = "A Novel Feature Extraction Scheme for Human Gait
Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "4",
pages = "575--587",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003895",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Dec 9 21:06:32 MST 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dharwadkar:2010:SSG,
author = "Nagaraj V. Dharwadkar and B. B. Amberker",
title = "Steganographic Scheme for Gray-Level Image Using Pixel
Neighborhood and {LSB} Substitution",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "4",
pages = "589--607",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003901",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Dec 9 21:06:32 MST 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2010:AIV,
author = "Anonymous",
title = "Author Index (Volume 10)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "10",
number = "4",
pages = "609--611",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467810003901",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Dec 9 21:06:32 MST 2010",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nguyen:2011:OTV,
author = "Thanh Binh Nguyen and Ashish Khare",
title = "Object Tracking of Video Sequences in Curvelet
Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "1",
pages = "1--20",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811003968",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Mar 8 10:11:09 MST 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Suraj:2011:RPC,
author = "M. G. Suraj and D. S. Guru and S. Manjunath",
title = "Recognition of Postal Codes from Fingerspelling Video
Sequence",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "1",
pages = "21--41",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781100397X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Mar 8 10:11:09 MST 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Abbadeni:2011:TPI,
author = "Noureddine Abbadeni and Haikel S. Alhichri and Alaa B.
Elmasry",
title = "Tackling the Problem of Invariant Texture Retrieval
Using Multiple Strategies",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "1",
pages = "43--64",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811003981",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Mar 8 10:11:09 MST 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2011:MHC,
author = "Wenjia Yang and Lihua Dou and Juan Zhan",
title = "A Multi-Histogram Clustering Approach Toward {Markov}
Random Field for Foreground Segmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "1",
pages = "65--81",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811003993",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Mar 8 10:11:09 MST 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Periasamy:2011:ATB,
author = "P. S. Periasamy and S. Athinarayanan and K.
Duraiswamy",
title = "An Adaptive Thresholding-Based Color Reduction
Algorithm and Its Applications",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "1",
pages = "83--101",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004007",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Mar 8 10:11:09 MST 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kallel:2011:SMM,
author = "Mohamed Kallel and Mohamed-Salim Bouhlel and
Jean-Christophe Lapayre",
title = "Security of the Medical Media Using a Hybrid and
Multiple Watermark Technique",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "1",
pages = "103--115",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004019",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Mar 8 10:11:09 MST 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Boulila:2011:MAS,
author = "Wadii Boulila and Imed Riadh Farah",
title = "Multi-Approach Satellite Images Fusion Based on Blind
Sources Separation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "1",
pages = "117--136",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004020",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Mar 8 10:11:09 MST 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Su:2011:ISR,
author = "Ya Su and Xinbo Gao",
title = "Iterative Shape Refinement in {AAM}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "1",
pages = "137--151",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004032",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Mar 8 10:11:09 MST 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2011:IQF,
author = "Haoting Liu and Jie Li and Zheng Wang and Jian Cheng
and Hanqing Lu and Yan Zhao",
title = "Image Quality Feedback-Based Adaptive Video Definition
Improvement for the Space Manipulation Task",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "2",
pages = "153--175",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004044",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jul 8 14:32:32 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Florinabel:2011:MBM,
author = "D. Jemi Florinabel and S. Ebenezer Juliet and V.
Sadasivam",
title = "Multiorientation-Based Multistructure Morphological
Inpainting",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "2",
pages = "177--193",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004056",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jul 8 14:32:32 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2011:QBC,
author = "Yuqing Wang and Ming Zhu and Haochen Pang and Yong
Wang",
title = "Quaternion Based Color Image Quality Assessment
Index",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "2",
pages = "195--206",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004111",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jul 8 14:32:32 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nasrollahi:2011:SSV,
author = "Kamal Nasrollahi and Thomas B. Moeslund and Mohammad
Rahmati",
title = "Summarization of Surveillance Video Sequences Using
Face Quality Assessment",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "2",
pages = "207--233",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004068",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jul 8 14:32:32 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2011:STV,
author = "Fuzheng Yang and Shuai Wan",
title = "Spatial-Temporal Video Quality Assessment Based on
Two-Level Temporal Pooling",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "2",
pages = "235--249",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781100407X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jul 8 14:32:32 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2011:IMV,
author = "J. X. Yang and D. M. Tan and H. R. Wu",
title = "An Impairment Metric for Video Temporal Fluctuation
Measure",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "2",
pages = "251--264",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004081",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jul 8 14:32:32 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Deng:2011:IQM,
author = "Cheng Deng and Jie Li and Yifan Zhang and Dongyu Huang
and Lingling An",
title = "An Image Quality Metric Based on Biologically Inspired
Feature Model",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "2",
pages = "265--279",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004093",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jul 8 14:32:32 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lu:2011:NCI,
author = "Wen Lu and Lihuo He and Wenjian Tang and Fei Gao and
Weilong Hou",
title = "A Novel Compressed Images Quality Metric",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "2",
pages = "281--292",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781100410X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jul 8 14:32:32 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Urolagin:2011:KAR,
author = "Siddhaling Urolagin and K. V. Prema and N. V. Subba
Reddy",
title = "{Kannada} Alphabets Recognition with Application to
{Braille} Translation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "3",
pages = "293--314",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004159",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 24 06:48:16 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{He:2011:SBN,
author = "Liqiang He and Guangyong Zhang and Yanyan Zhang",
title = "Speeding Up Best Neighborhood Matching Algorithm for
High-Definition Image on {GPU} {Platform}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "3",
pages = "315--337",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004196",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 24 06:48:16 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2011:NTL,
author = "Xing-Yuan Wang and Zhi-Feng Chen and Jiao-Jiao Yun",
title = "A Novel Two-Level Color Image Retrieval Method",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "3",
pages = "339--353",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004184",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 24 06:48:16 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bonyadi:2011:NHQ,
author = "Mohammad Reza Bonyadi and Mohsen Ebrahimi Moghaddam",
title = "A Nonuniform High-Quality Image Compression Method to
Preserve User-Specified Compression Ratio",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "3",
pages = "355--375",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004123",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 24 06:48:16 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kamath:2011:RES,
author = "Chandrika Kamath and Omar A. Hurricane",
title = "Robust Extraction of Statistics from Images of
Material Fragmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "3",
pages = "377--401",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004172",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 24 06:48:16 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Su:2011:BIR,
author = "Liyun Su and Ruihua Liu",
title = "Blind Image Restoration with Modified {CMA}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "3",
pages = "403--413",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004147",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 24 06:48:16 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chia:2011:BUI,
author = "Wai Chong Chia and Li Wern Chew and Li-Minn Ang and
Kah Phooi Seng",
title = "Binary-Uncoded Image and Video Compression Using
{SPIHT--ZTR} Coding",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "3",
pages = "415--437",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004135",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 24 06:48:16 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Alvarez:2011:VLD,
author = "Miguel Alvarez and Mar{\'\i}a-Elena Algorri",
title = "Vectorization and Line Detection for Automatic Image
Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "3",
pages = "439--470",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004160",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 24 06:48:16 MDT 2011",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rawat:2011:CBR,
author = "Sanjay Rawat and Balasubramanian Raman",
title = "A Chaos-Based Robust Watermarking Algorithm for
Rightful Ownership Protection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "4",
pages = "471--493",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004263",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 8 18:48:57 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lu:2011:NTB,
author = "Huchuan Lu and Dong Wang and Yen-Wei Chen and Hao
Chen",
title = "A Novel Texture-Based Multi-Linear Analysis Algorithm
for Face Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "4",
pages = "495--508",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781100424X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 8 18:48:57 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Massoptier:2011:CGC,
author = "Laurent Massoptier and Avishkar Misra and Arcot Sowmya
and Sergio Casciaro",
title = "Combining Graph-Cut Technique and Anatomical Knowledge
for Automatic Segmentation of Lungs Affected by Diffuse
Parenchymal Disease in {HRCT} Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "4",
pages = "509--529",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004202",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 8 18:48:57 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kudelski:2011:FLE,
author = "Dimitri Kudelski and Sophie Viseur and Giovanni
Scrofani and Jean-Luc Mari",
title = "Feature Line Extraction on Meshes Through Vertex
Marking and {$2$D} Topological Operators",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "4",
pages = "531--548",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004226",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 8 18:48:57 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2011:ISC,
author = "Wei Wang and Chi-Kit Ronald Chung",
title = "Image Segmentation with Complementary Use of Edge and
Region Information",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "4",
pages = "549--570",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004275",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 8 18:48:57 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Schwartz:2011:IFI,
author = "William Robson Schwartz and Helio Pedrini",
title = "Improved Fractal Image Compression Based on Robust
Feature Descriptors",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "4",
pages = "571--587",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004251",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 8 18:48:57 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Drew:2011:ICR,
author = "Mark S. Drew and Graham D. Finlayson",
title = "Improvement of Colorization Realism Via the Structure
Tensor",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "4",
pages = "589--609",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004214",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 8 18:48:57 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Balster:2011:PCR,
author = "Eric J. Balster and Benjamin T. Fortener and William
F. Turri",
title = "Post-Compression Rate-Distortion Development for
Embedded Block Coding with Optimal Truncation in
{JPEG2000} Imagery",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "4",
pages = "611--627",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004238",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 8 18:48:57 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2011:AIV,
author = "Anonymous",
title = "Author Index (Volume 11)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "11",
number = "4",
pages = "629--631",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467811004238",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 8 18:48:57 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Helmy:2012:CMC,
author = "Tarek Helmy",
title = "A Computational Model for Context-Based Image
Categorization and Description",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "1",
pages = "1250001",
month = jan,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500015",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 29 07:59:06 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "19",
}
@Article{Fu:2012:DRA,
author = "Bin Fu and Wenxin Li and Minghui Wu and Rongfeng Li
and Zhuoqun Xu",
title = "A Document Rectification Approach Dealing with Both
Perspective Distortion and Warping Based on Text Flow
Curve Fitting",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "1",
pages = "1250002",
month = jan,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500027",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 29 07:59:06 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "25",
}
@Article{Prasath:2012:ADS,
author = "V. B. Surya Prasath and Arindama Singh",
title = "An Adaptive Diffusion Scheme for Image Restoration and
Selective Smoothing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "1",
pages = "1250003",
month = jan,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500039",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 29 07:59:06 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "18",
}
@Article{Mukherjee:2012:CIU,
author = "Dipti Prasad Mukherjee and Nilanjan Ray",
title = "Contour Interpolation Using Level-Set Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "1",
pages = "1250004",
month = jan,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500040",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 29 07:59:06 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "16",
}
@Article{Rani:2012:FRU,
author = "J. Sheeba Rani",
title = "Face Recognition Using Hybrid Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "1",
pages = "1250005",
month = jan,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500052",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 29 07:59:06 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "27",
}
@Article{Lai:2012:FMI,
author = "Shuhua Lai and Fuhua (Frank) Cheng",
title = "Fast Mesh Interpolation and Mesh Decomposition with
Applications",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "1",
pages = "1250006",
month = jan,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500064",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 29 07:59:06 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "18",
}
@Article{Li:2012:HIR,
author = "Ping Li and Hanqiu Sun and Jianbing Shen and Chen
Huang",
title = "{HDR} Image Rerendering Using {GPU}-Based Processing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "1",
pages = "1250007",
month = jan,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500076",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 29 07:59:06 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "19",
}
@Article{Suresh:2012:STP,
author = "R. M. Suresh and N. Jayalakshmi",
title = "Segmentation and Tracking of Progenitor Cells in Time
Lapse Microscopy",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "1",
pages = "1250008",
month = jan,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500088",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 29 07:59:06 MST 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "15",
}
@Article{Nejla:2012:BIR,
author = "Gribaa Nejla and Noblet Vincent and Khlifa Nawres and
Faisan Sylvain and Hamrouni Kamel",
title = "Binary Image Registration Based on Geometric Moments:
Application to the Registration of {$3$D} Segmented
{CT} Head Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "2",
pages = "1250009",
month = apr,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781250009X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 3 08:15:54 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "16",
}
@Article{Kodavalla:2012:DVC,
author = "Vijay Kumar Kodavalla and P. G. Krishna Mohan",
title = "Distributed Video Coding: Feedback-Free Architecture
and Implementation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "2",
pages = "1250010",
month = apr,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500106",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 3 08:15:54 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "16",
}
@Article{Xu:2012:FRB,
author = "Gang Xu and Huchuan Lu and Zunyi Wang",
title = "Face Recognition Based on {GPPBTF} and {LBP} with
Classifier Fusion",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "2",
pages = "1250011",
month = apr,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500118",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 3 08:15:54 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "16",
}
@Article{Fan:2012:GIC,
author = "N. Fan and Cheng Jin",
title = "Geometric Invariants Construction for Semantic Scene
Understanding from Multiple Views Inspired by the Human
Visual System",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "2",
pages = "1250012",
month = apr,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781250012X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 3 08:15:54 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "14",
}
@Article{Yang:2012:IRB,
author = "Shiueng-Bien Yang and Ting-Wen Liang",
title = "Image Restoration Based on Smooth Gray-Level Detection
and Line Prediction Method for Large Missing Regions",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "2",
pages = "1250013",
month = apr,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500131",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 3 08:15:54 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "20",
}
@Article{Feddaoui:2012:IRM,
author = "Nadia Feddaoui and Hela Mahersia and Kamel Hamrouni",
title = "Iris Recognition Method Based on {Gabor} Filters and
Uniform Local Binary Patterns",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "2",
pages = "1250014",
month = apr,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500143",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 3 08:15:54 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "20",
}
@Article{Wang:2012:MIS,
author = "Haijun Wang and Ming Liu",
title = "Medical Images Segmentation Using Active Contours
Driven by Global and Local Image Fitting Energy",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "2",
pages = "1250015",
month = apr,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500155",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 3 08:15:54 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "15",
}
@Article{Santosh:2012:RPS,
author = "K. C. Santosh and Cholwich Nattee and Bart Lamiroy",
title = "Relative Positioning of Stroke-Based Clustering: a New
Approach to Online Handwritten {Devanagari} Character
Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "2",
pages = "1250016",
month = apr,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500167",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 3 08:15:54 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
pagecount = "25",
}
@Article{Zhi:2012:IIA,
author = "Zhanjiang Zhi and Yi Sun",
title = "An Image Inpainting Algorithm Based on Energy
Minimization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "3",
pages = "1250017",
month = jul,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500179",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Nov 3 13:35:52 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2012:HBS,
author = "Shifeng Li and Meng Yao and Huchuan Lu",
title = "Human Body Segmentation in a Static Image with On-Line
{AdaBoost} at Multiscale Superpixels",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "3",
pages = "1250018",
month = jul,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500180",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Nov 3 13:35:52 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhang:2012:ICB,
author = "Lihe Zhang and Zhenzhen Liu",
title = "Image Cosegmentation Based on Local and Global Level
Set Methods",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "3",
pages = "1250019",
month = jul,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500192",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Nov 3 13:35:52 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jiang:2012:RTO,
author = "Ming-Xin Jiang and Zhi-Jing Shao and Hong-Yu Wang",
title = "Real-Time Object Tracking Algorithm with Cameras
Mounted on Moving Platforms",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "3",
pages = "1250020",
month = jul,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500209",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Nov 3 13:35:52 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2012:TTC,
author = "Dong Wang and Gang Yang and Huchuan Lu",
title = "Tri-Tracking: Combining Three Independent Views for
Robust Visual Tracking",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "3",
pages = "1250021",
month = jul,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500210",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Nov 3 13:35:52 MDT 2012",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mahjoub:2012:MOR,
author = "Mohamed Ali Mahjoub and Malek Abbassi",
title = "{$3$D} Mesh Object Retrieval by Discrete and
Continuous Hidden {Markov} Models",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "4",
pages = "1250022",
month = oct,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500222",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:00 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2012:IED,
author = "Xingyuan Wang and Zhifeng Chen and Xuemei Bao",
title = "An Improved Edge-Directed Image Interpolation
Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "4",
pages = "1250023",
month = oct,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500234",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:00 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pal:2012:CAS,
author = "Shyamosree Pal and Rahul Dutta and Partha Bhowmick",
title = "Circular Arc Segmentation by Curvature Estimation and
Geometric Validation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "4",
pages = "1250024",
month = oct,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500246",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:00 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lee:2012:EGC,
author = "Jong Kwan Lee and Timothy S. Newman",
title = "Exploring {GPU}- and Cluster-Based Improvements for
Over-Sampled Volume Ray Casting Opacity Correction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "4",
pages = "1250025",
month = oct,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500258",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:00 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Takimoto:2012:ICP,
author = "Hironori Takimoto and Seiki Yoshimori and Yasue
Mitsukura",
title = "Invisible Calibration Pattern for Print-And-Scan Data
Hiding Based on Human Visual Perception",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "4",
pages = "1250026",
month = oct,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781250026X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:00 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2012:LTI,
author = "Shiueng-Bien Yang and Chi-Feng Wu",
title = "Locating Text in Images Based on the Smooth Gray-Level
Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "4",
pages = "1250027",
month = oct,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500271",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:00 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dube:2012:PQT,
author = "Mridula Dube and Reenu Sharma",
title = "Piecewise Quartic Trigonometric Polynomial {B}-Spline
Curves with Two Shape Parameters",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "4",
pages = "1250028",
month = oct,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500283",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:00 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Slamu:2012:SFE,
author = "Wushour Slamu and Juming Cao and Xinhui Yao",
title = "Sharp Features Extraction from Point Clouds",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "4",
pages = "1250029",
month = oct,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467812500295",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:00 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2012:AIV,
author = "Anonymous",
title = "{Author Index} (Volume 12)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "12",
number = "4",
pages = "1299001",
month = oct,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781299001X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:00 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mohanta:2013:NTS,
author = "Partha Pratim Mohanta and Sanjoy Kumar Saha and
Bhabatosh Chanda",
title = "A Novel Technique for Size Constrained Video
Storyboard Generation Using Statistical Run Test and
Spanning Tree",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "1",
pages = "1350001:1--1350001:24",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500010",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:18 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Melkemi:2013:PAD,
author = "Mahmoud Melkemi and Frederic Cordier and Nickolas S.
Sapidis",
title = "A Provable Algorithm to Detect Weak Symmetry in a
Polygon",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "1",
pages = "1350002:1--1350002:28",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500022",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:18 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anagnostopoulos:2013:EFS,
author = "Vasileios I. Anagnostopoulos and Emmanuel S. Sardis
and Theodora A. Varvarigou",
title = "Estimation of Frame Sequence Noise with Removal of
{JPEG} Artifacts",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "1",
pages = "1350003:1--1350003:31",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500034",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:18 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jha:2013:LEU,
author = "Rajib Kumar Jha and Prabir Kumar Biswas and B. N.
Chatterji",
title = "Logo Extraction Using Combined Discrete Wavelet
Transform and Dynamic Stochastic Resonance",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "1",
pages = "1350004:1--1350004:21",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500046",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:18 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Senapati:2013:LBR,
author = "Ranjan K. Senapati and Umesh C. Pati and Kamala K.
Mahapatra",
title = "Low Bit Rate Image Compression Using Hierarchical
Listless Block-Tree {DTT} Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "1",
pages = "1350005:1--1350005:23",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500058",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:18 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gupta:2013:QMC,
author = "Rajani Gupta and Prashant Bansod and R. S. Gamad",
title = "Quality Measure of the Compressed Echo, {X}-Ray and
{CT} Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "1",
pages = "1350006:1--1350006:29",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781350006X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:18 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mohideen:2013:RBC,
author = "Abubacker Kaja Mohideen and Kuttiannan Thangavel",
title = "Region-Based Contrast Enhancement of Digital
Mammograms Using an Improved Watershed Segmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "1",
pages = "1350007:1--1350007:25",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500071",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 6 16:27:18 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Agrawal:2013:P,
author = "Anupam Agrawal and R. C. Tripathi and Ellen Yi-Luen Do
and M. D. Tiwar",
title = "Preface",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "2",
pages = "??--??",
month = apr,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813020014",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 6 10:37:51 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rautaray:2013:HGR,
author = "Siddharth Swarup Rautaray and Anupam Agrawal",
title = "Hand Gesture Recognition Towards Vocabulary and
Application Independency",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "2",
pages = "??--??",
month = apr,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813400019",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 6 10:37:51 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Singh:2013:QBF,
author = "Durgesh Singh and Shivendra Shivani and Suneeta
Agarwal",
title = "Quantization-Based Fragile Watermarking Using
Block-Wise Authentication and Pixel-Wise Recovery
Scheme for Tampered Image",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "2",
pages = "??--??",
month = apr,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813400020",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 6 10:37:51 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2013:GAI,
author = "Piyush Kumar and Anupam Agrawal",
title = "{GPU}-Accelerated Interactive Visualization of {$ 3 D
$} Volumetric Data Using {CUDA}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "2",
pages = "??--??",
month = apr,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813400032",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 6 10:37:51 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bhale:2013:ESF,
author = "Aparna Narendra Bhale and Manish Ratnakar Joshi",
title = "Enhancement of Screen Film Mammogram Up to a Level of
Digital Mammogram: Experimental Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "2",
pages = "??--??",
month = apr,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813400044",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 6 10:37:51 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{De:2013:SDO,
author = "Kanjar De and V. Masilamani",
title = "A Spatial Domain Object Separability Based
No-Reference Image Quality Measure Using Mean and
Variance",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "2",
pages = "??--??",
month = apr,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813400056",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 6 10:37:51 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Verma:2013:ISP,
author = "Nishchal K. Verma and Shikha Singh",
title = "Image Sequence Prediction Using {ANN} and {RBFNN}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "2",
pages = "??--??",
month = apr,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813400068",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 6 10:37:51 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
keywords = "artificial neural network (ANN); Canny edge
detection-based image comparison metric (CIM); mean
structure similarity index measure (MSSIM)",
}
@Article{Singh:2013:HAI,
author = "Pankaj Pratap Singh and R. D. Garg",
title = "A Hybrid Approach for Information Extraction from High
Resolution Satellite Imagery",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "2",
pages = "??--??",
month = apr,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781340007X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 6 10:37:51 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nigam:2013:FCI,
author = "Akriti Nigam and Ajay Indoria and R. C. Tripathi",
title = "Fuzzy Clustering of Image Trademark Database and
Preprocessing Using Adaptive Filter and
{Karhunen--Lo{\`e}ve} Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "2",
pages = "??--??",
month = apr,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813400081",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 6 10:37:51 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kuijper:2013:CBM,
author = "Arjan Kuijper and Ilkka Havukkala",
title = "Comparing Bitmapped {MicroRNA} Structure Images Using
Mutual Symmetry",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "3",
pages = "??--??",
month = jul,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500083",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 28 14:00:38 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Carvalho:2013:IVF,
author = "Paulo Roberto {De Carvalho, Jr.} and Maikon Cismoski
{Dos Santos} and William Robson Schwartz and Helio
Pedrini",
title = "An Improved View Frustum Culling Method Using Octrees
for {$3$D} Real-Time Rendering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "3",
pages = "??--??",
month = jul,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500095",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 28 14:00:38 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jiji:2013:FMP,
author = "C. V. Jiji and Ravi Krishnan Unni",
title = "Fusion of Multispectral and Panchromatic Images Based
on the Nonsubsampled Contourlet Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "3",
pages = "??--??",
month = jul,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500101",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 28 14:00:38 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Huang:2013:ADC,
author = "Wei Huang and Hongtao Lu",
title = "Automatic Defect Classification of {TFT-LCD} Panels
with Shape, Histogram and Color Features",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "3",
pages = "??--??",
month = jul,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500113",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 28 14:00:38 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{He:2013:RAM,
author = "Liwen He and Yong Xu and Yan Chen and Jiajun Wen",
title = "Recent Advance on Mean Shift Tracking: a Survey",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "3",
pages = "??--??",
month = jul,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500125",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 28 14:00:38 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Maitra:2013:MDE,
author = "Indra Kanta Maitra and Sanjay Nag and Samir K.
Bandyopadhyay",
title = "Mammographic Density Estimation and Classification
Using Segmentation and Progressive Elimination Method",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "3",
pages = "??--??",
month = jul,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500137",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 28 14:00:38 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wei:2013:SMO,
author = "Jie Wei",
title = "Small Moving Object Detection from Infra-Red
Sequences",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "3",
pages = "??--??",
month = jul,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500149",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 28 14:00:38 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dorini:2013:SDF,
author = "Leyza Baldo Dorini and Neucimar Jer{\^o}nimo Leite",
title = "A Self-Dual Filtering Toggle Operator for Speckle
Noise Filtering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "3",
pages = "??--??",
month = jul,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500150",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Aug 28 14:00:38 MDT 2013",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mollah:2013:HDB,
author = "Ayatullah Faruk Mollah and Subhadip Basu and Mita
Nasipuri",
title = "Handheld Device-Based Character Recognition System for
Camera Captured Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "4",
pages = "1350016",
month = oct,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500162",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:32 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ray:2013:PAD,
author = "Kumar S. Ray and Bimal Kumar Ray",
title = "Polygonal Approximation of Digital Curve Based on
Reverse Engineering Concept",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "4",
pages = "1350017",
month = oct,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500174",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:32 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2013:ROC,
author = "Hao Liu and Guanhua Zhu and Jianning Zhao and Hongbo
Qian and Ning Dai",
title = "Recognition of Occlusions in {CT} Images Using a
Curve-Based Parameterization Method",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "4",
pages = "1350018",
month = oct,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500186",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:32 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2013:VBA,
author = "Shuenn-Jyi Wang and Chung-Kai Hsieh and Tsorng-Lin
Chia",
title = "Video-Based Approach for Detecting Prohibited
Activities on Sporting Courts",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "4",
pages = "1350019",
month = oct,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500198",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:32 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Haddad:2013:SAC,
author = "Bashar Haddad and Amin Jarrah",
title = "Semi-Automatic Cracks Correction Based on Seam
Processing, Stochastic Analysis and Learning Process",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "4",
pages = "1350020",
month = oct,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500204",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:32 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2013:SHB,
author = "Jia Yang and Chee Kooi Chan and Ameersing Luximon",
title = "A Survey on {$3$D} Human Body Modeling for Interactive
Fashion Design",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "4",
pages = "1350021",
month = oct,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500216",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:32 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dorini:2013:SST,
author = "Leyza Baldo Dorini and Neucimar Jer{\^o}nimo Leite",
title = "A Scale-Space Toggle Operator for Image
Transformations",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "4",
pages = "1350022",
month = oct,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813500228",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:32 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2013:AIV,
author = "Anonymous",
title = "Author Index (Volume 13)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "13",
number = "4",
pages = "1399001",
month = oct,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467813990015",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:32 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yuan:2014:MGD,
author = "Yongfeng Yuan and Kuanquan Wang",
title = "A Mixed {Gauss} and Directional Distance Filter for
Fiber Direction Tracking",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "1--2",
pages = "1450001",
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500016",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:40 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Prakash:2014:MID,
author = "Om Prakash and Ashish Khare",
title = "Medical Image Denoising Based on Soft Thresholding
Using Biorthogonal Multiscale Wavelet Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "1--2",
pages = "1450002",
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500028",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:40 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Angadi:2014:RST,
author = "S. A. Angadi and M. M. Kodabagi",
title = "A Robust Segmentation Technique for Line, Word and
Character Extraction from {Kannada} Text in Low
Resolution Display Board Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "1--2",
pages = "1450003",
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781450003X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:40 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Batagelo:2014:GBS,
author = "Harlen Costa Batagelo and Jo{\~a}o Paulo Gois",
title = "{GPU}-Based Sphere Tracing for Radial Basis Function
Implicits",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "1--2",
pages = "1450004",
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500041",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:40 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Carvalho:2014:HCS,
author = "L. E. Carvalho and S. L. Mantelli Neto and A. von
Wangenheim and A. C. Sobieranski and L. Coser and E.
Comunello",
title = "Hybrid Color Segmentation Method Using a Customized
Nonlinear Similarity Function",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "1--2",
pages = "1450005",
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500053",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:40 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jasim:2014:RTC,
author = "Mahmood Jasim and Tao Zhang and Md. Hasanuzzaman",
title = "A Real-Time Computer Vision-Based Static and Dynamic
Hand Gesture Recognition System",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "1--2",
pages = "1450006",
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500065",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:40 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Takimoto:2014:FOE,
author = "Hironori Takimoto and Hitoshi Yamauchi and Mitsuyoshi
Kishihara and Kensuke Okubo",
title = "Foreground Object Extraction Based on Interactive
Color Saliency Map",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "1--2",
pages = "1450007",
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500077",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 6 06:13:40 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tan:2014:SVI,
author = "Cheen-Hau Tan and Lap-Pui Chau",
title = "Single Viewpoint Image-Driven Simplification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "3",
pages = "1450008",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500089?",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 26 06:23:26 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ray:2014:PRB,
author = "Kumar S. Ray",
title = "Pattern Recognition Based on Fuzzy Set and Genetic
Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "3",
pages = "1450009",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500090?",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 26 06:23:26 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kulkarni:2014:GBM,
author = "S. B. Kulkarni and Raghavendrarao B. Kulkarni and U.
P. Kulkarni and Ravindra S. Hegadi",
title = "{GLCM}-Based Multiclass Iris Recognition Using {FKNN}
and {KNN}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "3",
pages = "1450010",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500107?",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 26 06:23:26 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ali:2014:LDF,
author = "Haider Ali and Umair Ullah Tariq and Muhammad Abid",
title = "Learning Discriminating Features for Gender
Recognition of Real World Faces",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "3",
pages = "1450011",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500119?",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 26 06:23:26 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2014:AIA,
author = "Yongmei Liu and Tanakrit Wongwitit and Linsen Yu",
title = "Automatic Image Annotation Based on Scene Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "3",
pages = "1450012",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500120?",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 26 06:23:26 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Roy:2014:MSU,
author = "Kaushik Roy and Brian O'Connor and Foysal Ahmad and
Mohamed S. Kamel",
title = "Multibiometric System Using Level Set, Modified {LBP}
and Random Forest",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "3",
pages = "1450013",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500132?",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 26 06:23:26 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lin:2014:LBF,
author = "Jian Lin and Bo Peng and Tianrui Li",
title = "A Learning-Based Framework for Supervised and
Unsupervised Image Segmentation Evaluation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "3",
pages = "1450014",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500144?",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Aug 26 06:23:26 MDT 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bareja:2014:IIB,
author = "Milan N. Bareja and Chintan K. Modi",
title = "An Improved Iterative Back Projection Based Single
Image Super Resolution Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "4",
pages = "1450015",
month = oct,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500156",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Dec 3 09:27:35 MST 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wei:2014:MEM,
author = "Jie Wei",
title = "On {Markov Earth Mover}'s Distance",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "4",
pages = "1450016",
month = oct,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500168",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Dec 3 09:27:35 MST 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ma:2014:EDV,
author = "Ji Ma and David Murphy and Gregory Provan and Cian
O'Mathuna and Michael Hayes",
title = "The Evaluation of Direct Volume Rendering-Based
Uncertainty Visualization Techniques for {$3$D} Scalar
Data",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "4",
pages = "1450017",
month = oct,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781450017X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Dec 3 09:27:35 MST 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{AlNachar:2014:REB,
author = "Rabih {Al Nachar} and Elie Inaty and Patrick J. Bonnin
and Yasser Alayli",
title = "A Robust Edge-Based Corner Detector {(EBCD)}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "4",
pages = "1450018",
month = oct,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500181",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Dec 3 09:27:35 MST 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jacobsen:2014:IED,
author = "C. Robert Jacobsen and Morten Nielsen",
title = "Investigation of the Effects of Data Collection on
Visual Stylometry",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "4",
pages = "1450019",
month = oct,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500193",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Dec 3 09:27:35 MST 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Senapati:2014:ILE,
author = "Ranjan Kumar Senapati and Prasanth Mankar",
title = "Improved Listless Embedded Block Partitioning
Algorithms for Image Compression",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "4",
pages = "1450020",
month = oct,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781450020X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Dec 3 09:27:35 MST 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Guo:2014:FLD,
author = "Yanyan Guo and Xiangdong Fei and Qijun Zhao",
title = "Fingerprint Liveness Detection Using Multiple Static
Features and Random Forests",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "4",
pages = "1450021",
month = oct,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814500211",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Dec 3 09:27:35 MST 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2014:AIV,
author = "Anonymous",
title = "Author Index (Volume 14)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "14",
number = "4",
pages = "1499001",
month = oct,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467814990010",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Dec 3 09:27:35 MST 2014",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Suruliandi:2015:EEG,
author = "A. Suruliandi and G. Murugeswari and P. Arockia Jansi
Rani",
title = "Empirical Evaluation of Generic Weighted Cubicle
Pattern and {LBP} Derivatives for Abnormality Detection
in Mammogram Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "1",
pages = "1550001",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500011",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:35 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Singh:2015:MOT,
author = "Brij Mohan Singh and Rahul Sharma and Debashis Ghosh
and Ankush Mittal",
title = "Multi-Oriented Text Extraction in Stylistic
Documents",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "1",
pages = "1550002",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500023",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:35 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{daSilva:2015:EDB,
author = "Ricardo Dutra da Silva and Rosane Minghim and Helio
Pedrini",
title = "{$3$D} Edge Detection Based on {Boolean} Functions and
Local Operators",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "1",
pages = "1550003",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500035",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:35 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Heickal:2015:CVB,
author = "Hasnain Heickal and Tao Zhang and Md. Hasanuzzaman",
title = "Computer Vision-Based Real-Time {$3$D} Gesture
Recognition Using Depth Image",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "1",
pages = "1550004",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500047",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:35 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Keefer:2015:SDI,
author = "Robert Keefer and Nikolaos Bourbakis",
title = "A Survey on Document Image Processing Methods Useful
for Assistive Technology for the Blind",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "1",
pages = "1550005",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500059",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:35 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Filisbino:2015:CRM,
author = "Tiene A. Filisbino and Gilson A. Giraldi and Carlos E.
Thomaz",
title = "Comparing Ranking Methods for Tensor Components in
Multilinear and Concurrent Subspace Analysis with
Applications in Face Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "1",
pages = "1550006",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500060",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:35 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2015:IFB,
author = "Shuiwang Li and Qijun Zhao and Xiangdong Fei",
title = "An Improved {AM--FM}-Based Approach for Reconstructing
Fingerprints from Minutiae",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "1",
pages = "1550007",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500072",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:35 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Stando:2015:P,
author = "Jacek Stando and Ali Dehghan Tanha and Waralak V.
Siricharoen and Yoshiro Imai",
title = "Preface",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "2",
pages = "1502001",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815020015",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:43 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kim:2015:DDA,
author = "Yejin Kim and Myunggyu Kim",
title = "Data-Driven Approach for Human Locomotion Generation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "2",
pages = "1540001",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781540001X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:43 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Javadi:2015:ARI,
author = "Mohammad Saleh Javadi and Zulaikha Kadim and Hon Hock
Woon and Khairunnisa Mohamed Johari and Norshuhada
Samudin",
title = "An Automatic Robust Image Registration Algorithm for
Aerial Mapping",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "2",
pages = "1540002",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815400021",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:43 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hashimoto:2015:VCS,
author = "Hideyuki Hashimoto and Yuki Fujibayashi and Hiroki
Imamura",
title = "{$3$D} Video Communication System by Using {Kinect}
and Head Mounted Displays",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "2",
pages = "1540003",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815400033",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:43 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Iwasaki:2015:RRM,
author = "Fumiya Iwasaki and Hiroki Imamura",
title = "A Robust Recognition Method for Occlusion of Mini
Tomatoes Based on Hue Information and the Curvature",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "2",
pages = "1540004",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815400045",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:43 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Engelbrecht:2015:DVI,
author = "Louis Engelbrecht and Adele Botha and Ronell Alberts",
title = "Designing the Visualization of Information",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "2",
pages = "1540005",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815400057",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:43 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Refaey:2015:BRL,
author = "Mohammed A. A. Refaey",
title = "Background Ruled-Lines Detection and Removal in
Full-Colored Handwritten Image Documents",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "2",
pages = "1540006",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815400069",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Apr 15 14:00:43 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Latha:2015:LFI,
author = "Y. L. Malathi Latha and Munaga V. N. K. Prasad and
Banoth Sammulal",
title = "Local Feature Integration Method Using Phase
Congruency for Palm Print Authentication",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "3",
pages = "1550008",
month = jul,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500084",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 12 10:01:17 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Syamsuddin:2015:FFB,
author = "Muhammad Rusdi Syamsuddin and Jimwook Kim and Sung-Hee
Lee",
title = "Force Field-Based Control of Dynamic Particles with
User-Specified Paths",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "3",
pages = "1550009",
month = jul,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500096",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 12 10:01:17 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2015:HIA,
author = "Hao Liu and Hongbo Qian and Ning Dai and Jianning
Zhao",
title = "Heuristic Initialization for Active Contour Models in
{CT\slash MRI} Image Processing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "3",
pages = "1550010",
month = jul,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500102",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 12 10:01:17 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dehshibi:2015:GPW,
author = "Mohammad Mahdi Dehshibi and Ali Shirmohammadi and
Andrew Adamatzky",
title = "On Growing {Persian} Words with {$L$}-Systems: Visual
Modeling of {Neyname}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "3",
pages = "1550011",
month = jul,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500114",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 12 10:01:17 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shreekanth:2015:HBT,
author = "T. Shreekanth and V. Udayashankara",
title = "A Histogram-Based Two-Stage Adaptive Character
Segmentation for Transcription of Inter-Point {Hindi
Braille} to Text",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "3",
pages = "1550012",
month = jul,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500126",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 12 10:01:17 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2015:CMS,
author = "Shiguang Liu and Dongfang Fan",
title = "Computer Modeling and Simulation of Fruit Sunscald",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "3",
pages = "1550013",
month = jul,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500138",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 12 10:01:17 MDT 2015",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sidram:2015:ENS,
author = "M. H. Sidram and Nagappa U. Bhajantri",
title = "An Exploration with Novel Shape Signature of {GMSC}
Distance Function to Track the Object",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "4",
pages = "1550014",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781550014X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:06 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bhattacharjee:2015:CBH,
author = "Debjyoti Bhattacharjee and Ashish Bakshi and Kuntal
Ghosh",
title = "Comparison Between an {HVS} Inspired Linear Filter and
the Bilateral Filter in Performing ``Vision at a
Glance'' through Smoothing with Edge Preservation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "4",
pages = "1550015",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500151",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:06 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Peng:2015:UBT,
author = "Chao Peng and Bing Fang and Francis Quek and Yong Cao
and Seung In Park and Liguang Xie",
title = "Upper Body Tracking and {$3$D} Gesture Reconstruction
Using Agent-Based Architecture",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "4",
pages = "1550016",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500163",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:06 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Angadi:2015:LWT,
author = "S. A. Angadi and M. M. Kodabagi",
title = "A Light Weight Text Extraction Technique for Hand-Held
Device",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "4",
pages = "1550017",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500175",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:06 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Carvalho:2015:IGB,
author = "L. E. Carvalho and S. L. Mantelli Neto and A. C.
Sobieranski and E. Comunello and A. von Wangenheim",
title = "Improving Graph-Based Image Segmentation Using
Nonlinear Color Similarity Metrics",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "4",
pages = "1550018",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500187",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:06 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Saini:2015:SVB,
author = "Deepika Saini and Sanjeev Kumar",
title = "Stereo Vision-Based Conic Reconstruction Using a
Ray-Quadric Intersection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "4",
pages = "1550019",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815500199",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:06 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2015:AIV,
author = "Anonymous",
title = "Author Index (Volume 15)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "15",
number = "4",
pages = "1599001",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467815990016",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:06 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bandyopadhyay:2016:ASB,
author = "Oishila Bandyopadhyay and Bhabatosh Chanda and Bhargab
B. Bhattacharya",
title = "Automatic Segmentation of Bones in {X}-ray Images
Based on Entropy Measure",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "1",
pages = "1650001",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500017",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:07 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cai:2016:PRL,
author = "Haipeng Cai",
title = "Parallel Rendering for Legible Illustrative
Visualizations of Dense Geometries on Commodity
{CPUs}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "1",
pages = "1650002",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500029",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:07 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yuan:2016:ADM,
author = "Jianjun Yuan and Lipei Liu",
title = "Anisotropic Diffusion Model Based on a New Diffusion
Coefficient and Fractional Order Differential for Image
Denoising",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "1",
pages = "1650003",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500030",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:07 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{McGraw:2016:SNN,
author = "Tim McGraw and Jisun Kang and Donald Herring",
title = "Sparse Non-Negative Matrix Factorization for Mesh
Segmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "1",
pages = "1650004",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500042",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:07 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Aswatha:2016:IRS,
author = "Shashaank M. Aswatha and Jayanta Mukherjee and Partha
Bhowmick",
title = "An Integrated Repainting System for Digital
Restoration of {Vijayanagara} Murals",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "1",
pages = "1650005",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500054",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Feb 26 05:50:07 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Manimehalai:2016:NRR,
author = "P. Manimehalai and P. Arockia Jansi Rani",
title = "A New Robust Reversible Blind Watermarking in
Wavelet-Domain for Color Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "2",
pages = "1650006",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500066",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 5 06:44:22 MDT 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pratihar:2016:FDP,
author = "Sanjoy Pratihar and Partha Bhowmick",
title = "Fast and Direct Polygonization for Gray-Scale Images
Using Digital Straightness and Exponential Averaging",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "2",
pages = "1650007",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500078",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 5 06:44:22 MDT 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bini:2016:IRU,
author = "A. A. Bini and P. Jidesh",
title = "Image Restoration Using Adaptive Region-Wise $p$-Norm
Filter with Local Constraints",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "2",
pages = "1650008",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781650008X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 5 06:44:22 MDT 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Grim:2016:ART,
author = "Anna Grim and Timothy O'Connor and Peter J. Olver and
Chehrzad Shakiban and Ryan Slechta and Robert
Thompson",
title = "Automatic Reassembly of Three-Dimensional Jigsaw
Puzzles",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "2",
pages = "1650009",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500091",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 5 06:44:22 MDT 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Fathimal:2016:SSS,
author = "P. Mohamed Fathimal and P. Arockia Jansi Rani",
title = "{$K$} out of {$N$} Secret Sharing Scheme for Multiple
Color Images with Steganography and Authentication",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "2",
pages = "1650010",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500108",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 5 06:44:22 MDT 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yuan:2016:PMM,
author = "Jianjun Yuan and Jianjun Wang",
title = "{Perona--Malik} Model with a New Diffusion Coefficient
for Image Denoising",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "2",
pages = "1650011",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781650011X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 5 06:44:22 MDT 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tomar:2016:LRP,
author = "Divya Tomar and Sonali Agarwal",
title = "Leaf Recognition for Plant Classification Using Direct
Acyclic Graph Based Multi-Class Least Squares Twin
Support Vector Machine",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "3",
pages = "1650012",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500121",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Nov 16 05:43:38 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sethi:2016:CAD,
author = "Gaurav Sethi and B. S. Saini",
title = "Computer Aided Diagnosis of Abdomen Diseases Using
Curvelet Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "3",
pages = "1650013",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500133",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Nov 16 05:43:38 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ladha:2016:RPC,
author = "Shamsuddin N. Ladha and Kate Smith-Miles and Sharat
Chandran",
title = "Realistic Projection on Casual Dual-Planar Surfaces
with Global Illumination Compensation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "3",
pages = "1650014",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500145",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Nov 16 05:43:38 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sowmyayani:2016:ETR,
author = "S. Sowmyayani and P. Arockia Jansi Rani",
title = "An Efficient Temporal Redundancy Transformation for
Wavelet Based Video Compression",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "3",
pages = "1650015",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500157",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Nov 16 05:43:38 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gupta:2016:CAA,
author = "Pooja Gupta and Kuldip Pahwa",
title = "Clock Algorithm Analysis for Increasing Quality of
Digital Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "3",
pages = "1650016",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500169",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Nov 16 05:43:38 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Caetano:2016:VDU,
author = "Felipe Andrade Caetano and Marcelo Bernardes Vieira
and Rodrigo Luis de Souza da Silva",
title = "A Video Descriptor Using Orientation Tensors and
Shape-Based Trajectory Clustering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "4",
pages = "1650017",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500170",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 17 05:56:01 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Raja:2016:EMT,
author = "S. P. Raja and A. Suruliandi",
title = "Evaluating Multiscale Transform Based Image
Compression Using Encoding Techniques",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "4",
pages = "1650018",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500182",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 17 05:56:01 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{daSilva:2016:GFA,
author = "Fl{\'a}vio Altinier Maximiano da Silva and Helio
Pedrini",
title = "Geometrical Features and Active Appearance Model
Applied to Facial Expression Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "4",
pages = "1650019",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500194",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 17 05:56:01 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Atiampo:2016:UIS,
author = "Armand Kodjo Atiampo and Georges Laussane Loum",
title = "Unsupervised Image Segmentation with Pairwise {Markov}
Chains Based on Nonparametric Estimation of Copula
Using Orthogonal Polynomials",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "4",
pages = "1650020",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500200",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 17 05:56:01 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Singh:2016:IGZ,
author = "Geetika Singh and Indu Chhabra",
title = "Integrating Global {Zernike} and Local Discriminative
{HOG} Features for Face Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "4",
pages = "1650021",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500212",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 17 05:56:01 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gambhir:2016:NFR,
author = "Deepak Gambhir and Meenu Manchanda",
title = "A Novel Fusion Rule for Medical Image Fusion in
Complex Wavelet Transform Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "4",
pages = "1650022",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816500224",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 17 05:56:01 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2016:AIV,
author = "Anonymous",
title = "Author Index (Volume 16)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "16",
number = "4",
pages = "1699001",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467816990011",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 17 05:56:01 MST 2016",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Al-Naji:2017:CCA,
author = "Ali Al-Naji and Javaan Chahl",
title = "Contactless Cardiac Activity Detection Based on Head
Motion Magnification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "1",
pages = "1750001",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467817500012",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 26 07:01:04 MST 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kavitha:2017:WBF,
author = "J. Kavitha and P. Arockia Jansi Rani and S.
Sowmyayani",
title = "Wavelet-Based Feature Vector for Shot Boundary
Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "1",
pages = "1750002",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467817500024",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 26 07:01:04 MST 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kittisuwan:2017:TID,
author = "P. Kittisuwan",
title = "Textural Image Denoising Using {Gumbel} Random Vectors
in {Gaussian} Noise",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "1",
pages = "1750003",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467817500036",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 26 07:01:04 MST 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nnolim:2017:FBM,
author = "Uche A. Nnolim",
title = "{FPGA}-Based Multiplier-Less Log-Based Hardware
Architectures for Hybrid Color Image Enhancement
System",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "1",
pages = "1750004",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467817500048",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 26 07:01:04 MST 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bhat:2017:MIF,
author = "Aruna Bhat",
title = "Makeup Invariant Face Recognition using Features from
Accelerated Segment Test and Eigen Vectors",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "1",
pages = "1750005",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781750005X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 26 07:01:04 MST 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cejnog:2017:WAR,
author = "Luciano W. X. Cejnog and Fernando A. A. Yamada and
Marcelo Bernardes Vieira",
title = "Wide Angle Rigid Registration Using a Comparative
Tensor Shape Factor",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "1",
pages = "1750006",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467817500061",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Jan 26 07:01:04 MST 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tian:2017:ISC,
author = "Chunwei Tian and Guanglu Sun and Qi Zhang and Weibing
Wang and Teng Chen and Yuan Sun",
title = "Integrating Sparse and Collaborative Representation
Classifications for Image Classification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "2",
pages = "1750007",
month = apr,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:12 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Manchanda:2017:FTB,
author = "Meenu Manchanda and Rajiv Sharma",
title = "Fuzzy Transform-Based Fusion of Multiple Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "2",
pages = "1750008",
month = apr,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:12 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kontakis:2017:SIC,
author = "Konstantinos Kontakis and Athanasios G. Malamos and
Malvina Steiakaki and Spyros Panagiotakis",
title = "Spatial Indexing of Complex Virtual Reality Scenes in
the {Web}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "2",
pages = "1750009",
month = apr,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:12 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ghislain:2017:ATD,
author = "Pandry Koffi Ghislain and Georges Lausanne Loum and
Ouattara Nouho",
title = "Adaptation of Telegraph Diffusion Equation for Noise
Reduction on Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "2",
pages = "1750010",
month = apr,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:12 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shah:2017:NMI,
author = "Said Khalid Shah",
title = "Nonrigid Medical Image Registration Based on Curves",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "2",
pages = "1750011",
month = apr,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:12 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Parseh:2017:NCF,
author = "Mohammad Javad Parseh and Mojtaba Meftahi",
title = "A New Combined Feature Extraction Method for {Persian}
Handwritten Digit Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "2",
pages = "1750012",
month = apr,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:12 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Maity:2017:ODC,
author = "Santi P. Maity and Hirak Kumar Maity",
title = "Optimality in Distortion Control in Reversible
Watermarking Using Genetic Algorithms",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "3",
pages = "1750013",
month = jul,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:13 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Farshi:2017:INR,
author = "Taymaz Rahkar Farshi",
title = "Image Noise Reduction Method Based on Compatibility
with Adjacent Pixels",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "3",
pages = "1750014",
month = jul,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:13 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sarfraz:2017:QTN,
author = "Muhammad Sarfraz and Shamaila Samreen and Malik Zawwar
Hussain",
title = "A Quadratic Trigonometric Nu Spline with Shape
Control",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "3",
pages = "1750015",
month = jul,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:13 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Prabhanjan:2017:DLA,
author = "S. Prabhanjan and R. Dinesh",
title = "Deep Learning Approach for {Devanagari} Script
Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "3",
pages = "1750016",
month = jul,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:13 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Abdelwahab:2017:IIS,
author = "Ahmed A. Abdelwahab",
title = "Inter-Image Similarity-Based Fast Adaptive Block Size
Vector Quantizer for Image Coding",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "3",
pages = "1750017",
month = jul,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:13 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Alcantara:2017:HAC,
author = "Marlon F. Alcantara and Helio Pedrini and Yu Cao",
title = "Human Action Classification Based on Silhouette
Indexed Interest Points for Multiple Domains",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "3",
pages = "1750018",
month = jul,
year = "2017",
CODEN = "????",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 24 06:24:13 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Maehara:2017:DOR,
author = "Seiichi Maehara and Kazuo Ikeshiro and Hiroki
Imamura",
title = "A $3$-Dimensional Object Recognition Method Using
Relationship of Distances and Angles in Feature
Points",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "4",
pages = "1750019",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781750019X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 31 06:37:09 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Fei:2017:EME,
author = "Lunke Fei and Shaohua Teng and Jigang Wu and Imad
Rida",
title = "Enhanced Minutiae Extraction for High-Resolution
Palmprint Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "4",
pages = "1750020",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467817500206",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 31 06:37:09 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yamada:2017:SBW,
author = "F. A. A. Yamada and L. W. X. Cejnog and M. B. Vieira
and R. L. S. da Silva",
title = "A Shape-Based Weighting Strategy Applied to the
Covariance Estimation on {ICP}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "4",
pages = "1750021",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467817500218",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 31 06:37:09 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nnolim:2017:FBH,
author = "Uche A. Nnolim",
title = "{FPGA}-Based Hardware Architecture for Fuzzy
Homomorphic Enhancement Based on Partial Differential
Equations",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "4",
pages = "1750022",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781750022X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 31 06:37:09 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cheng:2017:HLM,
author = "Ruzhong Cheng and Yongjun Zhang and Guoping Wang and
Yong Zhao and Rahmatulloev Khusravsho",
title = "{Haar}-Like Multi-Granularity Texture Features for
Pedestrian Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "4",
pages = "1750023",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467817500231",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 31 06:37:09 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2017:OAS,
author = "Qianwen Li and Zhihua Wei and Cairong Zhao",
title = "Optimized Automatic Seeded Region Growing Algorithm
with Application to {ROI} Extraction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "4",
pages = "1750024",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467817500243",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 31 06:37:09 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2017:AIV,
author = "Anonymous",
title = "Author Index (Volume 17)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "17",
number = "4",
pages = "1799001",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467817990017",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Oct 31 06:37:09 MDT 2017",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wei:2018:AQE,
author = "Jie Wei and Lin Zhang and Bingmei M. Fu",
title = "Automatic Quantification of Endothelial Nitric Oxide
Levels in a Microvessel with and without Tumor Cell
Adhesion",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "1",
pages = "1850001",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500018",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 24 07:10:26 MST 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2018:IST,
author = "Huan Wang and Fei Yang and Congcong Zhang and Mingwu
Ren",
title = "Infrared Small Target Detection Based on Patch Image
Model with Local and Global Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "1",
pages = "1850002",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781850002X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 24 07:10:26 MST 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Verma:2018:IDU,
author = "Atul Kumar Verma and Barjinder Singh Saini and
Taranjit Kaur",
title = "Image Denoising using {Alexander} Fractional Hybrid
Filter",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "1",
pages = "1850003",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500031",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 24 07:10:26 MST 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Divakara:2018:HSA,
author = "S. S. Divakara and Sudarshan Patilkulkarni and Cyril
Prasanna Raj",
title = "High Speed Area Optimized Hybrid {DA} Architecture for
{$2$D-DTCWT}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "1",
pages = "1850004",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500043",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 24 07:10:26 MST 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Domadiya:2018:SFE,
author = "Prashant Domadiya and Pratik Shah and Suman K. Mitra",
title = "Shadow-Free, Expeditious and Precise, Moving Object
Separation from Video",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "1",
pages = "1850005",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500055",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 24 07:10:26 MST 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Choudhury:2018:SBC,
author = "Bismita Choudhury and Patrick Then and Biju Issac and
Valliappan Raman and Manas Kumar Haldar",
title = "A Survey on Biometrics and Cancelable Biometrics
Systems",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "1",
pages = "1850006",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500067",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jan 24 07:10:26 MST 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Giangreco-Maidana:2018:CPS,
author = "Alejandro J. Giangreco-Maidana and Horacio Legal-Ayala
and Christian E. Schaerer and Waldemar
Villamayor-Venialbo",
title = "Contour-Point Signature Shape Descriptor for Point
Correspondence",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "2",
pages = "1850007",
month = apr,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500079",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Apr 7 18:25:20 MDT 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ghosh:2018:VVE,
author = "Swarup Kr Ghosh and Anupam Ghosh and Amlan
Chakrabarti",
title = "{VEA}: Vessel Extraction Algorithm by Active Contour
Model and a Novel Wavelet Analyzer for Diabetic
Retinopathy Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "2",
pages = "1850008",
month = apr,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500080",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Apr 7 18:25:20 MDT 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chen:2018:VCE,
author = "Liang-Hua Chen and Chih-Wen Su",
title = "Video Caption Extraction Using Spatio-Temporal
Slices",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "2",
pages = "1850009",
month = apr,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500092",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Apr 7 18:25:20 MDT 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shreekanth:2018:NDI,
author = "T. Shreekanth and M. R. Deeksha and Karthikeya R.
Kaushik",
title = "A Novel Data Independent Approach for Conversion of
Hand Punched {Kannada} {Braille} Script to Text and
Speech",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "2",
pages = "1850010",
month = apr,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500109",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Apr 7 18:25:20 MDT 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ngom:2018:SDP,
author = "Ndeye Fatou Ngom and Cheikh H. T. C. Ndiaye and Oumar
Niang and Samba Sidibe",
title = "Shape Descriptors for Porous Media Analysis Using
Computed Tomography Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "2",
pages = "1850011",
month = apr,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500110",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Apr 7 18:25:20 MDT 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wu:2018:IAB,
author = "Zhaoqi Wu and Reziwanguli Xiamixiding and Atul
Sajjanhar and Juan Chen and Quan Wen",
title = "Image Appearance-Based Facial Expression Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "2",
pages = "1850012",
month = apr,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500122",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Apr 7 18:25:20 MDT 2018",
bibsource = "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Helmy:2018:GFS,
author = "Tarek Helmy",
title = "A Generic Framework for Semantic Annotation of
Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "3",
pages = "??--??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500134",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:48 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500134",
abstract = "Advanced digital capturing technologies have led to
the explosive growth of images on the Web. To retrieve
the desired image from a huge amount of images, textual
query is handier to represent the user's interest than
providing a visually similar image as a query. Semantic
annotation of images' has been identified as an
important step towards more efficient manipulation and
retrieval of images. The aim of the semantic annotation
of images is to annotate the existing images on the Web
so that the images are more easily interpreted by
searching programs. To annotate the images effectively,
extensive image interpretation techniques have been
developed to explore the semantic concept of images.
But, due to the complexity and variety of backgrounds,
effective image annotation is still a very challenging
and open problem. Semantic annotation of Web contents
manually is not feasible or scalable too, due to the
huge amount and rate of emerging Web content. In this
paper, we have surveyed the existing image annotation
models and developed a hierarchical
classification-based image annotation framework for
image categorization, description and annotation.
Empirical evaluation of the proposed framework with
respect to its annotation accuracy shows high precision
and recall compared with other annotation models with
significant time and cost. An important feature of the
proposed framework is that its specific annotation
techniques, suitable for a particular image category,
can be easily integrated and developed for other image
categories.",
acknowledgement = ack-nhfb,
articleno = "1850013",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Benchaou:2018:FSB,
author = "Soukaina Benchaou and M'Barek Nasri and Ouafae {El
Melhaoui}",
title = "Feature Selection Based on Evolution Strategy for
Character Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "3",
pages = "??--??",
month = jul,
year = "2018",
DOI = "https://doi.org/10.1142/S0219467818500146",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:48 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500146",
abstract = "Handwriting, printed character recognition is an
interesting area in image processing and pattern
recognition. It consists of a number of phases which
are preprocessing, feature extraction and
classification. The phase of feature extraction is
carried out by different techniques; zoning, profile
projection, and ameliored Freeman. The high number of
features vector can increase the error rate and the
training time. So, to solve this problem, we present in
this paper a new method of selecting attributes based
on the evolution strategy in order to reduce the
feature vector dimension and to improve the recognition
rate. The proposed model has been applied to recognize
numerals and it obtained a better results and showed
more robustness than without the selection system.",
acknowledgement = ack-nhfb,
articleno = "1850014",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Roy:2018:HIR,
author = "Aniket Roy and Arpan Kumar Maiti and Kuntal Ghosh",
title = "An {HVS} Inspired Robust Non-blind Watermarking Scheme
in {YCbCr} Color Space",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "3",
pages = "??--??",
month = jul,
year = "2018",
DOI = "https://doi.org/10.1142/S0219467818500158",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:48 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500158",
abstract = "Digital Watermarking is an important tool for
copyright protection. A good quality watermarking
scheme should provide both perceptual transparency as
well as robustness against attacks. Many efficient
schemes exist for grayscale image watermarking, but
relatively less attention has been paid to watermarking
for color images. Moreover, the existing works do not
provide adequate justification for the possible choice
of color space. In this paper, justification is
provided for the choice of YCbCr color space for
watermark embedding. A human visual system
(HVS)-inspired image-adaptive non-blind watermarking
scheme in the YCbCr space has subsequently been
proposed. This new algorithm has been referred to as
the Additive Embedding Scheme (AES). It comprises of a
modified watermarking strength parameter (
{\textalpha}mean {\textalpha}mean {\textalpha}mean ),
in combination with the discrete wavelet transform and
singular value decomposition (DWT-SVD). Experimental
results demonstrate that the proposed watermarking
scheme in YCbCr color space provides better perceptual
quality as well as robustness against attacks as
compared to existing schemes. We have further
improvised the aforementioned scheme to come up with a
Multiplicative Embedding Scheme (MES) for additional
robustness against a special type of attack, viz. the
Singular Value Exchange Attack.",
acknowledgement = ack-nhfb,
articleno = "1850015",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sacht:2018:RTC,
author = "Leonardo Sacht and Diego Nehab and Rodolfo Schulz de
Lima",
title = "Real-Time Continuous Image Processing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "3",
pages = "??--??",
month = jul,
year = "2018",
DOI = "https://doi.org/10.1142/S021946781850016X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:48 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946781850016X",
abstract = "In this work, we propose a framework that performs a
number of popular image-processing operations in the
continuous domain. This is in contrast to the standard
practice of defining them as operations over discrete
sequences of sampled values. The guiding principle is
that, in order to prevent aliasing, nonlinear
image-processing operations should ideally be performed
prior to prefiltering and sampling. This is of course
impractical, as we may not have access to the
continuous input. Even so, we show that it is best to
apply image-processing operations over the continuous
reconstruction of the input. This transformed
continuous representation is then prefiltered and
sampled to produce the output. The use of high-quality
reconstruction strategies brings this alternative much
closer to the ideal than directly operating over
discrete values. We illustrate the advantages of our
framework with several popular effects. In each case,
we demonstrate the quality difference between
continuous image-processing, their discrete
counterparts and previous anti-aliasing alternatives.
Finally, our GPU implementation shows that current
graphics hardware has enough computational power to
perform continuous image processing in real-time.",
acknowledgement = ack-nhfb,
articleno = "1850016",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhang:2018:ACF,
author = "Jinpeng Zhang and Jinming Zhang",
title = "An Analysis of {CNN} Feature Extractor Based on {KL}
Divergence",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "3",
pages = "??--??",
month = jul,
year = "2018",
DOI = "https://doi.org/10.1142/S0219467818500171",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:48 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500171",
abstract = "Convolutional neural networks (CNNs) have brought in
exciting progress in many computer vision tasks. But
the feature extraction process executed by CNN still
keeps a black box to us, and we have not fully
understood its working mechanism. In this paper, we
propose a method to evaluate CNN features and further
to analyze the CNN feature extractor, which is inspired
by Bayes Classification Theory and KL divergence (KLD).
Experiments have shown that CNN can promote feature
discrimativeness by gradually increasing the
intra-class KLD, and meanwhile promote feature
robustness by gradually decreasing the inner-class KLD
during training. Experiments also reveal that, with the
deepening of network, CNN can gradually improve
separability information density in feature space and
encode much more separability information into the
final feature vectors.",
acknowledgement = ack-nhfb,
articleno = "1850017",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lu:2018:FVS,
author = "Yan Lu and Bin Liu and Weihai Li and Nenghai Yu",
title = "Fast Video Stitching for Aerially Captured {HD}
Videos",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "3",
pages = "??--??",
month = jul,
year = "2018",
DOI = "https://doi.org/10.1142/S0219467818500183",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:48 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500183",
abstract = "Videos captured from the air by flying devices like
Unmanned Aerial Vehicles (UAVs) have great application
prospects in many fields such as journalism, art,
military and public security. Due to the difficulties
such as vibration, needing for speed and high
resolution and so on, it is non-trivial to apply
traditional static image stitching algorithms to flying
cameras. To this end, we propose a real-time video
stitching system which is capable to stitch high
definition (HD) videos captured by mobile aerial
devices. In our work, we use scale invariant
information to speed up the feature point extraction.",
acknowledgement = ack-nhfb,
articleno = "1850018",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Samanta:2018:LTB,
author = "Sourav Samanta and Amartya Mukherjee and Amira S.
Ashour and Nilanjan Dey and Jo{\~a}o Manuel R. S.
Tavares and Wahiba {Ben Abdessalem Kar{\^a}a} and Redha
Taiar and Ahmad Taher Azar and Aboul {Ella Hassanien}",
title = "Log Transform Based Optimal Image Enhancement Using
Firefly Algorithm for Autonomous Mini Unmanned Aerial
Vehicle: An Application of Aerial Photography",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "4",
pages = "??--??",
month = oct,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467818500195",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:50 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500195",
abstract = "The Unmanned Aerial Vehicles (UAV) are widely used for
capturing images in border area surveillance, disaster
intensity monitoring, etc. An aerial photograph offers
a permanent recording solution as well. But rapid
weather change, low quality image capturing equipments
results in low/poor contrast images during image
acquisition by Autonomous UAV. In this current study, a
well-known meta-heuristic technique, namely, Firefly
Algorithm (FA) is reported to enhance aerial images
taken by a Mini Unmanned Aerial Vehicle (MUAV) via
optimizing the value of certain parameters. These
parameters have a wide range as used in the Log
Transformation for image enhancement. The entropy and
edge information of the images is used as an objective
criterion for evaluating the image enhancement of the
proposed system. Inconsistent with the objective
criterion, the FA is used to optimize the parameters
employed in the objective function that accomplishes
the superlative enhanced image. A low-light imaging has
been performed at evening time to prove the
effectiveness of the proposed algorithm. The results
illustrate that the proposed method has better
convergence and fitness values compared to Particle
Swarm Optimization. Therefore, FA is superior to PSO,
as it converges after a less number of iterations.",
acknowledgement = ack-nhfb,
articleno = "1850019",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sharma:2018:CSS,
author = "Himani Sharma and D. C. Mishra and R. K. Sharma and
Naveen Kumar",
title = "Crypto-stego System for Securing Text and Image Data",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "4",
pages = "??--??",
month = oct,
year = "2018",
DOI = "https://doi.org/10.1142/S0219467818500201",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:50 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500201",
abstract = "Conventional techniques for security of data, designed
by using only one of the security mechanisms,
cryptography or steganography, are suitable for limited
applications only. In this paper, we propose a
crypto-stego system that would be appropriate for
secure transmission of different forms of data. In the
proposed crypto-stego system, we present a mechanism to
provide secure transmission of data by multiple safety
measures, firstly by applying encryption using Affine
Transform and Discrete Cosine Transform (DCT) and then
merging this encrypted data with an image, randomly
chosen from a set of available images, and sending the
image so obtained to the receiver at the other end
through the network. The data to be sent over a
communication channel may be a gray-scale or colored
image, or a text document ({.doc}, {.txt}, or {.pdf}
file). As it is encrypted and sent hidden in an image,
it avoids any attention to itself by the observers in
the network. At the receiver's side, reverse
transformations are applied to obtain the original
information. The experimental results, security
analysis and statistical analysis for gray-scale
images, RGB images, text documents ({.doc}, {.txt},
{.pdf} files), show robustness and appropriateness of
the proposed crypto-stego system for secure
transmission of the data through unsecured network. The
security analysis and key space analysis demonstrate
that the proposed technique is immune from
cryptanalysis.",
acknowledgement = ack-nhfb,
articleno = "1850020",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Talbi:2018:SIW,
author = "Mourad Talbi and Med Salim Bouhlel",
title = "Secure Image Watermarking Based on {LWT} and {SVD}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "4",
pages = "??--??",
month = oct,
year = "2018",
DOI = "https://doi.org/10.1142/S0219467818500213",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:50 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500213",
abstract = "Nowadays, digital watermarking is employed for
authentication and copyright protection. In this paper,
a secure image watermarking scheme based on lifting
wavelet transform (LWT) and singular value
decomposition (SVD), is proposed. Both LWT and SVD are
used as mathematical tools for embedding watermark in
the host image. In this work, the watermark is a speech
signal which is segmented into shorted portions having
the same length. This length is equal to 256 and these
different portions constitute the different columns of
a speech image. The latter is then embedded into a
grayscale or color image (the host image). This
procedure is performed in order to insert into an image
a confidential data which is in our case a speech
signal. But instead of embedding this speech signal
directly into the image, we transform it into a matrix
and treated it as an image (``a speech image''). Of
course, this speech signal transformation permits us to
use LWT-2D and SVD to both the host image and the
watermark (``a speech image''). The proposed technique
is applied to a number of grayscale and color images.
The obtained results from peak signal-to-noise ratio
(PSNR) and structural similarity (SSIM) computations
show the performance of the proposed technique.
Experimental evaluation also shows that the proposed
scheme is able to withstand a number of attacks such as
JPEG compression, mean and median attacks. In our
evaluation of the proposed technique, we used another
technique of secure image watermarking based on DWT-2D
and SVD.",
acknowledgement = ack-nhfb,
articleno = "1850021",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jangid:2018:HDS,
author = "Mahesh Jangid and Sumit Srivastava",
title = "Handwritten {Devanagari} Similar Character Recognition
by {Fisher} Linear Discriminant and Pairwise
Classification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "4",
pages = "??--??",
month = oct,
year = "2018",
DOI = "https://doi.org/10.1142/S0219467818500225",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:50 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500225",
abstract = "The research works in Handwritten Devanagari
Characters are continually evolving into new
challenges, which exposed the new sources of further
research work like, character normalization, gray-level
normalization, a discrimination of the similar
characters and many more. This paper discusses the
discrimination of the similar characters, which is one
of the major sources of classification error. The
similar shape character has a very minute difference,
which is called critical region and used to
discriminate them by human beings. The primary goal of
the current work is to identify the critical region of
the similar character and use the same to generate
additional features in order to minimize the
classification errors in the end results. It is also
quite challenging to identify the critical region as
the characters are written in different handwriting
styles and fonts. The paper suggests the Fisher linear
discriminant model to detect the critical region, which
is used to extract the additional feature. The
experiments work was conducted on the standard
database, which has 36 172 handwritten Devanagari
characters and significant improvement has been
recorded by the aforesaid technique.",
acknowledgement = ack-nhfb,
articleno = "1850022",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Salehi:2018:RHF,
author = "Hadi Salehi and Javad Vahidi and Homayun Motameni",
title = "A Robust Hybrid Filter Based on Evolutionary
Intelligence and Fuzzy Evaluation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "4",
pages = "??--??",
month = oct,
year = "2018",
DOI = "https://doi.org/10.1142/S0219467818500237",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:50 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500237",
abstract = "In this paper, a novel denoising method based on
wavelet, extended adaptive Wiener filter and the
bilateral filter is proposed for digital images.
Production of mode is accomplished by the genetic
algorithm. The proposed extended adaptive Wiener filter
has been developed from the adaptive Wiener filter.
First, the genetic algorithm suggest some hybrid
models. The attributes of images, including peak signal
to noise ratio, signal to noise ratio and image quality
assessment are studied. Then, in order to evaluate the
model, the values of attributes are sent to the Fuzzy
deduction system. Simulations and evaluations mentioned
in this paper are accomplished on some standard images
such as Lena, boy, fruit, mandrill, Barbara, butterfly,
and boat. Next, weaker models are omitted by studying
of the various models. Establishment of new generations
performs in a form that a generation emendation is
carried out, and final model has a more optimum quality
compared to each two filters in order to obviate the
noise. At the end, the results of this system are
studied so that a comprehensive model with the best
performance is to be found. Experiments show that the
proposed method has better performance than wavelet,
bilateral, Butterworth, and some other filters.",
acknowledgement = ack-nhfb,
articleno = "1850023",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kittisuwan:2018:TRD,
author = "Pichid Kittisuwan",
title = "Textural Region Denoising: Application in
Agriculture",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "4",
pages = "??--??",
month = oct,
year = "2018",
DOI = "https://doi.org/10.1142/S0219467818500249",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:50 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818500249",
abstract = "Geo-science and remote sensing technologies play
enormous roles in agriculture nowadays, especially in
analysis of data from aerial images such as satellite
images and drone images. Most agricultural images
contain more textural regions than non-textural
regions. Therefore, data management in terms of
textural regions is very important. Indeed, additive
white Gaussian noise (AWGN) is the fundamental problem
in digital image analysis. In wavelet transform,
Bayesian estimation is useful in several noise
reduction methods. The Bayesian technique requires a
prior modeling of noise-free wavelet coefficients. In
non-textural regions, the wavelet coefficients might be
better modeled by super-Gaussian density such as
Laplacian, Pearson type VII, Cauchy, and two-sided
gamma distributions. However, the statistical model of
textural regions is Gaussian model. Therefore, in
agricultural images, we require flexible model between
super-Gaussian and Gaussian models. In fact, the
generalized Gaussian distribution (GGD) is the suitable
model for this problem. Therefore, we present new
maximum a posteriori (MAP) estimator for spacial case
of GGD in AWGN. Here, we obtained the analytical form
solution. Moreover, this research work will also
describe limitations of GGD application in Bayesian
estimator. The simulation results illustrate that our
presented method outperforms the state-of-the-art
methods qualitatively and quantitatively.",
acknowledgement = ack-nhfb,
articleno = "1850024",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2018:AIV,
author = "Anonymous",
title = "Author Index (Volume 18)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "18",
number = "4",
pages = "??--??",
month = oct,
year = "2018",
DOI = "https://doi.org/10.1142/S0219467818990012",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Nov 9 06:55:50 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467818990012",
acknowledgement = ack-nhfb,
articleno = "1899001",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Arani:2019:HFW,
author = "Seyed Ali Asghar Abbaszadeh Arani and Ehsanollah Kabir
and Reza Ebrahimpour",
title = "Handwritten {Farsi} Word Recognition Using {NN}-Based
Fusion of {HMM} Classifiers with Different Types of
Features",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "1",
pages = "??--??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467819500013",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 14 06:31:30 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500013",
abstract = "In this paper, an off-line method, based on hidden
Markov model, HMM, is used for holistic recognition of
handwritten words of a limited vocabulary. Three
feature sets based on image gradient, black--white
transition and contour chain code are used. For each
feature set an HMM is trained for each word. In the
recognition step, the outputs of these classifiers are
combined through a multilayer perceptron, MLP. High
number of connections in this network causes a
computational complexity in the training. To avoid this
problem, a new method is proposed. In the experiments
on 16000 images of 200 names of Iranian cities, from
``Iranshahr 3'' dataset, the results of the proposed
method are presented and compared with some similar
methods. An error analysis on these results is also
provided.",
acknowledgement = ack-nhfb,
articleno = "1950001",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dutta:2019:ISB,
author = "P. K. Dutta",
title = "Image Segmentation Based Approach for the Purpose of
Developing Satellite Image Spatial Information
Extraction for Forestation and River Bed Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "1",
pages = "??--??",
month = jan,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500025",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 14 06:31:30 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500025",
abstract = "Classification of remote sensing spatial information
from multi spectral satellite imagery can be used to
obtain multiple representation of the image and capture
different structure lineaments. Pixels are grouped
using clustering and morphology based segmentation for
region based spatial information. This is used to
calculate the spatial features of the contiguous
regions by classifying the region into the statistics
of the pixel properties. In the proposed work, analysis
of Google Earth images for identification of
morphological patterns of the river flow is done for
remote sensing image using graph-cuts. Multi-temporal
satellite images acquired from Google Earth to identify
the digital elevation is used to formulate the energy
function from images to compare the displacement in
pixel value using similarity measure. A method is
proposed to solve non-rigid image transformation via
graph-cuts algorithm by modeling the registration
process as a discrete labeling problem. A displacement
vector associated to each pixel in the source image
indicates the corresponding position in the moving
image. The transformation matrix produced from change
in the intensity of the pixels for a region is then
optimized for energy minimization by using the
graph-cuts algorithm and demon registration technique.
The proposed study enhances the advantages of regional
segmentation in order to know homogeneous areas for
optimal image segmentation and digital footprints for
change in the river bed patterns by identifying the
change in LANDSAT data from temporal satellite images.
By applying the proposed multi-level registration
method, the number of labels used in each level is
greatly reduced due to lower image resolution being
used in coarser levels. The results demonstrate that
the lineament detection for better accuracy compared to
traditional sources of lineament identification
methods. It has provided better geotectonic
understanding of Cudappah rock in Ahobhilam with
Quartzite. The imprints of Eastern Ghat orogeny are
seen in upper stream section through a graph cut based
segmentation approach.",
acknowledgement = ack-nhfb,
articleno = "1950002",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nnolim:2019:FFD,
author = "Uche A. Nnolim",
title = "Formulation of Fractional Derivative-Based De-Hazing
Algorithm and Implementation on Mobile-Embedded
Devices",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "1",
pages = "??--??",
month = jan,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500037",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 14 06:31:30 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500037",
abstract = "This paper presents the modification of a previously
developed algorithm using fractional order calculus and
its implementation on mobile-embedded devices such as
smartphones. The system performs enhancement on three
categories of images such as those exhibiting uneven
illumination, faded features/colors and hazy
appearance. The key contributions include the
simplified scheme for illumination correction, contrast
enhancement and de-hazing using fractional
derivative-based spatial filter kernels. These are
achieved without resorting to logarithmic image
processing, histogram-based statistics and complex
de-hazing techniques employed by conventional
algorithms. The simplified structure enables ease of
implementation of the algorithm on mobile devices as an
image processing application. Results indicate that the
fractional order version of the algorithm yields good
results relative to the integer order version and other
algorithms from the literature.",
acknowledgement = ack-nhfb,
articleno = "1950003",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Biswas:2019:CPM,
author = "Biswajit Biswas and Biplab Kanti Sen",
title = "Color {PET-MRI} Medical Image Fusion Combining
Matching Regional Spectrum in Shearlet Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "1",
pages = "??--??",
month = jan,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500049",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 14 06:31:30 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500049",
abstract = "The color PET-MRI medical image fusion is a growing
research area in medical image processing domain. MRI
imagery provides the picture of the anatomy of brain
tissues without any functional information, while the
color PET imagery gives the functional information of
brain tissues with low spatial resolution. An ideal
fusion model should maintain both the functional and
spatial information of the images without any spatial
distortion or color deformation. In this work, we
present a novel fusion technique for color PET-MRI
medical images using Two-Dimensional Discrete Fourier
(2DFT)-Karhunen--Loeve transform (KLT) and singular
value decomposition (SVD) in shearlet domain. This
method decomposes the source images into multi-scaled
and multi-directional sub-bands by shearlet transform
(ST). Then, SVD is utilized to eliminate superfluous ST
coefficients; later, the 2DFT and KLT are utilized to
estimate optimal low-pass ST coefficients in each of
the decomposed images. Later, we combine the largest
low-pass ST coefficients using a novel fusion strategy.
The process of decomposing the source image has been
discussed in detail. Finally, we use the inverse
shearlet transformation (IST) to obtain the fused
image. Experimental results establish the excellence of
our proposed method in terms of quantitative and
qualitative evaluation criteria compared to other
state-of-the-art techniques.",
acknowledgement = ack-nhfb,
articleno = "1950004",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Baig:2019:RTF,
author = "M. Amir Baig and Athar A. Moinuddin and Ekram Khan",
title = "Real-Time Fidelity Measurement of {JPEG2000} Coded
Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "1",
pages = "??--??",
month = jan,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500050",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 14 06:31:30 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500050",
abstract = "The progressive nature of the JPEG2000 coded bitstream
allows the reconstruction of images of different
qualities from a single coded bitstream. This feature
is utilized in this work to estimate the
mean-squared-error (MSE) of reconstructed images
without requiring the original image. It is based on
the fact that if the MSE between the original image and
a lower quality image is known, the MSE for higher
quality images can be estimated from a quality scalable
bitstream. The proposed method is highly accurate and
is very simple as no complex statistical modeling is
needed. Therefore, it is suitable to measure the
fidelity of JPEG2000 decoded images at any desired
quality in a real-time scenario.",
acknowledgement = ack-nhfb,
articleno = "1950005",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Halder:2019:SSP,
author = "Amiya Halder and Sayan Halder and Samrat Chakraborty
and Apurba Sarkar",
title = "A Statistical Salt-and-Pepper Noise Removal
Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "1",
pages = "??--??",
month = jan,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500062",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 14 06:31:30 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500062",
abstract = "This paper proposes a novel approach to remove
salt-and-pepper noise from a given noisy image. The
proposed algorithm is based on statistical quantities
such as mean and standard deviation. It determines the
intensity to be placed on the impulse point by
calculating the eligibility of the nearby points in a
very simple way. This method works iteratively and
removes all the impulse points restoring the edges and
minute details. The proposed algorithm is very
efficient and gives better results than various
existing algorithms. The performance of the proposed
method are compared with other existing methods with
images of noise density as high as 99\% and is found to
perform better.",
acknowledgement = ack-nhfb,
articleno = "1950006",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Oliveira:2019:NTB,
author = "Walter Alexandre A. de Oliveira and Denise Guliato and
Douglas {Coelho Braga de Oliveira} and Rodrigo Luis de
{Souza da Silva} and Gilson Antonio Giraldi",
title = "New Technique for Binary Morphological Shape-Based
Interpolation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "2",
pages = "??--??",
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467819500074",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 10 09:47:18 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500074",
abstract = "In this paper we consider shape-based methods to
generate additional slices in 3D binary volumes. The
focused interpolation approaches, named SIMOL and BORS,
are based on morphological and logical operators. Given
two adjacent slices S1 and S2 of the binary image set,
the methods iteratively generate a sequence of new
slices showing a gradual transition between the
corresponding shapes. First, we analyze the SIMOL and
BORS techniques and highlight their problems. Then we
present the main contribution of this paper: a new
interpolation scheme, called SIMOL-NEW, that combines
the iterative scheme of BORS and an interpolation
kernel generated through SIMOL framework. Next, we
compare SIMOL-NEW and BORS approaches using theoretical
elements and computational experiments. The latter are
executed using: (a) benchmark shapes; (b) simple
volumes defined by sphere and paraboloid; (c)
combination of ellipsoids; (d) a fork-like volume; (e)
Cylinder Minus Sphere. The conclusion is that SIMOL-NEW
performs closer to BORS for the cases (a) and (c) but
it is more accurate than BORS in the tests (b) and (d).
Besides, we offer comparisons of state-of-the-art
approaches in shape-based interpolation and SIMOL-NEW
using ground truth volumes (d) and (e). The
computational experiment report that SIMOL-NEW gets
outstanding results regarding the ability to recover
the target volume.",
acknowledgement = ack-nhfb,
articleno = "1950007",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dixit:2019:FBD,
author = "Umesh D. Dixit and M. S. Shirdhonkar",
title = "Fingerprint-Based Document Image Retrieval",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "2",
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500086",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 10 09:47:18 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500086",
abstract = "Most of the documents use fingerprint impression for
authentication. Property related documents, bank
checks, application forms, etc., are the examples of
such documents. Fingerprint-based document image
retrieval system aims to provide a solution for
searching and browsing of such digitized documents. The
major challenges in implementing fingerprint-based
document image retrieval are an efficient method for
fingerprint detection and an effective feature
extraction method. In this work, we propose a method
for automatic detection of a fingerprint from given
query document image employing Discrete Wavelet
Transform (DWT)-based features and SVM classifier. In
this paper, we also propose and investigate two feature
extraction schemes, DWT and Stationary Wavelet
Transform (SWT)-based Local Binary Pattern (LBP)
features for fingerprint-based document image
retrieval. The standardized Euclidean distance is
employed for matching and ranking of the documents.
Proposed method is tested on a database of 1200
document images and is also compared with current
state-of-art. The proposed scheme provided 98.87\% of
detection accuracy and 73.08\% of Mean Average Precision
(MAP) for document image retrieval.",
acknowledgement = ack-nhfb,
articleno = "1950008",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{BinMortuza:2019:KCB,
author = "Fahad {Bin Mortuza}",
title = "Kernel-Coefficient-Based Feature Method for Face
Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "2",
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500098",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 10 09:47:18 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500098",
abstract = "A kernel-coefficient-based feature method is proposed
to detect faces. The proposed method uses a
mathematical expression and 26 different arrangements
of kernel-coefficients of a kernel (testing region).
The method manipulates the symmetric appearance of a
face with respect to a rigid-kernel (fixed region). The
expression, which is used to generate feature values,
responds to pixels on edges of the image-objects only.
For each distinct arrangement of kernel-coefficients, a
feature-value is generated. The objective of the
proposed kernel-coefficient-based feature method is to
reduce the number of feature values required for face
detection.",
acknowledgement = ack-nhfb,
articleno = "1950009",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Guillen-Reyes:2019:BLD,
author = "Fernando O. Guill{\'e}n-Reyes and Francisco J.
Dom{\'\i}nguez-Mota",
title = "Boundary Layer Detection Techniques Applied to Edge
Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "2",
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500104",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 10 09:47:18 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500104",
abstract = "In this paper, we describe a novel algorithm for edge
detection on a digital image, which is based locally on
the directional averaged gradient properties of the
intensity function, and produces very satisfactory
results in high-resolution digital images in low
execution time. Several examples show results which are
comparable to those obtained by Canny and Sobel
methods.",
acknowledgement = ack-nhfb,
articleno = "1950010",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Han:2019:RBV,
author = "Myounghee Han and Yongjoo Kim and Jang Ryul Park and
Benjamin J. Vakoc and Wang-Yuhl Oh and Sukyoung Ryu",
title = "Retinal Blood Vessel Caliber Estimation for Optical
Coherence Tomography Angiography Images Based on {$3$D}
Superellipsoid Modeling",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "2",
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500116",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 10 09:47:18 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500116",
abstract = "Changes of retinal blood vessel calibers may reflect
various retinal diseases and even several non-retinal
diseases. We propose a new method to estimate retinal
vessel calibers from 3D optical coherence tomography
angiography (OCTA) images based on 3D modeling using
superellipsoids. Taking advantage of 3D visualization
of the retinal tissue microstructures in vivo provided
by OCTA, our method can detect retinal blood vessels
precisely, estimate their calibers reliably, and show
the relative flow speed visually.",
acknowledgement = ack-nhfb,
articleno = "1950011",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Raja:2019:LPC,
author = "S. P. Raja",
title = "Line and Polygon Clipping Techniques on Natural Images
--- A Mathematical Solution and Performance
Evaluation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "2",
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500128",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 10 09:47:18 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500128",
abstract = "The objective of this paper is to apply clipping
techniques on natural images and to analyze the
performance of various clipping algorithms in computer
graphics. The clipping techniques used in this paper is
Cohen--Sutherland line clipping, Liang--Barsky line
clipping, Nicholl--Lee--Nicholl line clipping and
Sutherland--Hodgman polygon clipping. The clipping
algorithms are evaluated by using the three parameters:
time complexity, space complexity and image accuracy.
Previously, there is no performance evaluation on
clipping algorithms done. Motivating by this factor, in
this paper an evaluation of clipping algorithms is
made. The novelty of this paper is to apply the
clipping algorithms on natural images. It is justified
that the above mentioned clipping algorithms outperform
well on clipping the natural images.",
acknowledgement = ack-nhfb,
articleno = "1950012",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gill:2019:RES,
author = "Jasmeen Gill and Akshay Girdhar and Tejwant Singh",
title = "A Review of Enhancement and Segmentation Techniques
for Digital Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "3",
pages = "??--??",
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946781950013X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 23 06:58:38 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946781950013X",
abstract = "Image enhancement and segmentation are the two
imperative steps while processing digital images. The
goal of enhancement is to improve the quality of images
so as to nullify the effect of poor illumination
conditions during image acquisition. Afterwards,
segmentation is performed to extract region of interest
(ROI) from the background details of the image. There
is a vast literature available for both the techniques.
Therefore, this paper is intended to summarize the
basic as well as advanced enhancement and segmentation
techniques under a single heading; to provide an
insight for future researches in the field of pattern
recognition.",
acknowledgement = ack-nhfb,
articleno = "1950013",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2019:IFB,
author = "Manoj Kumar and Anuj Rani and Sangeet Srivastava",
title = "Image Forensics Based on Lighting Estimation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "3",
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500141",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 23 06:58:38 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500141",
abstract = "Computer generated images are assumed to be a key part
in each person's life in this era of information
technology, where individuals effectively inhabit the
advertisements, magazines, websites, televisions and
many more. At the point when digital images played
their role, the event of violations in terms of
misrepresentation of information, use of their wrong
doings winds up and also becomes easier with the help
of image editing application programs. To be
legitimate, if anyone does wrong anything then the
proposed method can be used for a correct
identification of the forgery and the imitations in the
digital images. In existing techniques, researchers
have suggested most well-known types of digital
photographic manipulations based on source, meta-data,
image copying, splicing and many more. The proposed
approach is inspired by physics-based techniques and
requires less human involvement. The presented approach
works for images having any type of objects present in
the scene, i.e. not only limited to human faces and
selection of same intensity regions of the image. By
assessing the lighting parameters, the proposed
technique identifies the manipulated object and returns
angle of incidence w.r.t light source direction. The
demonstrated result produces forgery recognition rate
of 92\% on an image dataset comprising of various types
of manipulated images.",
acknowledgement = ack-nhfb,
articleno = "1950014",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Aditya:2019:ISF,
author = "B. P. Aditya and U. G. K. Avaneesh and K. Adithya and
Akshay Murthy and R. Sandeep and B. Kavyashree",
title = "Invisible Semi-Fragile Watermarking and Steganography
of Digital Videos for Content Authentication and Data
Hiding",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "3",
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500153",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 23 06:58:38 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500153",
abstract = "In the current digital age, the piracy of digital
media content has become rampant. Illegal distribution
of movies and video clips on a global scale causes a
significant loss to the media industry. To prevent such
theft and distribution of content, we use a
watermarking technique for videos where copyright
information is hidden inside the original video in the
form of a watermark video. Using a video as the
watermark facilitates the user in hiding a large amount
of information. The watermarking scheme used in this
paper is semi-fragile, such that tampering of videos
can be detected with relative ease. To improve the
robustness of the watermark, we embed the watermark in
frequency domain, where we use DWT+DCT+SVD to embed the
watermark. The original video and watermark video are
transformed by using the DWT and DCT sequentially, then
the singular values of the watermark with some
embedding strength are added to the singular values of
the original video thus obtaining a watermarked video.
Some detection tools which are available today cannot
detect the watermark video inside the original video.
This method equalizes the frames of the watermark and
original video to reduce time consumed as well as
complexity. The effects of various attacks on the
watermarked video have been analyzed using the
calculated PSNR values.",
acknowledgement = ack-nhfb,
articleno = "1950015",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Miyazaki:2019:EVD,
author = "Daisuke Miyazaki and Sayaka Taomoto and Shinsaku
Hiura",
title = "Extending the Visibility of Dichromats Using Histogram
Equalization of Hue Value Defined for Dichromats",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "3",
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500165",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 23 06:58:38 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500165",
abstract = "Dichromats lack one of the three cone cells, which
detects red, green, and blue lights. For example,
red-green color blinds cannot distinguish the color
between red, yellow, and green. In order to extend the
ability of dichromats to recognize the color
difference, we proposed a method to expand the color
difference when observed by dichromats. We have defined
a hue variable for dichromats and implemented to our
algorithm. We applied the histogram equalization to the
hue of dichromats in order to enlarge the color
difference recognized by dichromats. We have applied
our method to RGB color image, and shown its
performance at the experimental section.",
acknowledgement = ack-nhfb,
articleno = "1950016",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Cheng:2019:LDC,
author = "Lu Cheng and Yuan-Ke Zhang and Yun Song and Chen Li
and Dao-Shun Guo",
title = "Low-Dose {CT} Image Restoration Based on Adaptive
Prior Feature Matching and Nonlocal Means",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "3",
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500177",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 23 06:58:38 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500177",
abstract = "Although the low-dose CT (LDCT) technique can reduce
the radiation damage to patients, it will be highly
detrimental to the reconstructed image quality. The
normal-dose scan assisted algorithms have shown their
potential in improving LDCT image quality by using a
registered previously scanned normal-dose CT (NDCT)
reference to regularize the corresponding LDCT target.
The major drawback of such methods is the requirement
of a previous patient-specific NDCT scan, which limits
their clinical application. To address these problems,
this paper proposed adaptive prior feature matching
method for better restoration of the LDCT image. The
innovation lies in construction of offline texture
feature database and online adaptive prior feature
matching integrated with the NLM regularization.
Specifically, the prior features were extracted by the
gray level co-occurrence matrix (GLCM) from regions of
interest (ROIs) in existing NDCT scans of population
patients. For online adaptive prior feature matching,
ROIs with their texture features being similar to those
of the current noisy target ROI are selected from the
database as the references for the NLM regularization.
The effectiveness of the proposed algorithm is
validated by clinical lung cancer studies, the gain
over traditional methods is noticeable in terms of both
noise suppression and textures preservation.",
acknowledgement = ack-nhfb,
articleno = "1950017",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Veinidis:2019:EDH,
author = "Christos Veinidis and Antonios Danelakis and Ioannis
Pratikakis and Theoharis Theoharis",
title = "Effective Descriptors for Human Action Retrieval from
{$3$D} Mesh Sequences",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "3",
pages = "??--??",
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500189",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Jul 23 06:58:38 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500189",
abstract = "Two novel methods for fully unsupervised human action
retrieval using 3D mesh sequences are presented. The
first achieves high accuracy but is suitable for
sequences consisting of clean meshes, such as
artificial sequences or highly post-processed real
sequences, while the second one is robust and suitable
for noisy meshes, such as those that often result from
unprocessed scanning or 3D surface reconstruction
errors. The first method uses a spatio-temporal
descriptor based on the trajectories of 6 salient
points of the human body (i.e. the centroid, the top of
the head and the ends of the two upper and two lower
limbs) from which a set of kinematic features are
extracted. The resulting features are transformed using
the wavelet transformation in different scales and a
set of statistics are used to obtain the descriptor. An
important characteristic of this descriptor is that its
length is constant independent of the number of frames
in the sequence. The second descriptor consists of two
complementary sub-descriptors, one based on the
trajectory of the centroid of the human body across
frames and the other based on the Hybrid static shape
descriptor adapted for mesh sequences. The robustness
of the second descriptor derives from the robustness
involved in extracting the centroid and the Hybrid
sub-descriptors. Performance figures on publicly
available real and artificial datasets demonstrate our
accuracy and robustness claims and in most cases the
results outperform the state-of-the-art.",
acknowledgement = ack-nhfb,
articleno = "1950018",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hamouda:2019:FAS,
author = "Maissa Hamouda and Karim Saheb Ettabaa and Med Salim
Bouhlel",
title = "Framework for Automatic Selection of Kernels based on
Convolutional Neural Networks and {CkMeans} Clustering
Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "4",
pages = "??--??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467819500190",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Feb 1 09:16:38 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500190",
abstract = "Convolutional neural networks (CNN) can learn deep
feature representation for hyperspectral imagery (HSI)
interpretation and attain excellent accuracy of
classification if we have many training samples. Due to
its superiority in feature representation, several
works focus on it, among which a reliable
classification approach based on CNN, used filters
generated from cluster framework, like k Means
algorithm, yielded good results. However, the kernels
number to be manually assigned. To solve this problem,
a HSI classification framework based on CNN, where the
convolutional filters to be adaptatively learned from
the data, by grouping without knowing the cluster
number, has recently proposed. This framework, based on
the two algorithms CNN and kMeans, showed high accuracy
results. So, in the same context, we propose an
architecture based on the depth convolutional neural
networks principle, where kernels are adaptatively
learned, using CkMeans network, to generate filters
without knowing the number of clusters, for
hyperspectral classification. With adaptive kernels,
the proposed framework automatic kernels selection by
CkMeans algorithm (AKSCCk) achieves a better
classification accuracy compared to the previous
frameworks. The experimental results show the
effectiveness and feasibility of AKSCCk approach.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Montazeri:2019:MAI,
author = "Mitra Montazeri",
title = "Memetic Algorithm Image Enhancement for Preserving
Mean Brightness Without Losing Image Features",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "4",
pages = "??--??",
month = oct,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500207",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Feb 1 09:16:38 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500207",
abstract = "In the image processing application, contrast
enhancement is a major step. Conventional contrast
enhancement methods such as Histogram Equalization (HE)
do not have satisfactory results on many different low
contrast images and they also cannot automatically
handle different images. These problems result in
specifying parameters manually to produce high contrast
images. In this paper, an automatic image contrast
enhancement on Memetic algorithm (MA) is proposed. In
this study, simple exploiter is proposed to improve the
current image contrast. The proposed method
accomplishes multi goals of preserving brightness,
retaining the shape features of the original histogram
and controlling excessive enhancement rate, suiting for
applications of consumer electronics. Simulation
results shows that in terms of visual assessment, peak
signal-to-noise (PSNR) and Absolute Mean Brightness
Error (AMBE) the proposed method is better than the
literature methods. It improves natural looking images
specifically in images with high dynamic range and the
output images were applicable for products of consumer
electronic.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nnolim:2019:ISI,
author = "Uche A. Nnolim",
title = "Improved Single Image De-Hazing Via Sky Region
Detection, Classification and Illumination Refinement",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "4",
pages = "??--??",
month = oct,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500219",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Feb 1 09:16:38 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500219",
abstract = "This paper presents an automated sky detection
technique based on statistical and fuzzy rule-based
edge detection for improved hazy image contrast
enhancement. This is significant since most
conventional de-hazing approaches yield hazy images
with over-enhanced sky regions and under-enhanced
detail regions due to inability to adaptively determine
and enhance such regions. Earlier and current schemes
developed to remedy this issue are highly complex,
usually require training with vast amount of images and
manual tuning of one or several parameters. The
proposed method utilizes standard deviation and fuzzy
logic-based edge detection combined with thresholding
algorithms to generate a homogeneity map identifying
sky and non-sky regions. The areas of these regions are
subsequently computed and used to obtain a homogeneity
ratio. The ratio is then used to trigger a
decision-based, switching scheme incorporated into a
partial differential equation (PDE) de-hazing algorithm
to improve results. Alternatively, a log illumination
refinement method is proposed as a less complex
alternative combined with the modified PDE algorithm to
process hazy images without degrading sky regions,
while yielding brighter images. Several image datasets
from the literature were used to validate the proposed
approaches and yielded mostly consistent and comparable
results similar to or better than algorithms from the
literature.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Khadilkar:2019:FIB,
author = "Samrat P. Khadilkar and Sunil R. Das and Mansour H.
Assaf and Satyendra N. Biswas",
title = "Face Identification Based on Discrete Wavelet
Transform and Neural Networks",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "4",
pages = "??--??",
month = oct,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500220",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Feb 1 09:16:38 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500220",
abstract = "The subject paper presents implementation of a new
automatic face recognition system. To formulate an
automated framework for the recognition of human faces
is a highly challenging endeavor. The face
identification problem is particularly very crucial in
the context of today's rapid emergence of technological
advancements with ever expansive requirements. It has
also significant relevance in the related engineering
disciplines of computer graphics, pattern recognition,
psychology, image processing and artificial neural
networks. This paper proposes a side-view face
authentication approach based on discrete wavelet
transform and artificial neural networks for the
solution of the problem. A subset determination
strategy that expands on the number of training samples
and permits protection of the global information is
discussed. The authentication technique involves image
profile extraction, decomposition of the wavelets,
splitting of the subsets and finally neural network
verification. The procedure exploits the localization
property of the wavelets in both the frequency and
spatial domains, while maintaining the generalized
properties of the neural networks. The realization
strategy of the methodology was executed using MATLAB,
demonstrating that the performance of the technique is
quite satisfactory.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mashaly:2019:PAS,
author = "Ahmed S. Mashaly",
title = "Performance Assessment of Sky Segmentation Approaches
for {UAVs}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "4",
pages = "??--??",
month = oct,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500232",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Feb 1 09:16:38 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500232",
abstract = "Image segmentation is one of the most challenging
research fields for both image analysis and
interpretation. The applications of image segmentation
could be found as the primary step in various computer
vision systems. Therefore, the choice of a reliable and
accurate segmentation method represents a non-trivial
task. Since the selected image segmentation method
influences the overall performance of the remaining
system steps, sky segmentation appears as a vital step
for Unmanned Aerial Vehicle (UAV) autonomous obstacle
avoidance missions. In this paper, we are going to
introduce a comprehensive literature survey of the
different types of image segmentation methodology
followed by a detailed illustration of the
general-purpose methods and the state-of-art sky
segmentation approaches. In addition, we introduce an
improved version of our previously published work for
sky segmentation purpose. The performance of the
proposed sky segmentation approach is compared with
various image segmentation approaches using different
parameters and datasets. For performance assessment, we
test our approach under different situations and
compare its performance with commonly used approaches
in terms of several assessment indexes. From the
experimental results, the proposed method gives
promising results compared with the other image
segmentation approaches.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Suryanarayana:2019:SIS,
author = "Gunnam Suryanarayana and Ravindra Dhuli and Jie Yang",
title = "Single Image Super-Resolution Algorithm Possessing
Edge and Contrast Preservation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "4",
pages = "??--??",
month = oct,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819500244",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Feb 1 09:16:38 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819500244",
abstract = "In real time surveillance video applications, it is
often required to identify a region of interest in a
degraded low resolution (LR) image. State-of-the-art
super-resolution (SR) techniques produce images with
poor illumination and degraded high frequency details.
In this paper, we present a different approach for SISR
by correcting the dual-tree complex wavelet transform
(DT-CWT) subbands using the multi-stage cascaded joint
bilateral filter (MSCJBF) and singular value
decomposition (SVD). The proposed method exploits
geometric regularity for implementing the
covariance-based interpolation in the spatial domain.
We decompose the interpolated LR image into different
image and wavelet coefficients by employing DT-CWT. To
preserve edges, we alter the wavelet sub-bands with the
high frequency details obtained from the MSCJBF.
Simultaneously, we retain uniform illumination by
improving the image coefficients using SVD. In
addition, the wavelet sub-bands undergo Lanczos
interpolation prior to the subband refinement.
Experimental results demonstrate the effectiveness of
our method.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2019:AIV,
author = "Anonymous",
title = "Author Index (Volume 19)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "19",
number = "4",
pages = "??--??",
month = oct,
year = "2019",
DOI = "https://doi.org/10.1142/S0219467819990018",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Feb 1 09:16:38 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467819990018",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nandal:2020:FOA,
author = "Savita Nandal and Sanjeev Kumar",
title = "Fractional-Order Anisotropic Diffusion for Defogging
of {RGB} Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "01",
pages = "??--??",
month = jan,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467820500011",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 6 07:43:16 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500011",
abstract = "This paper proposes a novel and efficient algorithm
for defogging of color (RGB) images. The fog in a scene
is mostly due to the attenuation and airlight map,
which decrease the quality of the image of the scene.
To enhance such images from the visual point of view, a
fractional-order anisotropic diffusion algorithm with p
-Laplace norm is proposed for removing the fog effect.
In particular, a coupling term is added in order to
model the inter-channel correlations. The weights used
in the coupling term stop the transmission of diffusion
with in the edges, thus balances the inter-channel data
in the diffusion procedure. Experimental results
validate the better performance of the proposed
algorithm over some of the existing anisotropic
diffusion-based methods. The proposed method is
independent of the measure of fog in the images, thus
images with different amount of fog can be enhanced.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yadav:2020:EIA,
author = "Navneet Yadav and Navdeep Goel",
title = "An Effective Image-Adaptive Hybrid Watermarking Scheme
with Transform Coefficients",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "01",
pages = "??--??",
month = jan,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500023",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 6 07:43:16 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500023",
abstract = "Robust and invisible watermarking provides a feasible
solution to prove the ownership of the genuine content
owner. Different watermarking algorithms have been
presented by the researchers in the past but no
algorithm could be termed as perfect. Proposed work
puts forward a novel image-adaptive method of embedding
a binary watermark in the image in a transparent
manner. Discrete wavelet transform (DWT), singular
value decomposition (SVD) and discrete cosine transform
(DCT) are used together in the proposed hybrid
watermarking scheme. Image-adaptive nature of the
scheme is reflected in the usage of only high entropy
8{\texttimes}8 blocks for the watermark embedding.
Binary watermark is embedded in the DCT coefficients
using a flexible strength derived from the means of the
DCT coefficients. This flexible strength factor (SF)
has different value for the DCT coefficients originated
from different 8{\texttimes}8 blocks. Any desired level
of visual quality could be obtained by varying the
adjusting parameter of the flexible SF. Side
information generated in the watermark embedding is
used in the detection of watermark. The presented
watermarking technique shows better robustness in
comparison to the three contemporary watermarking
techniques.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ye:2020:MFM,
author = "Dan Ye and Chiou-Shann Fuh",
title = "{$3$D} Morphable Face Model for Face Animation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "01",
pages = "??--??",
month = jan,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500035",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 6 07:43:16 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500035",
abstract = "This paper employs a new technology for modeling
textured 3D faces. 3D faces can either be generated
automatically from one or more photographs, or modeled
directly through an intuitive user interface. Users are
assisted in two key problems of computer-aided face
modeling. It presents two algorithms for 3D face
modeling from an image sequence. The first method works
by creating an initial estimate using multiframe
structure from motion (SfM) reconstruction framework,
which is refined by comparing against a generic face
model. The comparison is carried out using an
energy-function optimization strategy. Results of 3D
reconstruction algorithm are presented. The second
method presented reconstructs a face model by adapting
a generic model to contours of a face over all the
frames of an image sequence. The algorithm for pose
estimation and 3D face reconstruction relies solely on
contours and the system does not require knowledge of
rendering parameters (e.g. light direction and
intensity). Results relying on finding accurate point
correspondences across frames is presented.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sakib:2020:RDB,
author = "Mohammad Nazmus Sakib and Shuvashis Das Gupta and
Satyendra N. Biswas",
title = "A Robust {DWT}-Based Compressed Domain Video
Watermarking Technique",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "01",
pages = "??--??",
month = jan,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500047",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 6 07:43:16 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500047",
abstract = "To achieve robustness and imperceptibility, an
adaptive compressed domain blind video watermarking
method based on Discrete Wavelet Transform (DWT) is
proposed in this research. In this technique, multiple
binary images derived from a single watermark image are
first embedded in a video sequence. The spatial spread
spectrum watermark is directly incorporated in the
compressed bit streams by modifying the four sets of
discrete wavelet coefficients. Comprehensive simulation
experiments demonstrate that the developed approach is
efficient and also robust against spatial attacks such
as scaling and frame averaging, noise attacks such as
Gaussian and salt pepper noise, and temporal attacks
like frame dropping and shifting. Moreover, the
proposed approach can also withstand against rotation
attacks of arbitrary angle.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bibi:2020:SPP,
author = "Khalida Bibi and Ghazala Akram and Kashif Rehan",
title = "Shape Preserving Properties with Constraints on the
Tension Parameter of Binary Three-Point Approximating
Subdivision Scheme",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "01",
pages = "??--??",
month = jan,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500059",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 6 07:43:16 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500059",
abstract = "The paper analyzes conditions for preserving the shape
properties from the initial data to the limit curves of
the binary three-point approximating subdivision
scheme. We provide suitable conditions on the initial
data utilizing the tension parameter $ \omega $, thus
the scheme can maintain three important shape
properties, namely positivity, monotonicity and
convexity in the limit curves. The use of derived
conditions is illustrated in few examples, which offers
more flexibility in the generation of smooth limit
curves endowed with shape preserving properties.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Arora:2020:GFB,
author = "Tanvi Arora and Renu Dhir",
title = "Geometric Feature-Based Classification of Segmented
Human Chromosomes",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "01",
pages = "??--??",
month = jan,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500060",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Feb 6 07:43:16 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500060",
abstract = "The chromosomes are the carriers of the geometric
information, any alteration in the structure or number
of these chromosomes is termed as genetic defect. These
alterations cause malfunctioning in the proteins and
are cause of the various underlying medical conditions
that are hard to cure or detect by normal clinical
procedures. In order to detect the underlying causes of
these defects, the cells of the humans need to be
imaged during the mitosis phase of cell division.
During this phase, the chromosomes are the longest and
can be easily studied and the alterations in the
structure and count of the chromosomes can be analyzed
easily. The chromosomes are non-rigid objects, due to
which they appear in varied orientations, which makes
them hard to be analyzed for the detection of
structural defects. In order to detect the genetic
abnormalities due to structural defects, the
chromosomes need to be in straight orientation.
Therefore, in this work, we propose to classify the
segmented chromosomes from the metaspread images into
straight, bent, touching overlapping or noise, so that
the bent, touching, overlapping chromosomes can be
preprocessed and straightened and the noisy objects be
discarded. The classification has been done using a set
of 17 different geometric features. We have proposed a
Multilayer Perceptron-based classification approach to
classify the chromosomes extracted from metaspread
images into five distinct categories considering their
orientation. The results of the classification have
been analyzed using the segmented objects of the
Advance Digital Imaging Research (ADIR) dataset. The
proposed technique is capable of classifying the
segmented chromosomes with 94.28\% accuracy. The
performance of the proposed technique has been compared
with seven other state-of-the-art classifiers and
superior results have been achieved by the proposed
method.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Girishwaingankar:2020:PNB,
author = "Poorva Girishwaingankar and Sangeeta Milind Joshi",
title = "The {PHY-NGSC}-Based {ORT} Run Length Encoding Scheme
for Video Compression",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "02",
pages = "??--??",
month = apr,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467820500072",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 11 09:44:18 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib;
https://www.math.utah.edu/pub/tex/bib/matlab.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500072",
abstract = "This paper proposes a compression algorithm using
octonary repetition tree (ORT) based on run length
encoding (RLE). Generally, RLE is one type of lossless
data compression method which has duplication problem
as a major issue due to the usage of code word or flag.
Hence, ORT is offered instead of using a flag or code
word to overcome this issue. This method gives better
performance by means of compression ratio, i.e.
99.75\%. But, the functioning of ORT is not good in
terms of compression speed. For that reason,
physical-next generation secure computing (PHY-NGSC) is
hybridized with ORT to raise the compression speed. It
uses an MPI-open MP programming paradigm on ORT to
improve the compression speed of encoder. The planned
work achieves multiple levels of parallelism within an
image such as MPI and OpenMP for parallelism across a
group of pictures level and slice level, respectively.
At the same time, wide range of data compression like
multimedia, executive files and documents are possible
in the proposed method. The performance of the proposed
work is compared with other methods like accordian RLE,
context adaptive variable length coding (CAVLC) and
context-based arithmetic coding (CBAC) through the
implementation in Matlab working platform.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Raja:2020:WBI,
author = "S. P. Raja",
title = "Wavelet-Based Image Compression Encoding Techniques
--- A Complete Performance Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "02",
pages = "??--??",
month = apr,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500084",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 11 09:44:18 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500084",
abstract = "This paper presents a complete analysis of
wavelet-based image compression encoding techniques.
The techniques involved in this paper are embedded
zerotree wavelet (EZW), set partitioning in
hierarchical trees (SPIHT), wavelet difference
reduction (WDR), adaptively scanned wavelet difference
reduction (ASWDR), set partitioned embedded block coder
(SPECK), compression with reversible embedded wavelet
(CREW) and spatial orientation tree wavelet (STW).
Experiments are done by varying level of the
decomposition, bits per pixel and compression ratio.
The evaluation is done by taking parameters like peak
signal to noise ratio (PSNR), mean square error (MSE),
image quality index (IQI) and structural similarity
index (SSIM), average difference (AD), normalized
cross-correlation (NK), structural content (SC),
maximum difference (MD), Laplacian mean squared error
(LMSE) and normalized absolute error (NAE).",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Soni:2020:TRE,
author = "Rituraj Soni and Bijendra Kumar and Satish Chand",
title = "Text Region Extraction From Scene Images Using {AGF}
and {MSER}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "02",
pages = "??--??",
month = apr,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500096",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 11 09:44:18 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500096",
abstract = "The natural scene images contain text as an integral
part of that image that supplies paramount knowledge
about it. This information and knowledge can be used in
the variety of purposes like image-based searching,
automatic number plate recognition, robot navigation,
etc. but text region extraction and detection in
scenery images could be quite a challenging job due to
image blur, distortion, noise, etc. In this paper, we
discuss a method for extraction of text regions by
generating prospective components by applying maximally
stable extremal regions (MSER) and boundary smoothing
by Alternating guided image filter, which is one of the
newest filters to deal with noise and halo effect
elimination. The separation of non-text \& text
components is achieved by AdaBoost classifier that
separates text and non-text on the basis of the three
text specific features namely maximum stroke width
ratio, compactness, color divergence. The proposed
method assist in extracting text regions from the
blurred and low contrast natural scene images
effectively. The ICDAR 2013 training and testing
dataset is applied for the experiments and evaluation
of the method. The evaluation is carried out using
deteval software for calculating precision, f-measure,
recall for the detected, and extracted text regions.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nnolim:2020:PMS,
author = "Uche A. Nnolim",
title = "Probabilistic, Multi-Scale Fractional Tonal Correction
Bilateral Filter-Based Hazy Image Enhancement",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "02",
pages = "??--??",
month = apr,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500102",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 11 09:44:18 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500102",
abstract = "This paper describes an algorithm utilizing a modified
multi-scale fractional order-based operator combined
with a probabilistic tonal operator, adaptive color
enhancement and bilateral filtering to process hazy and
underwater images. The multi-scale algorithm
complements the tonal operator by enhancing edges,
preventing overexposure of bright image regions, while
enhancing details in the dark areas. The addition of a
previously developed global enhancement operator
removes color cast and improves global contrast in
underwater images. The color enhancement function
augments the color results of the dehazing algorithm
without distorting image intensity. Furthermore, the
bilateral filter suppresses noise while preserving
enhanced details/edges due to the multi-scale
algorithm. Experimental results indicate that the
proposed system yields comparable or better results
than other algorithms from the literature.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Surajkanta:2020:RIA,
author = "Yumnam Surajkanta and Shyamosree Pal",
title = "Recognition of Isothetic Arc Using Number Theoretic
Properties",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "02",
pages = "??--??",
month = apr,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500114",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 11 09:44:18 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500114",
abstract = "In this paper, we propose an arc recognition method
based on the number theoretic properties of isothetic
covers. A definition of digital circles is given based
on the dilation of Euclidean circles with unit squares.
We show that a variant of the digital circle is
equivalent to the grid centers between the isothetic
covers of disks. Number theoretic properties of the
isothetic covers of disks are explored and show that
the distribution of square numbers can be used to find
the run lengths of the isothetic covers. Arc
recognition algorithms are developed based on the
number theoretic properties.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Fuchsberger:2020:SAT,
author = "Alexander Fuchsberger and Brian Ricks and Zhicheng
Chen",
title = "A Semi-Automated Technique for Transcribing Accurate
Crowd Motions",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "02",
pages = "??--??",
month = apr,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500126",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 11 09:44:18 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500126",
abstract = "We present a novel technique for transcribing crowds
in video scenes that allows extracting the positions of
moving objects in video frames. The technique can be
used as a more precise alternative to image processing
methods, such as background-removal or automated
pedestrian detection based on feature extraction and
classification. By manually projecting pedestrian
actors on a two-dimensional plane and translating
screen coordinates to absolute real-world positions
using the cross ratio, we provide highly accurate and
complete results at the cost of increased processing
time. We are able to completely avoid most errors found
in other automated annotation techniques, resulting
from sources such as noise, occlusion, shadows, view
angle or the density of pedestrians. It is further
possible to process scenes that are difficult or
impossible to transcribe by automated image processing
methods, such as low-contrast or low-light
environments. We validate our model by comparing it to
the results of both background-removal and feature
extraction and classification in a variety of scenes.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Omari:2020:CPA,
author = "Mohammed Omari and Yamina Ouled Jaafri and Rekia
Dlim",
title = "Comparative Performance Analysis of Enhancement
Methods Applied to {Arabic} Manuscripts",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "02",
pages = "??--??",
month = apr,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500138",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 11 09:44:18 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500138",
abstract = "The ancient Arabic manuscripts are considered to be
more complex regarding enhancement compared to others
written in other languages. Complexity comes from
character degradation, stains, low-quality images,
curves of the text, character overlapping, etc. To
facilitate the restoration, a set of well-known
binarization techniques designed for historical
document images is presented in this paper. Existing
binarization techniques focus on either finding an
appropriate global threshold for the whole image or
adapting a local threshold for each area to achieve
better enhancement quality. This improvement aims to
remove noises, strains, uneven illumination, etc. The
goal of our work is to assess these methods when
applied to Arabic manuscripts in terms of readability,
elimination of original spots and production of
unwanted noise. Results show that no techniques work
well for all types of manuscripts, but some techniques
work better than others for particular types.
Experimental results also indicate that Nick and Wolf
techniques performed the best in terms of readability
in most of the processed manuscripts.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Arunlal:2020:DIC,
author = "S. L. Arunlal and N. Santhi and K. Ramar",
title = "Design and Implementation of Content-Based Natural
Image Retrieval Approach Using Feature Distance",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "02",
pages = "??--??",
month = apr,
year = "2020",
DOI = "https://doi.org/10.1142/S021946782050014X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 11 09:44:18 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782050014X",
abstract = "Generally, the database is a gathering of data that is
arranged for simple storage, retrieval and modernize.
This data comprises of numerous structures like text,
table, and image, outline and chart and so on.
Content-based image retrieval (CBIR) is valuable for
calculating the huge amount of image databases and
records and for distinguishes retrieving similar
images. Rather than text-based searching, CBIR
effectively recovers images that are similar like query
image. CBIR assumes a significant role in various areas
including restorative finding, industry estimation,
geographical information satellite frameworks (GIS
frameworks), and biometrics; online searching and
authentic research, etc. Here different medical
database images are considered to the CBIR procedure is
done by the proposed strategy. The proposed method
considers the input features are shape, texture
feature, wavelet feature, and SIFT feature. To retrieve
the input image based on the features, the suggested
method utilizes artificial neural network (ANN)
structure. Back-propagation technique, which is an
organizational structure for learning is utilized for
training the neural network framework. Trial
demonstrates that the proposed work improves the
results of the retrieval system. From the outcomes
minimizes the image retrieval time and maximum
Precision 87.3\% in distance based ANN process.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sinduja:2020:EET,
author = "A. Sinduja and A. Suruliandi and S. P. Raja",
title = "Empirical Evaluation of Texture Features and
Classifiers for Liver Disease Diagnosis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "02",
pages = "??--??",
month = apr,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500151",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 11 09:44:18 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500151",
abstract = "The liver cancer is one of the most common fatal
diseases worldwide, and its early detection through
medical imaging is a major contributor to the reduction
in mortality from certain cancer. This paves the way to
work on diagnosing liver diseases effectively. An
accurate diagnosis of liver disease in CT image
requires an efficient description of textures and
classification methods. This paper performs comparative
analysis on proposed texture feature descriptor with
the different existing texture features with various
classifiers to classify six types of diffused and focal
liver diseases. The classification of liver diseases is
done in two stages. In first stage, features like
segmentation based fractal texture analysis, counting
label occurrence matrix, local configuration pattern,
eXtended center-symmetric local binary pattern and the
proposed local symmetric tetra pattern are used for
extracting information from the CT liver structure and
classifiers like support vector machine, k -nearest
neighbor, and naive Bayes are used for classifying the
pathologic liver. When pathologic conditions are
detected, the best feature descriptors and classifiers
are used to classify the results into any of six
exclusive pathologic liver diseases, in second stage.
The experiments are carried out in medically validated
liver datasets containing normal and six-disease
category of liver. The first experiment is analyzed
using sensitivity, specificity, and accuracy. The
second experiment is evaluated using precision, recall,
BCR, and F-measure. The results demonstrate that the
local symmetric tetra pattern with k -nearest neighbor
classifier culminates in a state-of-the-art performance
for diagnosing liver diseases.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2020:FFB,
author = "Sandeep Kumar and L. Suresh",
title = "Fruit Fly-Based Artificial Neural Network Classifier
with Kernel-Based Fuzzy $c$-Means Clustering for
Satellite Image Classification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "02",
pages = "??--??",
month = apr,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500163",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon May 11 09:44:18 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500163",
abstract = "Image segmentation and classification are the major
challenges to satellite imagery. Also, the
identification of unique objects in the satellite image
is a significant aspect in the application of remote
sensing. Many satellite image classification techniques
have been presented earlier. However, the accuracy of
the image classification has to be further improved. So
that, optimal artificial neural network with
kernel-based fuzzy c-means ( KFCM+OANN ) clustering
based satellite image classification is proposed in
this paper. Initially, the images are segmented with
the help of KFCM algorithm. Then, color features and
gray level co-occurrence matrix (GLCM) features to be
extracted from the segmented regions. Then, these
extracted features are given to the OANN classifier.
Based on these features, segmented regions are
classified as building, road, shadow, and tree. To
enhance the performance of the classifier, the weight
values are optimally selected with the help of fruit
fly algorithm. Simulation results show that the
performance of proposed classifier outperforms that of
the existing filters in terms of accuracy.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Divakara:2020:NDI,
author = "S. S. Divakara and Sudarshan Patilkulkarni and Cyril
Prasanna Raj",
title = "Novel {DWT\slash IDWT} Architecture for {$3$D} with
Nine Stage {$2$D} Parallel Processing using Split
Distributed Arithmetic",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "03",
pages = "??--??",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467820500175",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:10 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500175",
abstract = "Novel high-speed memory optimized distributed
arithmetic (DA)-based architecture is developed and
modeled for 3D discrete wavelet transform (DWT). The
memory requirement for the proposed architecture is
designed to 9{\texttimes}9N+36 pixel dynamic memory
space and 52W ROM. The proposed 3D-DWT architecture
implements 9/7 Daubechies wavelet filters, synthesizes
7127 bytes of memory for temporary storage and uses 758
adders, 36 multiplexers of 16:1 and 36 up counter to
realize the 3D-DWT hardware. The 3D-DWT engine is
implemented and tested in a Xilinx FPGA Vertex5
XC5VLX155T with high area and power efficiency. The
maximum delay in the timing path is 2.676 ns and the
3D-DWT works at maximum frequency of 381 MHz clock.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shrivastava:2020:ASR,
author = "Neeraj Shrivastava and Jyoti Bharti",
title = "Automatic Seeded Region Growing Image Segmentation for
Medical Image Segmentation: a Brief Review",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "03",
pages = "??--??",
month = jul,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500187",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:10 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500187",
abstract = "In the domain of computer technology, image processing
strategies have become a part of various applications.
A few broadly used image segmentation methods have been
characterized as seeded region growing (SRG),
edge-based image segmentation, fuzzy k -means image
segmentation, etc. SRG is a quick, strongly formed and
impressive image segmentation algorithm. In this paper,
we delve into different applications of SRG and their
analysis. SRG delivers better results in analysis of
magnetic resonance images, brain image, breast images,
etc. On the other hand, it has some limitations as
well. For example, the seed points have to be selected
manually and this manual selection of seed points at
the time of segmentation brings about wrong selection
of regions. So, a review of some automatic seed
selection methods with their advantages, disadvantages
and applications in different fields has been
presented.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Savakar:2020:ERU,
author = "Dayanand G. Savakar and Ravi Hosur",
title = "The {$3$D} Emotion Recognition Using {SVM} and {HoG}
Features",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "03",
pages = "??--??",
month = jul,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500199",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:10 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500199",
abstract = "Emotion recognition is becoming commercially popular
due to the major role of analytics in various aspects
of marketing and strategy management. Several papers
have been proposed in emotion recognition. They are
mainly classified in the past under 2D and 3D emotion
recognition, out of which 2D emotion recognition has
been more popular. Various aspects like facial posture,
light intensity variations and sensor-independent
recognition have been studied by different authors in
the past. However, in reality, 3D emotion recognition
has been found to be more efficient which has a broader
area of use. In this paper, a 3D tracking plane with 2D
feature points has enabled us to recognize emotions by
statistical voting method from all planes having over
threshold number of points in their respective contour
area. The proposed technique's results are comparable
to existing methods in terms of time, space complexity
and accuracy improvement.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Salehi:2020:UID,
author = "Hadi Salehi and Javad Vahidi",
title = "An Ultrasound Image Despeckling Method Based on
Weighted Adaptive Bilateral Filter",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "03",
pages = "??--??",
month = jul,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500205",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:10 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500205",
abstract = "Images are widely used in engineering. Unfortunately,
ultrasound images are mainly degraded by an intrinsic
noise called speckle. Therefore, de-speckling is a
critical preprocessing step. Therefore, a robust
despeckling method and accurate evaluation of images
are suggested. We suggest three phases and a three-step
denoising filter. In the first phase, the coefficients
of variation are computed from the noisy image. The
second phase is a three-step denoising filter. The
first step is denoising of extreme levels of
homogeneous regions, based on fuzzy homogeneous
regions. The second step is a proposed adaptive
bilateral filter (ABF). The ABF helps for better
denoising based on the three regions which are edge,
detail and homogeneous regions. The next step, a
weight, is applied to the ABF. This step is for
isolated noise denoising. Next, in the third phase, the
output image is evaluated by the fuzzy logic approach.
The proposed method is compared with other filters in
the literature. The experimental outcomes show that the
proposed method has better performance than the other
filters. That proposed denoising algorithm is able to
preserve image details and edges when compared with
other denoising methods.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nikesh:2020:DVB,
author = "P. Nikesh and G. Raju",
title = "Directional Vector-Based Skin Lesion Segmentation ---
a Novel Approach to Skin Segmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "03",
pages = "??--??",
month = jul,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500217",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:10 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500217",
abstract = "Efficient skin lesion segmentation algorithms are
required for computer aided diagnosis of skin cancer.
Several algorithms were proposed for skin lesion
segmentation. The existing algorithms are short of
achieving ideal performance. In this paper, a novel
semi-automatic segmentation algorithm is proposed. The
fare concept of the proposed is 8-directional search
based on threshold for lesion pixel, starting from a
user provided seed point. The proposed approach is
tested on 200 images from PH2 and 900 images from ISBI
2016 datasets. In comparison to a chosen set of
algorithms, the proposed approach gives high accuracy
and specificity values. A significant advantage of the
proposed method is the ability to deal with
discontinuities in the lesion.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Debnath:2020:UAS,
author = "Saswati Debnath and Pinki Roy",
title = "User Authentication System Based on Speech and Cascade
Hybrid Facial Feature",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "03",
pages = "??--??",
month = jul,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500229",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:10 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500229",
abstract = "With the increasing demand for security in many
fastest growing applications, biometric recognition is
the most prominent authentication system. User
authentication through speech and face recognition is
the important biometric technique to enhance the
security. This paper proposes a speech and facial
feature-based multi-modal biometric recognition
technique to improve the authentication of any system.
Mel Frequency Cepstral Coefficients (MFCC) is extracted
from audio as speech features. In visual recognition,
this paper proposes cascade hybrid facial (visual)
feature extraction method based on static, dynamic and
key-point salient features of the face and it proves
that the proposed feature extraction method is more
efficient than the existing method. In this proposed
method, Viola--Jones algorithm is used to detect static
and dynamic features of eye, nose, lip, Scale Invariant
Feature Transform (SIFT) algorithm is used to detect
some stable key-point features of face. In this paper,
a research on the audio-visual integration method using
AND logic is also made. Furthermore, all the
experiments are carried out using Artificial Neural
Network (ANN) and Support Vector Machine (SVM). An
accuracy of 94.90\% is achieved using proposed feature
extraction method. The main objective of this work is
to improve the authenticity of any application using
multi-modal biometric features. Adding facial features
to the speech recognition improve system security
because biometric features are unique and combining
evidence from two modalities increases the authenticity
as well as integrity of the system.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Prashar:2020:NCA,
author = "Navdeep Prashar and Meenakshi Sood and Shruti Jain",
title = "Novel Cardiac Arrhythmia Processing using Machine
Learning Techniques",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "03",
pages = "??--??",
month = jul,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500230",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:10 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500230",
abstract = "Electrocardiography (ECG) signals provides assistance
to the cardiologists for identification of various
cardiovascular diseases (CVD). ECG machine records the
electrical activity of the heart with the assistance of
electrodes placed on the patient's body. Qualitative
characterization of ECG signal reflects its
sensitiveness towards distinct artifacts that resulted
in low diagnostic accuracy and may lead to incorrect
decision of the clinician. The artifacts are removed
utilizing a robust noise estimator employing DTCWT
using various threshold values and functions. The
segments and intervals of ECG signals are calculated
using the peak detection algorithm followed by particle
swarm optimization (PSO) and the proposed optimization
technique to select the best features from a
considerable pool of features. Out of the 12 features,
the best four features are selected using PSO and the
proposed optimization technique. Comparative analysis
with other feature selection methods and
state-of-the-art techniques demonstrated that the
proposed algorithm precisely selects principle features
for handling the ECG signal and attains better
classification utilizing distinctive machine learning
algorithms. The obtained accuracy using our proposed
optimization technique is 95.71\% employing k -NN and
neural networks. Also, 4\% and 10\% improvements have
been observed while using k -NN over ANN and SVM,
respectively, when the PSO technique is executed.
Similarly, a 14.16\% improvement is achieved while
using k -NN and ANN over the SVM machine learning
technique for the proposed optimization technique.
Heart rate is calculated using the proposed estimator
and optimization technique, which is in consensus with
the gold standard.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jiji:2020:DST,
author = "G. Wiselin Jiji and A. Rajesh and P. Johnson Durai
Raj",
title = "Decision Support Techniques for Dermatology Using
Case-Based Reasoning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "03",
pages = "??--??",
month = jul,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500242",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:10 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500242",
abstract = "Identification of skin disease has become a
challenging task with the origination of various skin
diseases. This paper presents a case-based reasoning
(CBR) decision support system to enhance dermatological
diagnosis for rural and remote communities. In this
proposed work, an automated way is introduced to deal
with the inconsistency problem in CBRs. This new hybrid
architecture is to support the diagnosis in multiple
skin diseases. The architecture used case-based
reasoning terminology facilitates the medical
diagnosis. Case based reasoning system retrieves the
data which contains symptoms and treatment plan of the
disease from the data repository by the way of matching
visual contents of the image, such as shape, texture,
and color descriptors. The extracted feature vector is
fed into a framework to retrieve the data. The results
proved using ROC curve that the proposed architecture
yields high contribution to the computer-aided
diagnosis of skin lesions. In experimental analysis,
the system yields a specificity of 95.25\% and a
sensitivity of 86.77\%. Our empirical evaluation has a
superior retrieval and diagnosis performance when
compared to the performance of other works.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nisha:2020:STP,
author = "S. Shajun Nisha and S. P. Raja and A. Kasthuri",
title = "Static Thresholded Pulse Coupled Neural Networks in
Contourlet Domain --- a New Framework for Medical Image
Denoising",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "03",
pages = "??--??",
month = jul,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500254",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:10 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500254",
abstract = "Image denoising, a significant research area in the
field of medical image processing, makes an effort to
recover the original image from its noise corrupted
image. The Pulse Coupled Neural Networks (PCNN) works
well against denoising a noisy image. Generally, image
denoising techniques are directly applied on the
pixels. From the literature review, it is reported that
denoising after frequency domain transformation is
performing better since noise removal is applied over
the coefficients. Motivated by this, in this paper, a
new technique called the Static Thresholded Pulse
Coupled Neural Network (ST-PCNN) is proposed by
combining PCNN with traditional filtering or threshold
shrinkage technique in Contourlet Transform domain.
Four different existing PCNN architectures, such as
Neuromime Structure, Intersecting Cortical Model,
Unit-Linking Model and Multichannel Model are
considered for comparative analysis. The filters such
as Wiener, Median, Average, Gaussian and threshold
shrinkage techniques such as Sure Shrink, HeurShrink,
Neigh Shrink, BayesShrink are used. For noise removal,
a mixture of Speckle and Gaussian noise is considered
for a CT skull image. A mixture of Rician and Gaussian
noise is considered for MRI brain image. A mixture of
Speckle and Salt and Pepper noise is considered for a
Mammogram image. The Performance Metrics such as Peak
Signal-to-Noise Ratio (PSNR), Structural Similarity
Index (SSIM), Image Quality Index (IQI), Universal
Image Quality Index (UQI), Image Enhancement Filter
(IEF), Structural Content (SC), Correlation Coefficient
(CC), and Weighted Signal-to-Noise Ratio (WSNR) and
Visual Signal-to-Noise Ratio (VSNR) are used to
evaluate the performance of denoising.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Araujo:2020:ECQ,
author = "Leonardo C. Araujo and Joao P. H. Sansao and Mario C.
S. Junior",
title = "Effects of Color Quantization on {JPEG} Compression",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "03",
pages = "??--??",
month = jul,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500266",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:10 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500266",
abstract = "This paper analyzes the effects of color quantization
on standard JPEG compression. Optimized color palettes
were used to quantize natural images, using dithering
and chroma subsampling as optional. The resulting
variations on file size and quantitative quality
measures were analyzed. Preliminary results, using a
small image database, show that file size suffered an
average 20\% increase and a concomitant loss in quality
was perceived ( {\textminus} 6dB PSNR, {\textminus}
0.16 SSIM and {\textminus} 9.6 Butteraugli). Color
quantization present itself as an ineffective tool on
JPEG compression but if necessarily imposed, on high
quality compressed images, it might lead to a
negligible increase in data size and quality loss. In
addition dithering seems to always decrease JPEG
compression ratio.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{MacIel:2020:SOF,
author = "Luiz Maur{\'\i}lio {da Silvad Maciel} and Marcelo
Bernardes Vieira",
title = "Sparse Optical Flow Computation Using Wave
Equation-Based Energy",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467820500278",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500278",
abstract = "Identification of motion in videos is a fundamental
task for several computer vision problems. One of the
main tools for motion identification is optical flow,
which estimates the projection of the 3D velocity of
the objects onto the plane of the camera. In this work,
we propose a differential optical flow method based on
the wave equation. The optical flow is computed by
minimizing a functional energy composed by two terms: a
data term based on brightness constancy and a
regularization term based on energy of the wave. Flow
is determined by solving a system of linear equations.
The decoupling of the pixels in the solution allows
solving the system by a direct or iterative approach
and makes the method suitable for parallelization. We
present the convergence conditions for our method since
it does not converge for all the image points. For
comparison purposes, we create a global video
descriptor based on histograms of optical flow for the
problem of action recognition. Despite its sparsity,
results show that our method improves the average
motion estimation, compared with classical methods. We
also evaluate optical flow error measures in image
sequences of a classical dataset for method
comparison.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mondal:2020:COD,
author = "Ajoy Mondal",
title = "Camouflaged Object Detection and Tracking: a Survey",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
DOI = "https://doi.org/10.1142/S021946782050028X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782050028X",
abstract = "Moving object detection and tracking have various
applications, including surveillance, anomaly
detection, vehicle navigation, etc. The literature on
object detection and tracking is rich enough, and there
exist several essential survey papers. However, the
research on camouflage object detection and tracking is
limited due to the complexity of the problem. Existing
work on this problem has been done based on either
biological characteristics of the camouflaged objects
or computer vision techniques. In this paper, we review
the existing camouflaged object detection and tracking
techniques using computer vision algorithms from the
theoretical point of view. This paper also addresses
several issues of interest as well as future research
direction in this area. We hope this paper will help
the reader to learn the recent advances in camouflaged
object detection and tracking.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Brahme:2020:EVV,
author = "Aparna Brahme and Umesh Bhadade",
title = "Effect of Various Visual Speech Units on Language
Identification Using Visual Speech Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500291",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500291",
abstract = "In this paper, we describe our work in Spoken language
Identification using Visual Speech Recognition (VSR)
and analyze the effect of various visual speech units
used to transcribe the visual speech on language
recognition. We have proposed a new approach of word
recognition followed by the word N-gram language model
(WRWLM), which uses high-level syntactic features and
the word bigram language model for language
discrimination. Also, as opposed to the traditional
visemic approach, we propose a holistic approach of
using the signature of a whole word, referred to as a
``Visual Word'' as visual speech unit for transcribing
visual speech. The result shows Word Recognition Rate
(WRR) of 88\% and Language Recognition Rate (LRR) of
94\% in speaker dependent cases and 58\% WRR and 77\%
LRR in speaker independent cases for English and
Marathi digit classification task. The proposed
approach is also evaluated for continuous speech input.
The result shows that the Spoken Language
Identification rate of 50\% is possible even though the
WRR using Visual Speech Recognition is below 10\%,
using only 1s of speech. Also, there is an improvement
of about 5\% in language discrimination as compared to
traditional visemic approaches.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nair:2020:RCA,
author = "Arun T. Nair and K. Muthuvel",
title = "Research Contributions with Algorithmic Comparison on
the Diagnosis of Diabetic Retinopathy",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500308",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500308",
abstract = "The medical field has been revolutionized by the
medical imaging system, which plays a key role in
providing information on the early life-saving
detection of dreadful diseases. Diabetic retinopathy is
a chronic visual disease that is the primary reason for
the vision loss in most of the patients, who left
undiagnosed at the initial stage. As the count of the
diabetic retinopathy affected people kept on
increasing, there is a necessity to have an automated
detection method. The accuracy of the diagnosis of the
automatic detection model is related to image
acquisition as well as image interpretation. In
contrast to this, the analysis of medical images by
using computerized models is still a limited task.
Thus, different kinds of detection methods are being
developed for early detection of diabetic retinopathy.
Accordingly, this paper focuses on the various
literature analyses on different detection algorithms
and techniques for diagnosing diabetic retinopathy.
Here, it reviews several research papers and exhibits
the significance of each detection method. This review
deals with the analysis on the segmentation as well as
classification algorithms that are included in each of
the researches. Besides, the adopted environment,
database collection and the tool for each of the
research are portrayed. It provides the details of the
performance analysis of the various diabetic detection
models and reveals the best value in the case of each
performance measure. Finally, it widens the research
issues that can be accomplished by future researchers
in the detection of diabetic retinopathy.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rukundo:2020:NEP,
author = "Olivier Rukundo",
title = "Non-Extra Pixel Interpolation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
DOI = "https://doi.org/10.1142/S021946782050031X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782050031X",
abstract = "A non-extra pixel interpolation NPI is introduced for
efficient image upscaling purposes. The NPI algorithm
uses extended-triangular and linear scaling functions
to match the pixel coordinates. The triangular function
uses a modulo-operator with only two variables
representing image pixels and scaling ratio. Every two
variables of the linear scaling function represent the
source/destination image pixels and scaling ratio. The
traditional ceil function is used to round off
non-integer pixel coordinates. The {\em circshift\/}
and {\em padarray\/} functions are used to circularly
shift the elements in array output by $k$-amount in
each dimension and pad elements of the $d$-th {\em
columns/rows\/} by {\em g-padsize\/} in the shifted
array, respectively. The $k$, $d$ and $g$ values are
determined with respect to integer scaling ratios by a
vector of $n$-elements. The Exactness, Peak
Signal-to-Noise Ratio, Signal-to-Noise Ratio and
Discrete Fourier Transform techniques were used for
objective evaluation purposes. Experiments demonstrated
comparable results as well as the need for further
researches.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bania:2020:ATM,
author = "Rubul Kumar Bania and Anindya Halder",
title = "Adaptive Trimmed Median Filter for Impulse Noise
Detection and Removal with an Application to Mammogram
Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500321",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500321",
abstract = "Mammography imaging is one of the most widely used
techniques for breast cancer screening and analysis of
abnormalities. However, due to some technical
difficulties during the time of acquisition and digital
storage of mammogram images, impulse noise may be
present. Therefore, detection and removals of impulse
noise from the mammogram images are very essential for
early detection and further diagnosis of breast cancer.
In this paper, a novel {\em adaptive trimmed median
filter\/} (ATMF) is proposed for impulse noise (salt &
pepper (SNP)) detection and removal with an application
to mammogram image denoising. Automatic switching
mechanism for updating the {\em Window of Interest\/}
(WoI) size from ( 3{\texttimes}3 ) to ( 5{\texttimes}5
) or ( 7{\texttimes}7 ) is performed. The proposed
method is applied on publicly available mammogram
images corrupted with varying SNP noise densities in
the range 5\%--90\%. The performance of the proposed
method is measured by various quantitative indices like
{\em peak signal to noise ratio\/} (PSNR), {\em mean
square error\/} (MSE), {\em image enhancement factor\/}
(IEF) and {\em structural similarity index measure\/}
(SSIM). The comparative analysis of the proposed method
is done with respect to other state-of-the-art noise
removal methods viz., {\em standard median filter\/}
(SMF), {\em decision based median filter\/} (DMF), {\em
decision based unsymmetric trimmed median filter\/}
(DUTMF), {\em modified decision based unsymmetric
trimmed median filter\/} (MDUTMF) and {\em decision
based unsymmetric trimmed winsorized mean filter\/}
(DUTWMF). The superiority of the proposed method over
other compared methods is well evident from the
experimental results in terms of the quantitative
indices (viz., PSNR, IEF and SSIM) and also from the
visual quality of the denoised images. Paired {\em
t-test\/} confirms the statistical significance of the
higher PSNR values achieved by the proposed method as
compared to the other counterpart techniques. The
proposed method turned out to be very effective in
denoising both high and low density noises present in
(mammogram) images.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kiley:2020:WMF,
author = "Matthew R. Kiley and Md Shafaeat Hossain",
title = "Who are My Family Members? {A} Solution Based on Image
Processing and Machine Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500333",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500333",
abstract = "Image creation and retention are growing at an
exponential rate. Individuals produce more images today
than ever in history and often these images contain
family. In this paper, we develop a framework to detect
or identify family in a face image dataset. The ability
to identify family in a dataset of images could have a
critical impact on finding lost and vulnerable
children, identifying terror suspects, social media
interactions, and other practical applications. We
evaluated our framework by performing experiments on
two facial image datasets, the Y-Face and KinFaceW,
comprising 37 and 920 images, respectively. We tested
two feature extraction techniques, namely PCA and HOG,
and three machine learning algorithms, namely {\em K\/}
-Means, agglomerative hierarchical clustering, and {\em
K\/} nearest neighbors. We achieved promising results
with a maximum detection rate of 94.59\% using {\em
K\/} -Means, 89.18\% with agglomerative clustering, and
77.42\% using {\em K\/} -nearest neighbors.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jiji:2020:FSE,
author = "G. Wiselin Jiji and A. Rajesh",
title = "Food Sustenance Estimation Using Food Image",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500345",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500345",
abstract = "The upcoming generation is at high risk of developing
many health issues like heart diseases, metabolic
diseases and other life-threatening problems with high
mortality as a consequence of obesity due to intake of
unhealthy food which is totally deviated from a normal
balanced diet with appropriate calories, proteins,
vitamins and carbohydrates. In this work, the nutrient
intake is calculated using food image. Our system
provides efficient segmentation algorithms for
separating food items from the plate. The given 2D
image of food is converted into 3D image by generating
its depth map for volume generation and color, texture
and shape features are extracted. These features are
fed as input into multi-class support vector machine
classifier for learning. The learning phase involves
training of various mixed and non mixed food items. The
testing phase includes query image segmentation and
classification for identifying the type of food and
then finding calories using the nutrition data table.
We have also estimated the ingredient and decay of food
items. Our result shows accurate calorie estimation for
various kinds of food items.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dhariwal:2020:INW,
author = "Sumit Dhariwal and Sellappan Palaniappan",
title = "Image Normalization and Weighted Classification Using
an Efficient Approach for {SVM} Classifiers",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500357",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500357",
abstract = "The content of massive image changing the brightest
brightness is an impasse between most tests of sorted
image realizations with low-resolution representation.
I have done this research through image security, which
will help curb crime in the coming days, and we propose
a novel receipt for their strong and effective
counterpart. Image classification using low levels of
the image is a difficult method, so for this, I have
adopted the method of automating the semantic image
classification of this research and used it with
different SVM classifiers, based on the normalized
weighted feature support vector machine for semantic
image classification. This is a novel approach given
that weighted feature or normalized biased feature is
applied and it is found that the normalized method is
the best. It also uses normalized weighted features to
compute kernel functions and train SVM. The trained SVM
is then used to classify new images. During training
and generalization, we displayed a decrease of
identification error rate and there have been many
benefits of using SVM with better performance in
normalized image-cataloging systems. The importance of
this technique and its role will be highlighted in the
years to come.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tripathi:2020:SAH,
author = "Kirthi Tripathi and Harsh Sohal and Shruti Jain",
title = "Statistical Analysis of {HRV} Parameters for the
Detection of Arrhythmia",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820500369",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820500369",
abstract = "The repolarization and depolarization in heart
generate electrical signals in the form of an ECG wave.
The condition of the heart can be indicated by using
Heart Rate Variability (HRV) features. In this work,
FIR filter is used at the pre-processing phase for
denoising, and then statistical analysis is applied for
time-domain HRV feature extraction and selection. This
algorithm is evaluated on different records of MIT/BIH
Normal Sinus Rhythm and Arrhythmia database. The t
-test implementation in both databases shows that there
are significant variations in HRV features, where
meanRR and HR have suggestive significant ( {0.05$<$
p}{\textlessequal}0.10 ) changes, while maxRR, minRR,
maxminRR, and SDNN have strongly significant (
p{\textlessequal}0.01 ) changes. To validate the
statistical analysis of HRV, feature classification has
been done using SVM and kNN classifiers. A significant
improvement of 2\% and 14.02\% has been observed in the
overall accuracy of SVM and kNN classifiers after
feature selection, respectively. These HRV features can
be used for the early prediction of various
Cardio-Vascular Diseases (CVD).",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2020:AIV,
author = "Anonymous",
title = "Author Index (Volume 20)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "20",
number = "04",
pages = "??--??",
month = oct,
year = "2020",
DOI = "https://doi.org/10.1142/S0219467820990016",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:11 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467820990016",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zaghloul:2021:FSI,
author = "Rawan I. Zaghloul and Hazem Hiary",
title = "A Fast Single Image Fog Removal Method Using Geometric
Mean Histogram Equalization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "01",
pages = "??--??",
month = jan,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467821500017",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500017",
abstract = "Fog is a natural phenomenon that affects scene
visibility, it reduces the contrast of the image and
causes color-fade. While various works in the
literature have addressed this issue, a fast effective
model is still lacking. In this paper, a single image
fog removal based on Geometric Mean Histogram
Equalization (GMHE) is proposed. In particular, the
proposed method is composed of three steps. The primary
step is to adaptively tune the performance of GMHE
according to the properties of the color histogram of
the foggy image. The obtained result then enters two
levels of chromaticity enhancement using the Hue
Saturation Value (HSV) and rotors color
transformations, respectively. Extensive experiments
demonstrate that the proposed method attains high
performance compared to the state-of-the-art methods in
terms of quality and execution time. The evaluation is
performed qualitatively by visual assessment, and
quantitatively using a set of full reference and
no-reference-based measures. As well, we suggest an
assessment criterion to combine the results of the
standard measures in a single score to facilitate the
comparisons between the different fog removal
methods.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ravikumar:2021:PPB,
author = "K. P. Ravikumar and H. S. Manjunatha Reddy",
title = "Pixel Prediction-Based Image Steganography Using Crow
Search Algorithm-Based Deep Belief Network Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "01",
pages = "??--??",
month = jan,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500029",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500029",
abstract = "Securing the confidentiality of patient information
using the image steganography process has gained more
attention in the research community. However, embedding
the patient information is a major task in the
steganography process due to the complexity in
identifying the pixel features. Thus, an effective Crow
Search Algorithm-based deep belief network (CSA-DBN) is
proposed for embedding the information in the medical
image. Initially, the appropriate pixels and the
features, like pixel coverage, wavelet energy, edge
information, and texture features, such as local binary
pattern (LBP) and local directional pattern (LDP), are
extracted from each pixel. The proposed CSA-DBN
utilizes the feature vector and identifies the suitable
pixels used for embedding. The patient information is
embedded into the image by using the embedding strength
and the DWT coefficient. Finally, the embedded
information is extracted using the DWT coefficient. The
analysis of the proposed CSA-DBN approach is done based
on the performance metrics, such as correlation
coefficient, peak signal-to-noise ratio (PSNR), and
structural similarity index (SSIM) that acquired the
average values as 0.9471, 24.836 dB, and 0.4916 in the
presence of salt and pepper noise and 0.9741, 57.832
dB, and 0.9766 in the absence of noise.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jagdale:2021:MRO,
author = "Rohita H. Jagdale and Sanjeevani K. Shah",
title = "Modified Rider Optimization-Based {V} Channel
Magnification for Enhanced Video Super Resolution",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "01",
pages = "??--??",
month = jan,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500030",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500030",
abstract = "In video Super Resolution (SR), the problem of cost
expense concerning the attainment of enhanced spatial
resolution, computational complexity and difficulties
in motion blur makes video SR a complex task. Moreover,
maintaining temporal consistency is crucial to
achieving an efficient and robust video SR model. This
paper plans to develop an intelligent SR model for
video frames. Initially, the video frames in RGB format
will be transformed into HSV. In general, the
improvement in video frames is done in V-channel to
achieve High-Resolution (HR) videos. In order to
enhance the RGB pixels, the current window size is
enhanced to high-dimensional window size. As a novelty,
this paper intends to formulate a high-dimensional
matrix with enriched pixel intensity in V-channel to
produce enhanced HR video frames. Estimating the
enriched pixels in the high-dimensional matrix is
complex, however in this paper, it is dealt in a
significant way by means of a certain process: (i)
motion estimation (ii) cubic spline interpolation and
deblurring or sharpening. As the main contribution, the
cubic spline interpolation process is enhanced via
optimization in terms of selecting the optimal
resolution factor and different cubic spline
parameters. For optimal tuning, this paper introduces a
new modified algorithm, which is the modification of
the Rider Optimization Algorithm (ROA) named Mean
Fitness-ROA (MF-ROA). Once the HR image is attained, it
combines the HSV and converts to RGB, which obtains the
enhanced output RGB video frame. Finally, the
performance of the proposed work is compared over other
state-of-the-art models with respect to BRISQUE, SDME
and ESSIM measures, and proves its superiority over
other models.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hassan:2021:CEM,
author = "Mohd Fikree Hassan",
title = "Color Enhancement Method to Improve the Colors of the
Images Perceived by the Elderly People",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "01",
pages = "??--??",
month = jan,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500042",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500042",
abstract = "Smartphones and tablets present data and information
in color images. Due to factors such as yellowing
pigmentation and miosis filter, elderly people may
experience difficulties and confusion when looking at
the color images on smartphones and tablets. In this
paper, we propose a color enhancement method to improve
the color perceived by elderly people. This method is
based on the color perception of the elderly simulated
using the uniform yellowing pigmentation method. The
proposed method enhances the colors of the images to
compensate for the effect of yellowing pigmentation and
miosis filter. This is achieved by utilizing the error
parameters between the original colors and colors
perceived by the elderly. Implementing an adaptation
matrix, the error parameters are modified and
distributed back into the original colors iteratively.
Experimental results showed that the proposed method
improves the colors perceived by the elderly.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jiji:2021:CRF,
author = "G. Wiselin Jiji and A. Rajesh and P. Johnson Durai
Raj",
title = "{CBI + R}: a Fusion Approach to Assist Dermatological
Diagnoses",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "01",
pages = "??--??",
month = jan,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500054",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500054",
abstract = "With the emerge of advanced technologies such as
high-resolution cameras and computational power, it
seems to ease to built a better dermatological
diagnostic system. However, the identification of skin
disease is still a challenging problem with the
origination of various skin diseases. In this paper, we
proposed a new fusion architecture --- CBI + R to
support the diagnosis in multiple skin diseases. The
architecture combines Content-Based Image Retrieval
(CBIR) and Case-Based Reasoning (CBR) technology
together to facilitate medical diagnosis. CBIR is to
retrieve digital dermoscopy images from a data
repository using the shape, texture and color features.
Along with these features, CBR is incorporated which
contains symptoms, case history and treatment plan of
the disease. Experiments on a set of 1210 images
yielded an accuracy of 98.2\%. This was a superior
retrieval and diagnosis performance in comparison with
the state-of-the-art works.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Joshi:2021:SDI,
author = "Anand B. Joshi and Dhanesh Kumar and D. C. Mishra",
title = "Security of Digital Images Based on {$3$D} {Arnold}
Cat Map and Elliptic Curve",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "01",
pages = "??--??",
month = jan,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500066",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500066",
abstract = "Security of digital data is an important task in the
present era. In this paper, we propose a new scheme of
digital image encryption and decryption method based on
three-dimensional (3D) Arnold cat map (ACM) and
elliptic curve. In this proposed encryption method, we
have applied 3D ACM on the digital color image which
performs the dual encryption first, it performs the
permutation and second, it performs the substitution of
image pixels. After that, elliptic curve cryptography
(ECC) is used to encrypt the image, for this a mapping
method is proposed to convert the pixels of the image
as points on the elliptic curve. Further, a mapping
inverting method is proposed for decryption and then 3D
inverse Arnold cat map (iACM) is applied to get the
original image. The statistical and security analyses
are done on various images and the experimental results
show the robustness of the proposed method.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Akrour:2021:FHI,
author = "Leila Akrour and Soltane Ameur and Mourad Lahdir and
R{\'e}gis Fournier and Amine Nait Ali",
title = "Fast Hyperspectral Image Encoder Based on Supervised
Multimodal Scheme",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "01",
pages = "??--??",
month = jan,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500078",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500078",
abstract = "Many compression methods, lossy or lossless, were
developed for 3D hyperspectral images, and various
standards have emerged and applied to these amounts of
data in order to achieve the best rate-distortion
performance. However, high-dimensional data volume of
hyperspectal images is problematic for compression and
decompression time. Nowadays, fast compression and
especially fast decompression algorithms are of primary
importance in image data applications. In this case, we
present a lossy hyperspectral image compression based
on supervised multimodal scheme in order to improve the
compression results. The supervised multimodal method
is used to reduce the amount of data before their
compression with the 3D-SPIHT encoder based on 3D
wavelet transform. The performance of the Supervised
Multimodal Compression (SMC-3D-SPIHT encoder) has been
evaluated on AVIRIS hyperspectral images. Experimental
results indicate that the proposed algorithm provides
very promising performance at low bit-rates while
reducing the encoding/decoding time.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Suryanarayana:2021:SRB,
author = "Gunnam Suryanarayana and Kandala N. V. P. S. Rajesh
and Jie Yang",
title = "Super-Resolution Based on Residual Learning and
Optimized Phase Stretch Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "01",
pages = "??--??",
month = jan,
year = "2021",
DOI = "https://doi.org/10.1142/S021946782150008X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782150008X",
abstract = "High resolution infrared (IR) images are often
required in military and industrial applications. Due
to the limited properties of IR imaging sensors and
camera lens, IR images exhibit poor spatial resolution
with a blur phenomenon in the edge regions. In this
correspondence, we develop a new super-resolution
(SR)-IR image reconstruction method using the residual
learning network in the wavelet domain (WRESNET) and
optimized phase stretch transform (PST). Our algorithm
first transforms the input low resolution (LR)-IR image
into its low-frequency and high-frequency subbands
using the discrete wavelet decomposition. Subsequently,
we introduce the optimized PST to operate on the LR-IR
image and extract the intrinsic edge structure. The PST
behaves differently at low-frequency and high-frequency
regions, thus capturing the intensity variations for
edge detection. We incorporate the PST extracted edge
map in the wavelet subbands to preserve the intrinsic
structure of images. The resultant subbands are further
refined based on the missing residuals obtained using
the WRESNET. The proposed method is validated through
quantitative and qualitative evaluations against the
conventional and state-of-art SR methods. Results
reveal that the proposed method outperforms the
existing methods.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pradhan:2021:ALE,
author = "Ashis Pradhan and Mohan P. Pradhan",
title = "Automatic Localization of Elevation Values in a Poor
Quality Topographic Map",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "01",
pages = "??--??",
month = jan,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500091",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500091",
abstract = "A topographic sheet hosts various morphological
features that effectively describe the terrain. This
multi-faced information content not only elevates human
perception but also provides ample direction for
research initiatives. Out of all possible attributes
based on utility, contours have wide set of
application. A contour is characterized by its
coordinate system and most importantly, its elevation
detail. Upon, successful attainment of these two
attributes, creating a fully automatic 3D projection
system may be achieved with relative ease. In contrast
to the traditional manual approach, this research
initiative puts forward a novel mechanism for
automatically localizing contour and its attributes
including coordinate pattern and elevation value in a
referenced map. To accomplish the aforementioned
objectives, the proposed mechanism relies on various
image processing techniques based on morphological
operations. Further, the extracted details can be used
to project the contours in a 3D space. This projection
is also called Digital Elevation Model (DEM). DEM is
crucial for various applications such as Terrain
Modeling, Hydrological Modeling, Path Optimization, to
name a few. Automatically and accurately created DEM
from topographic sheet could contribute a lot in many
Geographical Information System (GIS) applications.
This paper focuses mainly on elevation value
localization associated with specific contour.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Silva:2021:CMA,
author = "Rodrigo Dalvit C. Silva and Thomas R. Jenkyn",
title = "Classification of Mammogram Abnormalities Using
{Legendre} Moments",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "01",
pages = "??--??",
month = jan,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500108",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500108",
abstract = "In this paper, the issue of classifying mammogram
abnormalities using images from an mammogram image
analysis society (MIAS) database is discussed. We
compare a feature extractor based on Legendre moments
(LMs) with six other feature extractors. To determine
the best feature extractor, the performance of each was
compared in terms of classification accuracy rate and
extraction time using a k -nearest neighbors ( k -NN)
classifier. This study shows that feature extraction
using LMs performed best with an accuracy rate over
84\% and requiring relatively little time for feature
extraction, on average only 1 s.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jiji:2021:DPD,
author = "G. Wiselin Jiji and A. Rajesh and P. Johnson Durai
Raj",
title = "Diagnosis of {Parkinson}'s Disease Using {SVM}
Classifier",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946782150011X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782150011X",
abstract = "Parkinson's disease (PD) is the most common disease
that affects aged people which leads to
dopamine-producing cells in substantia nigra to be
damaged when motor system degenerates. Clinical
Diagnosis of Parkinson's disease at the earlier stage
is very difficult. This work is carried out to find the
significance of cognition function of basal ganglia
(BG) region and speech data values. The BG can be
segmented using morphological operation and active
contour algorithm. Co-occurrences features are
extracted and out of 720 features, the promising 110
features are selected using variance method. More
promising 22 features are selected in speech data and
both features are individually classified using SVM to
find out the efficiency in Diagnosis. The outcome shows
cognition function of BG performing a major role in
early diagnosis of Parkinson's disease when compared to
speech data.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Boudraa:2021:ECS,
author = "Omar Boudraa and Walid Khaled Hidouci and Dominique
Michelucci",
title = "An Efficient Cooperative Smearing Technique for
Degraded Historical Document Image Segmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500121",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500121",
abstract = "Segmentation is one of the critical steps in
historical document image analysis systems that
determines the quality of the search, understanding,
recognition and interpretation processes. It allows
isolating the objects to be considered and separating
the regions of interest (paragraphs, lines, words and
characters) from other entities (figures, graphs,
tables, etc.). This stage follows the thresholding,
which aims to improve the quality of the document and
to extract its background from its foreground, also for
detecting and correcting the skew that leads to redress
the document. Here, a hybrid method is proposed in
order to locate words and characters in both
handwritten and printed documents. Numerical results
prove the robustness and the high precision of our
approach applied on old degraded document images over
four common datasets, in which the pair (Recall,
Precision) reaches approximately 97.7\% and 97.9\%.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Khosravi:2021:NIR,
author = "Javanshir Khosravi and Mohammad Shams Esfand Abadi and
Reza Ebrahimpour",
title = "A Novel Iterative Rigid Image Registration Algorithm
Based on the {Newton} Method",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500133",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500133",
abstract = "In recent years, Image Registration has attracted lots
of attention due to its capabilities and numerous
applications. Various methods have been exploited to
map two images with the same concept but different
conditions. Considering the finding of the mentioned
map as an optimization problem, mathematical-based
optimization methods have been extensively employed due
to their real-time performances. In this paper, we
employed the Newton method to optimize two defined cost
functions. These cost functions are Sum of Square
Difference and Cross-Correlation. These presented
algorithms have fast convergence and accurate features.
Also, we propose an innovative treatment in order to
attend to one of the free parameter-rotations or scale
as a sole variable and the other one as the constant
value. The assignment is replaced through the
iterations for both parameters. The intuition is to
turn a two-variable optimization problem into a single
variable one in every step. Our simulation on benchmark
images by the means of Root Mean Square Error and
Mutual Information as the goodness criteria, that have
been extensively used in similar studies, has shown the
robustness and affectivity of the proposed method.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Poonkuntran:2021:SIC,
author = "S. Poonkuntran and P. Alli and T. M. Senthil Ganesan
and S. Manthira Moorthi and M. P. Oza",
title = "Satellite Image Classification Using Cellular
Automata",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500145",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500145",
abstract = "The satellite image classification plays a vital role
in remote sensing for analyzing the images and
recognizing the patterns. Supervised classification is
one of the methods in which pixels of an image are
grouped based on training samples. The uncertainty is
one of the major issues in a supervised classification,
where the pixel is classified into more than one class.
This is happened due to the use of spectral values
without considering contextual values in
classification. Hence, this paper proposes Cellular
Automata (CA)-based Classifier for Satellite Images
Classification, where spectral values are combined with
contextual values to improve the accuracy of the
classifier. The proposed CA classifier combines the
spectral values with contextual values in iteration
until the uncertainty is resolved. Thereby, the
proposed scheme improves the accuracy of the classical
supervised classifier of parallel piped, minimum
distance, DSVM and KNN classifier by 7\% at an
average.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Siddiqui:2021:CBV,
author = "Tanveer J. Siddiqui and Ashish Khare",
title = "Chaos-based Video Steganography Method in Discrete
Cosine Transform Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500157",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500157",
abstract = "Due to the technological advancements in digital
communication, the amount of multimedia content over
the internet has increased manifold in past decade.
This has renewed the internet of researchers in the
area of privacy and secure communication. This paper
presents a secure and robust video steganography method
in discrete cosine transform (DCT) domain. In order to
enhance the security of the proposed algorithm, the
frame selection process is randomized and the secret
data are pre-treated using Arnold's cat map. The secret
data are embedded in the middle band DCT coefficient
using two pseudo random sequences. These sequences are
generated using a chaotic map. We analyze the proposed
algorithm in terms of peak signal-to-noise ratio
(PSNR), structural similarity index (SSIM), multi-scale
structural similarity index (MSSIM) and video quality
metric (VQM). The evaluation has been done on 107 video
sequences. The experimental results demonstrate that
the algorithm maintains acceptable video quality. The
robustness of the proposed method is tested under
Gaussian and salt and pepper noise attack using
correlation between original and recovered images. The
proposed algorithm is able to recover 90.60\% data
without error under salt and pepper noise ( D=0.001 )
attack and 87.23\% data correctly under Gaussian noise
attack with mean $ = 0 $ and variance $ = 0.001 $.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jameel:2021:MLT,
author = "Samer Kais Jameel and Sezgin Aydin and Nebras H.
Ghaeb",
title = "Machine Learning Techniques for Corneal Diseases
Diagnosis: a Survey",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500169",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500169",
abstract = "Machine learning techniques become more related to
medical researches by using medical images as a
dataset. It is categorized and analyzed for ultimate
effectiveness in diagnosis or decision-making for
diseases. Machine learning techniques have been
exploited in numerous researches related to corneal
diseases, contribution to ophthalmologists for
diagnosing the diseases and comprehending the way
automated learning techniques act. Nevertheless,
confusion still exists in the type of data used,
whether it is images, data extracted from images or
clinical data, the course reliant on the type of device
for obtaining them. In this study, the researches that
used machine learning were reviewed and classified in
terms of the kind of utilized machine for capturing
data, along with the latest updates in sophisticated
approaches for corneal disease diagnostic techniques.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Budhiraja:2021:IVI,
author = "Sumit Budhiraja and Iftisam Rummy and Sunil Agrawal
and Balwinder Singh Sohi",
title = "Infrared and Visible Image Fusion Based on Sparse
Representation and Spatial Frequency in {DTCWT}
Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500170",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500170",
abstract = "Infrared and visible image fusion is a key area of
research in multi-sensor image fusion. The main purpose
of this fusion is to combine thermal information of the
infrared image and texture information of the visible
image. This paper presents an image fusion framework,
based on parallel arrangement of sparse representation
(SR) and spatial frequency (SF). In the proposed
framework, an efficient edge-aware filter, i.e. guided
filter, is first employed on the visible image. Then
dual-tree complex wavelet transform (DTCWT) is used to
obtain low-pass and high-pass coefficients of images,
as it is shift-invariant and has high directional
selectivity. The low-pass coefficients are fused using
the SR- and SF-based fusion rules in parallel, which
enhances the regional features of the images. The
simulation results show that the proposed technique has
better performance when compared with conventional
techniques in both subjective and objective
evaluations.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Choudhary:2021:MBB,
author = "Swati K. Choudhary and Ameya K. Naik",
title = "Multimodal Biometric-Based Authentication with Secured
Templates",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500182",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500182",
abstract = "This paper proposes a multimodal biometric based
authentication (verification and identification) with
secured templates. Multimodal biometric systems provide
improved authentication rate over unimodal systems at
the cost of increased concern for memory requirement
and template security. The proposed framework performs
person authentication using face and fingerprint.
Biometric templates are protected by hiding fingerprint
into face at secret locations, through blind and
key-based watermarking. Face features are extracted
from approximation sub-band of Discrete Wavelet
Transform, which reduces the overall working plane. The
proposed method also shows high robustness of biometric
templates against common channel attacks. Verification
and identification performances are evaluated using two
chimeric and one real multimodal dataset. The same
systems, working with compressed templates provides
considerable reduction in overall memory requirement
with negligible loss of authentication accuracies.
Thus, the proposed framework offers positive balance
between authentication performance, template robustness
and memory resource utilization.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pathade:2021:SMC,
author = "Manasi Pathade and Madhuri Khambete",
title = "Supervised Method for Congestion Detection at Entry
and Exit Corridors of Public Places",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500194",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500194",
abstract = "Continuous monitoring and automatic detection of crowd
activities is extremely helpful for management at
public places to avoid any possible disaster. Analysis
of crowded scene is a critical task as it typically
involves poor resolution of objects, occlusions and
complex dynamics. In this paper, we propose a novel,
systematic and generalized method based on global
motion analysis of people to detect Congestion
situation in crowded scenes at entry/exit corridors.
Our approach is tested on video footages acquired from
surveillance cameras installed at exit corridors of
public places. The results show the expediency of our
approach.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Manchanda:2021:ICB,
author = "Meenu Manchanda and Deepak Gambhir",
title = "Improvement in {CNN}-Based Multifocus Image Fusion
Algorithm with Triangulated Fuzzy Filter",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500200",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500200",
abstract = "Multifocus image fusion is a demanding research field
due to the utilization of modern imaging devices.
Generally, the scene to be captured contains objects at
different distances from these devices and so a set of
multifocus images of the scene is captured with
different objects in-focus. However, to improve the
situational awareness of the captured scene, these sets
of images are required to be fused together. Therefore,
a multifocus image fusion algorithm based on
Convolutional Neural Network (CNN) and triangulated
fuzzy filter is proposed. A CNN is used to extract
information regarding focused pixels of input images
and the same is used as fusion rule for fusing the
input images. The focused information so extracted may
still need to be refined near the boundaries.
Therefore, asymmetrical triangular fuzzy filter with
the median center (ATMED) is employed to correctly
classify the pixels near the boundary. The advantage of
using this filter is to rely on precise detection
results since any misdetection may considerably degrade
the fusion quality. The performance of the proposed
algorithm is compared with the state-of-art image
fusion algorithms, both subjectively and objectively.
Various parameters such as edge strength ( Q ), fusion
loss (FL), fusion artifacts (FA), entropy ( H ),
standard deviation (SD), spatial frequency (SF),
structural similarity index measure (SSIM) and feature
similarity index measure (FSIM) are used to evaluate
the performance of the proposed algorithm. Experimental
results proved that the proposed fusion algorithm
produces a fused image that contains all-in-one focused
pixels and is better than those obtained using other
popular and latest image fusion works.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sharma:2021:RIB,
author = "Urvashi Sharma and Meenakshi Sood and Emjee
Puthooran",
title = "Region of Interest-Based Coding Technique of Medical
Images Using Varying Grading of Compression",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500212",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500212",
abstract = "A region of interest (ROI)-based compression method
for medical image datasets is a requirement to maintain
the quality of the diagnostically important region of
the image. It is always a better option to compress the
diagnostic important region in a lossless manner and
the remaining portion of the image with a near-lossless
compression method to achieve high compression
efficiency without any compromise of quality. The
predictive ROI-based compression on volumetric CT
medical image is proposed in this paper;
resolution-independent gradient edge detection (RIGED)
and block adaptive arithmetic encoding (BAAE) are
employed to ROI part for prediction and encoding that
reduce the interpixel and coding redundancy. For the
non-ROI portion, RIGED with an optimal threshold value,
quantizer with optimal q -level and BAAE with optimal
block size are utilized for compression. The volumetric
8-bit and 16-bit standard CT image dataset is utilized
for the evaluation of the proposed technique, and
results are validated on real-time CT images collected
from the hospital. Performance of the proposed
technique in terms of BPP outperforms existing
techniques such as JPEG 2000, M-CALIC, JPEG-LS, CALIC
and JP3D by 20.31\%, 19.87\%, 17.77\%, 15.58\% and
13.66\%, respectively.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Panwar:2021:FES,
author = "Kirtee Panwar and Ravindra Kumar Purwar and Garima
Srivastava",
title = "A Fast Encryption Scheme Suitable for Video
Surveillance Applications Using {SHA-256} Hash Function
and {$1$D} Sine--Sine Chaotic Map",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500224",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500224",
abstract = "This paper proposes an image encryption technique
which is fast and secure. The encryption scheme is
designed for secure transmission of video surveillance
data (keyframes) over insecure network. The image
encryption technique employs 1D Sine--Sine system with
better chaotic properties than its seed map and faster
than higher-dimensional chaotic systems. Further,
design of encryption scheme is based on two permutation
rounds, which employs pixel swapping operation and
diffusion operation which is simple and provides
required security against plaintext, differential and
various other attacks. Three separate chaotic sequences
are generated using 1D Sine--Sine system which enhances
the key space of the encryption scheme. Secret keys are
updated dynamically with SHA-256 hash value obtained
from plain image. Hash values of plain image are
efficiently used without loss of any hash value
information. This makes the encryption scheme plaintext
sensitive and secure against plaintext attacks.
Performance and security aspects of encryption scheme
is analyzed both quantitatively using predefined
security metrics and qualitatively by scrutinizing the
internal working of encryption scheme. Computational
complexity of encrypting a plain image of size \(
rows{\texttimes} columns \) is {$ \mathcal {O} $} \(
rows{\texttimes}columns \) and is suitable for
encrypting keyframes of video for secure surveillance
applications.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kamath:2021:DSI,
author = "Priya R. Kamath and Kedarnath Senapati and P. Jidesh",
title = "Despeckling of {SAR} Images Using Shrinkage of
Two-Dimensional Discrete Orthonormal {$S$}-Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500236",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500236",
abstract = "Speckles are inherent to SAR. They hide and undermine
several relevant information contained in the SAR
images. In this paper, a despeckling algorithm using
the shrinkage of two-dimensional discrete orthonormal
S-transform (2D-DOST) coefficients in the transform
domain along with shock filter is proposed. Also, an
attempt has been made as a post-processing step to
preserve the edges and other details while removing the
speckle. The proposed strategy involves decomposing the
SAR image into low and high-frequency components and
processing them separately. A shock filter is used to
smooth out the small variations in low-frequency
components, and the high-frequency components are
treated with a shrinkage of 2D-DOST coefficients. The
edges, for enhancement, are detected using a
ratio-based edge detection algorithm. The proposed
method is tested, verified, and compared with some
well-known models on C-band and X-band SAR images. A
detailed experimental analysis is illustrated.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kalaivani:2021:EBI,
author = "A. Kalaivani and K. Swetha",
title = "An Enhanced Bidirectional Insertion Sort Over
Classical Insertion Sort",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500248",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500248",
abstract = "Sorting is a technique which is used to arrange the
data in specific order. A sorting technique is applied
to rearrange the elements in numerical order as
ascending order or descending order or for words in
alphabetical order. In this paper, we propose an
efficient sorting algorithm known as Enhanced
Bidirectional Insertion Sorting algorithm which is
developed from insertion sort concept. A comparative
analysis is done for the proposed Enhanced
Bidirectional Insertion Sort algorithm with the
selection sort and insertion sort algorithms. When
compared to insertion sort algorithm the proposed
algorithm outperforms with less number of comparisons
in worst case and average case computing time. The
proposed algorithm works efficiently for duplicated
elements which is the advanced improvement and the
results are proved.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shaikh:2021:STB,
author = "Ayesha S. Shaikh and Vibha D. Patel",
title = "Significance of the Transition to Biometric Template
Protection: Explore the Future",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "02",
pages = "??--??",
month = apr,
year = "2021",
DOI = "https://doi.org/10.1142/S021946782150025X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed May 5 11:23:13 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782150025X",
abstract = "The IT security paradigm evolves from secret-based to
biometric identity-based. Biometric identification has
gradually become more popular in recent years for
handheld devices. Privacy-preserving is a key concern
when biometrics is used in authentication systems in
the present world today. Nowadays, the declaration of
biometric traits has been imposed not only by the
government but also by many private entities. There are
no proper mechanisms and assurance that biometric
traits will be kept safe by such entities. The
encryption of biometric traits to avoid privacy attacks
is a giant problem. Hence, state-of-the-art safety and
security technological solutions must be devised to
prevent the loss and misuse of such biometric traits.
In this paper, we have identified different cancelable
biometrics methods with the possible attacks on the
biometric traits and directions on possible
countermeasures in order to design a secure and
privacy-preserving biometric authentication system. We
also proposed a highly secure method for cancelable
biometrics using a non-invertible function based on
Discrete Cosine Transformation and Index of max
hashing. We tested and evaluated the proposed novel
method on a standard dataset and achieved good
results.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kiran:2021:CSI,
author = "S. Shashi Kiran and K. V. Suresh",
title = "Challenges in Sparse Image Reconstruction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467821500261",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500261",
abstract = "Handling huge amount of data from different sources
more so in the images is the latest challenge. One of
the solutions to this is sparse representation. The
idea of sparsity has been receiving much attention
recently from many researchers in the areas such as
satellite image processing, signal processing, medical
image processing, microscopy image processing, pattern
recognition, neuroscience, seismic imaging, etc. Many
algorithms have been developed for various areas of
sparse representation. The main objective of this paper
is to provide a comprehensive study and highlight the
challenges in the area of sparse representation which
will be helpful for researchers. Also, the current
challenges and opportunities of applying sparsity to
image reconstruction, namely, image super-resolution,
image denoising and image restoration are discussed.
This survey on sparse representation categorizes the
existing methods into three groups: dictionary learning
approach, greedy strategy approximation approach and
deep learning approach.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sowmyayani:2021:MTC,
author = "S. Sowmyayani and V. Murugan",
title = "Multi-Type Classification Comparison of Mammogram
Abnormalities",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500273",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500273",
abstract = "Cancer is a life-threatening disease which reduces the
lifespan of humans. If the disease is treated early,
the lifespan can be extended. This paper provides a
useful method for detecting the abnormalities in the
mammograms. The proposed method uses four phases such
as pre-processing, segmentation, feature extraction and
classification. In the pre-processing phase, median
filter is utilized to enhance the quality of an image.
The pre-processed image is then segmented by fuzzy C
means (FCM). Three different features such as
Gaussian--Hermite moments (GHM), Jacobi moments and
pseudo Zernike moments (PZM) are extracted from the
segmented image. Finally, extreme learning machine
(ELM) classifier identifies the normal, malignant and
benign kinds of cancer. This method is compared with
four different classifiers. The proposed method is
tested on mammographic image analysis society (MIAS)
dataset and the performance is evaluated against
several analogous approaches in terms of accuracy,
sensitivity and specificity. The proposed approach
substantially provides the best result.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mondal:2021:SGS,
author = "Md. Abdul Mannan Mondal and Mohammad Haider Ali",
title = "Self-guided Stereo Correspondence Estimation
Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500285",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500285",
abstract = "This paper introduces an innovative algorithm,
``Self-guided Stereo Correspondence'' (SGSC), that is
directed by photometric properties of the candidate
pixels. As the photometric properties of reference
image (left image) pixel and its neighbor's pixel are
similar in most cases, so the upcoming corresponding
pixel exists in the surrounding of the previous
matching pixel. Searching performance is greatly
improved by utilizing this photometric property of the
candidate pixels. The searching performance is further
improved by applying the pioneering threshold
technique. These two key techniques sufficiently
reduced the computational cost with no degradation of
accuracy. The achievements of the proposed method are
testified on Middlebury standard Stereo Datasets of
2003 and 2006 and the Middlebury latest Optical Flow
Dataset. Finally, the proposed method is compared with
present state-of-the-art methods and our SGSC outdoes
the latest methods in terms of speed, visualization of
hidden ground truth, 3D reconstruction and accuracy.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Basheera:2021:GMS,
author = "Shaik Basheera and M. Satya Sai Ram",
title = "{Gray} Matter Segmentation of Brain {MRI} Using Hybrid
Enhanced Independent Component Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500297",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500297",
abstract = "One of the primary pre-processing tasks of medical
image analysis is segmentation; it is used to diagnose
the abnormalities in the tissues. As the brain is a
complex organ, anatomical segmentation of brain tissues
is a challenging task. Segmented gray matter is
analyzed for early diagnosis of neurodegenerative
disorders. In this endeavor, we used enhanced
independent component analysis to perform segmentation
of gray matter in noise-free and noisy environments. We
used modified k -means, expectation--maximization and
hidden Markov random field to provide better spatial
relation to overcome inhomogeneity, noise and low
contrast. Our objective is achieved using the following
two steps: (i) Irrelevant tissues are stripped from the
MRI using skull stripping algorithm. In this algorithm,
sequence of threshold, morphological operations and
active contour are applied to strip the unwanted
tissues. (ii) Enhanced independent component analysis
is used to perform segmentation of gray matter. The
proposed approach is applied on both T1w MRI and T2w
MRI images at different noise environments such as salt
and pepper noise, speckle noise and Rician noise. We
evaluated the performance of the approach using Jaccard
index, Dice coefficient and accuracy. The parameters
are further compared with existing frameworks. This
approach gives better segmentation of gray matter for
the diagnosis of atrophy changes in brain MRI.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rawal:2021:DMW,
author = "Kirti Rawal and Gaurav Sethi",
title = "Design of Matched Wavelet Using Improved Genetic
Algorithm for Heart Rate Variability Analysis of the
Menstrual Cycle",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500303",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500303",
abstract = "The matched wavelet is designed in this paper using an
improved genetic algorithm for detecting the Heart Rate
Variability (HRV) variations within phases of the
menstrual cycle accurately. The idea of an improved
genetic algorithm is to use an optimization technique
like least mean square (LMS) before the genetic
algorithm. The advantage of using the LMS prior to the
genetic algorithm is to optimize the data before giving
to the genetic algorithm, thereby limiting the area of
the search for an optimal solution. The results show
that matched wavelets created using an improved genetic
algorithm can detect the HRV variations accurately in
the standing and laying postures.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wagdy:2021:DDI,
author = "Marian Wagdy and Khaild Amin and Mina Ibrahim",
title = "Dewarping Document Image Techniques: Survey and
Comparative Study",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500315",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500315",
abstract = "In recent years, everyone has his/her own handheld
digital devices such as PDAs and camera phones which
are used to capture any documents, for example,
posters, magazine and books. This is the simplest way
to disseminating and collecting information.
Unfortunately, the snapshot of this document in an
uncontrolled environment has been suffering from
different perspectives and geometric distortions,
especially when a picture is taken from rolled
document, page of thick book, multi-folded documents
and crumpled pages. In such cases, the most common
distortion appeared is warping text lines. In this
paper, we present a survey and a comparative study of
document image dewarping techniques which aim to solve
the curled lines and geometric distortion problems. We
introduce a new classification of the available
dewarping document image techniques and investigate
their available datasets. Finally, we present the
evaluation metric to test these techniques.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nnolim:2021:SIH,
author = "Uche A. Nnolim",
title = "Single Image De-Hazing via Multiscale Wavelet
Decomposition and Estimation with Fractional
Gradient-Anisotropic Diffusion Fusion",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500327",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500327",
abstract = "This paper presents algorithms based on fractional
multiscale gradient fusion and multilevel wavelet
decomposition for underwater and hazy image
enhancement. The algorithms utilize partial
differential equation (PDE)-generated low- and
high-frequency images fused via gradient domain and
anisotropic diffusion. Furthermore, wavelet multi-level
decomposition, estimation and adjustment of detail and
approximation coefficients are employed in improving
local and global enhancement. Solutions to halo effect
are also developed using compressive bilateral filters
or other nonlinear/nonlocal means filter. Ultimately,
experimental comparisons indicate that the proposed
methods surpass or are comparable to several algorithms
from the literature.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Arora:2021:CHM,
author = "Tanvi Arora",
title = "Classification of Human Metaspread Images Using
Convolutional Neural Networks",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500339",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500339",
abstract = "Chromosomes are the genetic information carriers. Any
modification to the structure or the number of
chromosomes results in a medical condition termed as
genetic defect. In order to uncover the genetic
defects, the chromosomes are imaged during the cell
division process. The images thus generated are termed
as metaspread images and are used for identifying the
genetic defects. It has been observed that the
metaspread images generally suffer from intensity
inhomogeneity and the chromosomes are also present in
varied orientations, and as a result finding genetic
defects from such images is a tedious process.
Therefore, cytogeneticists manually select the images
that can be used for the purpose of uncovering the
genetic defects and the generation of the karyotype. In
the proposed approach, a novel method is being
presented using DenseNet architecture of the
convolutional neural networks-based classifier, which
classifies the human metaspread images into two
distinct categories, namely, analyzable and
non-analyzable based on the orientation of the
chromosomes present in the metaspread images. This
classification process will help to select the most
prominent metaspread images for karyotype generation
that has least amount of touching and overlapping
chromosomes. The proposed method is novel in comparison
to the earlier methods as it works on any type of
image, be it G band images, MFISH images or the
Q-banded images. The proposed method has been trained
by using a ground truth of 156{\nobreakspace}40750
metaspread images. The proposed classifier has been
able to achieve an error rate of 1.46\%.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gudise:2021:MBI,
author = "Sandhya Gudise and Giri Babu Kande and T. Satya
Savithri",
title = "{MR} Brain Image Segmentation to Detect White Matter,
Gray Matter, and Cerebro Spinal Fluid Using {TLBO}
Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500340",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500340",
abstract = "This paper proposes an advanced and precise technique
for the segmentation of Magnetic Resonance Image (MRI)
of the brain. Brain MRI segmentation is to be familiar
with the anatomical structure, to recognize the
deformities, and to distinguish different tissues which
help in treatment planning and diagnosis. Nature's
inspired population-based evolutionary algorithms are
extremely popular for a wide range of applications due
to their best solutions. Teaching Learning Based
Optimization (TLBO) is an advanced population-based
evolutionary algorithm designed based on Teaching and
Learning process of a classroom. TLBO uses common
controlling parameters and it won't require
algorithm-specific parameters. TLBO is more appropriate
to optimize the real variables which are fuzzy valued,
computationally efficient, and does not require
parameter tuning. In this work, the pixels of the brain
image are automatically grouped into three distinct
homogeneous tissues such as White Matter (WM), Gray
Matter (GM), and Cerebro Spinal Fluid (CSF) using the
TLBO algorithm. The methodology includes skull
stripping and filtering in the pre-processing stage.
The outcomes for 10 MR brain images acquired by
utilizing the proposed strategy proved that the three
brain tissues are segmented accurately. The
segmentation outputs are compared with the ground truth
images and high values are obtained for the measure's
sensitivity, specificity, and segmentation accuracy.
Four different approaches, namely Particle Swarm
Optimization (PSO), Genetic Algorithm (GA), Bacterial
Foraging Algorithm (BFA), and Electromagnetic
Optimization (EMO) are likewise implemented to compare
with the results of the proposed methodology. From the
results, it can be proved that the proposed method
performed effectively than the other.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shivsharan:2021:DRD,
author = "Nitin Shivsharan and Sanjay Ganorkar",
title = "Diabetic Retinopathy Detection Using Optimization
Assisted Deep Learning Model: Outlook on Improved Grey
Wolf Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500352",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500352",
abstract = "In recent days, study on retinal image remains a
significant area for analysis. Several retinal diseases
are identified by examining the differences occurring
in the retina. Anyhow, the major shortcoming between
these analyses was that the identification accuracy is
not satisfactory. The adopted framework includes two
phases namely; (i) feature extraction and (ii)
classification. Initially, the input fundus image is
subjected to the feature extraction process, where the
features like Local Binary Pattern (LBP), Local Vector
Pattern (LVP) and Local Tetra Patterns (LTrP) are
extracted. These extracted features are subjected to
the classification process, where the Deep Belief
Network (DBN) is used as the classifier. In addition,
to improve the accuracy, the activation function and
hidden neurons of DBN are optimally tuned by means of
the Self Improved Grey Wolf Optimization (SI-GWO).
Finally, the performance of implemented work is
compared and proved over the conventional models.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Salehi:2021:NHF,
author = "Hadi Salehi and Javad Vahidi",
title = "A Novel Hybrid Filter for Image Despeckling Based On
Improved Adaptive {Wiener} Filter, Bilateral Filter and
Wavelet Filter",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500364",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500364",
abstract = "Images are widely used in engineering. But, some
images such as medical ultrasound images are mainly
degraded by an intrinsic noise called speckle.
Therefore, de-speckling is a main pre-processing stage
for degraded images. In this paper, we suggest three
phases and three denoising filters. In the first phase,
the coefficient of variation is computed from the noisy
image. Next, fuzzy c-means (FCM) is applied to the
coefficients of variation. Applying FCM leads to the
fuzzy classification of image regions. Next, the second
phase is a hybrid of the three denoising filters. Fast
bilateral filter (BF) for homogeneous regions, improved
the adaptive wiener filters (AWFs) and wavelet filter
that are applied on homogeneous, detail and edge
regions, respectively. The proposed improved AWF has
been developed from the AWF. In the third phase, the
output image is evaluated by the fuzzy logic approach.
Thus, with three phases, the proposed method has a
better image detail preservation compared to some other
standard methods. The experimental outcomes show that
the proposed denoising algorithm is able to preserve
image details and edges compared with other
de-speckling methods.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rema:2021:EHC,
author = "N. R. Rema and P. Mythili",
title = "Extremely High Compression and Identification of
Fingerprint Images Using {SA4} Multiwavelet Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500376",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500376",
abstract = "The aim of any fingerprint image compression technique
is to achieve a maximum amount of compression with an
adequate quality compressed image which is suitable for
fingerprint recognition. Currently available techniques
in the literature provide 100\% recognition only up to
a compression ratio of 180:1. The performance of any
identification technique inherently depends on the
techniques with which images are compressed. To improve
the identification accuracy while the images are highly
compressed, a multiwavelet-based identification
approach is proposed in this paper. Both decimated and
undecimated coefficients of SA4 (Symmetric
Antisymmetric) multiwavelet are used as features for
identification. A study is conducted on the
identification performance of the multiwavelet
transform with various sizes of images compressed using
both wavelets and multiwavelets for fair comparison. It
was noted that for images with size power of 2, the
decimated multiwavelet-based compression and
identification give a better performance compared to
other combinations of compression/identification
techniques whereas for images with size not a power of
2, the undecimated multiwavelet transform gives a
better performance compared to other techniques. A
100\% identification accuracy was achieved for the
images from NIST-4, NITGEN, FVC2002DB3\_B,
FVC2004DB2\_B and FVC2004DB1\_B databases for
compression ratios up to 520:1, 210:1, 445:1, 545:1 and
1995:1, respectively.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zarif:2021:VIC,
author = "Sameh Zarif and Mina Ibrahim",
title = "Video Inpainting: A Complete Framework",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500388",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500388",
abstract = "Reconstructing and repairing of corrupted or missing
parts after object removal in digital video is an
important trend in artwork restoration. Video
inpainting is an active subject in video processing,
which deals with the recovery of the corrupted or
missing data. Most previous video inpainting approaches
consume more time in extensive search to find the best
patch to restore the damaged frames. In addition to
that, most of them cannot handle the gradual and sudden
illumination changes, dynamic background, full object
occlusion, and object changes in scale. In this paper,
we present a complete video inpainting framework
without the extensive search process. The proposed
framework consists of a segmentation stage based on
low-resolution version and background subtraction. A
background inpainting stage is applied to restore the
damaged background regions after static or moving
object removal based on the gray-level co-occurrence
matrix (GLCM). A foreground inpainting stage is based
on objects repository. GLCM is used to complete the
moving occluded objects during the occlusion. The
proposed method reduces the inpainting time from hours
to a few seconds and maintains the spatial and temporal
consistency. It works well when the background has
clutter or fake motion, and it can handle the changes
in object size and in posture. Moreover, it is able to
handle full occlusion and illumination changes.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Siri:2021:ALB,
author = "Sangeeta K. Siri and S. Pramod Kumar and Mrityunjaya
V. Latte",
title = "Accurate Liver Border Identification Model in {CT}
Scan Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S021946782150039X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782150039X",
abstract = "The liver is an important organ in human body with
certain variations in its edges, color, shape and pixel
intensity distribution. These uncertainties may be
because of various liver pathologies, hereditary or
both. Along with it, liver has close proximity to its
nearby organs. Hence, identifying liver in scanned
images is a challenging step in image processing. This
task becomes more imprecise when liver diseases are
present at the edges. The liver segmentation is
prerequisite for liver volumetry, computer-based
surgery planning, liver surgery modelling, surgery
training, 3D view generation, etc. The proposed hybrid
segmentation method overcomes the problems and
identifies liver boundary in Computed-Tomography (CT)
scan images accurately. In this paper, the first step
is to study statistics of pixel intensity distribution
within liver image, and novel methodology is designed
to obtain thresholds. Then, threshold-based
segmentation is applied which separates the liver from
abdominal CT scan images. In the second step, liver
edge is corrected using improved chain code and
Bresenham pixel interconnection methods. This provides
a precise liver image. The initial points are located
inside the liver region without user interventions.
These initial points evolve outwardly using Fast
Marching Method (FMM), identifying the liver boundary
accurately in CT abdominal scan images.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bentahar:2021:HTB,
author = "Tarek Bentahar and Atef Bentahar and Riad Saidi and
Hichem Mayache and Karim Ferroudji",
title = "Hybrid Technique of the Branch-Cut and the
Quality-Guided for {inSAR} Phase Unwrapping",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "03",
pages = "??--??",
month = jul,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500406",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Jul 5 15:21:12 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500406",
abstract = "Phase unwrapping is a key step for interferometric
synthetic aperture radar imaging. It is widely used for
earth mapping and surface change detection. Several
residue-immune phase unwrapping algorithms have been
proposed; among them, we find branch-cut and
quality-guided in the path-following category.
Branch-cut methods are usually faster than the
quality-guided techniques; however, the accuracy of
their unwrapped phase images is lower. In this paper, a
hybrid model which combines both algorithms is proposed
in order to establish a satisfactory compromise between
processing time and accuracy. In order to verify the
usefulness of the proposed hybridization, it is tested
on simulated and real inSAR data. The obtained results
are compared with the two methods under several
relevant metrics.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Brown:2021:CSF,
author = "Kyle Brown and Nikolaos Bourbakis",
title = "Curve and Surface Fitting Techniques in Computer
Vision",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467821500418",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500418",
abstract = "Curve and surface-fitting are classic problems of
approximation that find use in many fields, including
computer vision. There are two broad approaches to the
problem --- interpolation, which seeks to fit points
exactly, and regression, which seeks a rougher
approximation which is more robust to noise. This
survey looks at several techniques of both kinds, with
a particular focus on applications in computer vision.
We make use of an empirical first-level evaluation
approach which scores the techniques on multiple
features based on how important they are to users of
the technique and developers. This provides a quick
summary of the broad applicability of the technique to
most situations, rather than a deep evaluation of the
performance and accuracy of the technique obtained by
running it on several datasets.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pervej:2021:RTC,
author = "Masud Pervej and Sabuj Das and Md. Parvez Hossain and
Md. Atikuzzaman and Md. Mahin and Muhammad Aminur
Rahaman",
title = "Real-Time Computer Vision-Based {Bangla} Vehicle
License Plate Recognition using Contour Analysis and
Prediction Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S021946782150042X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782150042X",
abstract = "Computer vision-based recognition of Bangle vehicle
license plates (LPs) is an arduous task in dirty and
muddy situations. This paper proposes an efficient
method for real-time computer vision-based recognition
of Bangla vehicle LPs using contour analysis and
prediction algorithms. The method initially applies
gray scaling the input RGB images, histogram
equalization to improve the grayscale image quality,
edge detection using Sobel edge detector, and adaptive
thresholding to convert it to a binary image. The
system localizes the vehicle LP based on the maximum
rectangular contour area and converts it into a
predefined size. Noise removal technique using
morphological dilation and erosion operation is used,
followed by Gaussian filtering on binary image to
improve the image quality further. The system clusters
the two-lined LP into seven clusters. The
sub-clustering is applied on specific clusters and
makes 68 individual sub-clusters. The system extracts
vector contour (VC) from each 68 individual classes.
After VC extraction, the system normalizes it into a q
predefined length. The system applies inter co-relation
function (ICF) to categorize each sub-cluster to its
previously defined individual class. For that, it
calculates the maximum similarity between test and
previously trained VCs. The system applies the
dependency prediction algorithm in parallel to predict
the whole string (district name) in the cluster-1 based
on previously categorized class or classes (starting
character or characters of the district part). (Metro)
or (null) from cluster-2, ``-'' (hyphen) from cluster-3
and 6 are predicted automatically as their positions
are fixed. The system is trained using 68 classes in
which each class contains 100 samples generated by the
augmentation technique. The system is tested using
another set of 68 classes with a total of
68{\texttimes}100=6800 images acquiring the recognition
accuracy of 96.62\% with the mean computational cost of
8.363 ms/f. The system is also tested using 500 vehicle
whole Bangla LPs achieving the mean whole LP
recognition accuracy of 95.41\% with a mean
computational cost of 35.803 ms/f.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Patel:2021:ELB,
author = "Krina Patel and Dippal Israni and Dweepna Garg",
title = "An Efficient Local Block {Sobolev} Gradient and
{Laplacian} Approach for Elimination of Atmospheric
Turbulence",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500431",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500431",
abstract = "A long range observing systems can be sturdily
affected by scintillations. These scintillations are
caused by changes in atmospheric conditions. In recent
years, various turbulence mitigation approaches for
turbulence mitigation have been exhibiting a promising
nature. In this paper, we propose an effectual method
to alleviate the effects of atmospheric distortion on
observed images and video sequences. These sequences
are mainly affected through floating air turbulence
which can severely degrade the image quality. The
existing algorithms primarily focus on the removal of
turbulence and provides a solution only for static
scenes, where there is no moving entity (real motion).
As in the traditional SGL algorithm, the updated frame
is iteratively used to correct the turbulence. This
approach reduces the turbulence effect. However, it
imposes some artifacts on the real motion that blurs
the object. The proposed method is an alteration of the
existing Sobolev Gradient and Laplacian (SGL) algorithm
to eliminate turbulence. It eliminates the ghost
artifact formed on moving object in the existing
approach. The proposed method alleviates turbulence
without harming the moving objects in the scene. The
method is demonstrated on significantly distorted
sequences provided by OTIS and compared with the SGL
technique. The information conveyed in the scene
becomes clearly visible through the method on exclusion
of turbulence. The proposed approach is evaluated using
standard performance measures such as MSE, PSNR and
SSIM. The evaluation results depict that the proposed
method outperforms the existing state-of-the-art
approaches for all three standard performance
measures.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nivedita:2021:ICV,
author = "M. Nivedita and Priyanka Chandrashekar and Shibani
Mahapatra and Y. Asnath Victy Phamila and Sathish Kumar
Selvaperumal",
title = "Image Captioning for Video Surveillance System using
Neural Networks",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500443",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500443",
abstract = "Security has always been of paramount importance to
humans. In the absence of a sense of security at one's
workplace, home or anywhere else, people feel uneasy
and vulnerable. With the improvement of modern
technology, along with the lack of time at hand, the
need for faster, efficient, accurate as well as
low-cost security techniques is more than ever. Image
Captioning for Video Surveillance System is proposed to
develop visual systems that generate contextual
descriptions about objects in images, and then use
these descriptions to provide information of the scene
that needs to be secured. The proposed system uses a
neural network model composed of a Convolutional Neural
Network (CNN) and Long Short-Term Memory (LSTM) to
caption the incoming video feed. The main significance
of this paper is in integrating the system with
Discrete Wavelet Transform (DWT), which is applied on
the incoming video feed, so that the compressed LL band
frames transferred wirelessly to the model are smaller
in comparison, leading to less transfer time and faster
processing by the model.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pal:2021:RMD,
author = "Tannistha Pal",
title = "A Robust Method for Dehazing of Single Image with Sky
Region Detection and Segmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500455",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500455",
abstract = "In recent times, there has been a tremendous progress
in image dehazing for computer vision applications,
while the sky region processed by these algorithms
tends to degrade by noise and color distortion. In this
paper, an improved dark channel prior algorithm is
proposed which detects the sky region first and divides
the image into sky region and non-sky region and then
estimates the transmission of two parts separately,
followed by combining with refining step. The proposed
algorithm also accurately corrects the transmission of
the sky region to avoid noise and color distortion.
Experimental results show a greater quality improvement
in the output images than the existing strategies.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2021:ECB,
author = "Gangavarapu Venkata Satya Kumar and Pillutla Gopala
Krishna Mohan",
title = "Enhanced Content-Based Image Retrieval Using
Information Oriented Angle-Based Local Tri-Directional
{Weber} Patterns",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500467",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500467",
abstract = "In diverse computer applications, the analysis of
image content plays a key role. This image content
might be either textual (like text appearing in the
images) or visual (like shape, color, texture). These
two image contents consist of image's basic features
and therefore turn out to be as the major advantage for
any of the implementation. Many of the art models are
based on the visual search or annotated text for
Content-Based Image Retrieval (CBIR) models. There is
more demand toward multitasking, a new method needs to
be introduced with the combination of both textual and
visual features. This paper plans to develop the
intelligent CBIR system for the collection of different
benchmark texture datasets. Here, a new descriptor
named Information Oriented Angle-based Local
Tri-directional Weber Patterns (IOA-LTriWPs) is
adopted. The pattern is operated not only based on
tri-direction and eight neighborhood pixels but also
based on four angles 0\textdegree, 45\textdegree,
90\textdegree, and 135\textdegree. Once the patterns
concerning tri-direction, eight neighborhood pixels,
and four angles are taken, the best patterns are
selected based on maximum mutual information. Moreover,
the histogram computation of the patterns provides the
final feature vector, from which the new weighted
feature extraction is performed. As a new contribution,
the novel weight function is optimized by the Improved
MVO on random basis (IMVO-RB), in such a way that the
precision and recall of the retrieved image is high.
Further, the proposed model has used the logarithmic
similarity called Mean Square Logarithmic Error (MSLE)
between the features of the query image and trained
images for retrieving the concerned images. The
analyses on diverse texture image datasets have
validated the accuracy and efficiency of the developed
pattern over existing.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kang:2021:GIB,
author = "Henry Kang and Ioannis Stamoulis",
title = "{Gaussian} Image Binarization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500479",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500479",
abstract = "Line drawing and screentoning are two distinct areas
of study in non-photorealistic rendering, where the
former emphasizes object contours, while the latter
conveys tone and shading information on object
surfaces. As these two problems are concerned with
different yet equally important features, either method
seldom delivers a complete description of the scene
when used alone. Yet, research community has largely
treated them as separate problems and thus resulted in
two entirely different sets of solutions, complicating
both implementation and usage. In this paper, we
present a stylistic image binarization method called
{\em hybrid difference of Gaussians (HDoG)\/} that
performs both line drawing and screentoning in a
unified framework. Our method is based upon two
different extensions of DoG operator: one for line
extraction, and the other for tone description. In
particular, we propose an extension called {\em
adaptive DoG}, that uses luminance as weight to
automatically generate screentone that adapts to the
local tone. Experimental results demonstrate that our
hybrid method effectively generates aesthetically
pleasing image binarizations that encompass both line
drawing and screentoning, closely resembling
professional pen-and-ink illustrations. Also, being
based on Gaussian filtering, our method is very fast
and also easy to implement.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Swamy:2021:HIC,
author = "A. S. Anand Swamy and N. Shylashree",
title = "{HDR} Image Compression by Multi-Scale down Sampling
of Intensity Levels",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500480",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500480",
abstract = "HDR images are inherently very large in size compared
to normal images. Hence, storage and communication
overheads of HDR images are expensive to be used in
mobile devices. Hence, invariably image compression is
adopted for HDR images. In this paper, HDR image
compression is achieved by down sampling the intensity
levels while maintaining the dynamic range same as that
of the original. This aspect retains the edge
information of the images almost intact. Spatial
down-sampling process is used to reduce the number of
intensity samples. Consequently, this operation lowers
the bit depth required to store the corresponding index
file which in turn results in image compression.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Panigrahi:2021:JBF,
author = "Susant Kumar Panigrahi and Supratim Gupta",
title = "Joint Bilateral Filter for Signal Recovery from Phase
Preserved Curvelet Coefficients for Image Denoising",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500492",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500492",
abstract = "Thresholding of Curvelet Coefficients, for image
denoising, drains out subtle signal component in noise
subspace. In effect, it also produces ringing artifacts
near edges. We found that the noise sensitivity of
Curvelet phases{\nobreakspace}40 --- in contrast to
their magnitude{\nobreakspace}40 --- reduces with
higher noise level. Thus, we preserved the phase of the
coefficients below threshold at coarser scale and
estimated the corresponding magnitude by Joint
Bilateral Filtering (JBF) technique. In contrast to the
traditional hard thresholding, the coefficients in the
finest scale is estimated using Bilateral Filtering
(BF). The proposed filtering approach in the finest
scale exhibits better connectedness among the edges,
while removing the granular artifacts in the denoised
image due to hard thresholding. Finally, the use of
Guided Image Filter (GIF) on the Curvelet-based
reconstructed image (initial denoised image in spatial
domain) ensures the preservation of small image
information with sharper edges and textures detail in
the final denoised image. The lower noise sensitivity
of Curvelet phase at higher noise strength accelerates
the performance of proposed method over several
state-of-the-art techniques and provides comparable
outcome at lower noise levels.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ramwala:2021:RNC,
author = "Ojas A. Ramwala and Smeet A. Dhakecha and Chirag N.
Paunwala and Mita C. Paunwala",
title = "Reminiscent Net: Conditional {GAN}-based Old Image
De-Creasing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500509",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500509",
abstract = "Documents are an essential source of valuable
information and knowledge, and photographs are a great
way of reminiscing old memories and past events.
However, it becomes difficult to preserve the quality
of such ancient documents and old photographs for an
extremely long time, as these images usually get
damaged or creased due to various extrinsic effects.
Utilizing image editing software like Photoshop to
manually reconstruct such old photographs and documents
is a strenuous and an enduring process. This paper
attempts to leverage the generative modeling
capabilities of Conditional Generative Adversarial
Networks by utilizing specialized architectures for the
Generator and the Discriminator. The proposed
Reminiscent Net has a U-Net-based Generator with
numerous feature maps for complete information transfer
with the incorporation of location and contextual
details, and the absence of dense layers allows
utilization of diverse sized images. Implementation of
the PatchGAN-based Discriminator that penalizes the
image at the scale of patches has been proposed. NADAM
optimizer has been implemented to enable faster and
better convergence of the loss function. The proposed
method produces visually appealing de-creased images,
and experiments indicate that the architecture performs
better than various novel approaches, both
qualitatively and quantitatively.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kuzhali:2021:AID,
author = "S. Elavaar Kuzhali and D. S. Suresh",
title = "Automated Image Denoising Model: Contribution Towards
Optimized Internal and External Basis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500510",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500510",
abstract = "For handling digital images for various applications,
image denoising is considered as a fundamental
pre-processing step. Diverse image denoising algorithms
have been introduced in the past few decades. The main
intent of this proposal is to develop an effective
image denoising model on the basis of internal and
external patches. This model adopts Non-local means
(NLM) for performing the denoising, which uses
redundant information of the image in pixel or spatial
domain to reduce the noise. While performing the image
denoising using NLM, ``denoising an image patch using
the other noisy patches within the noisy image is done
for internal denoising and denoising a patch using the
external clean natural patches is done for external
denoising''. Here, the selection of optimal block from
the entire datasets including internal noisy images and
external clean natural images is decided by a new
hybrid optimization algorithm. The two renowned
optimization algorithms Chicken Swarm Optimization
(CSO), and Dragon Fly Algorithm (DA) are merged, and
the new hybrid algorithm Rooster-based Levy Updated DA
(RLU-DA) is adopted. The experimental results in terms
of some relevant performance measures show the
promising results of the proposed model with remarkable
stability and high accuracy.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ehsaeyan:2021:MIT,
author = "Ehsan Ehsaeyan and Alireza Zolghadrasli",
title = "A Multilevel Image Thresholding Method Using the
{Darwinian} Cuckoo Search Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500522",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500522",
abstract = "Image segmentation is a prime operation to understand
the content of images. Multilevel thresholding is
applied in image segmentation because of its speed and
accuracy. In this paper, a novel multilevel
thresholding algorithm based on Cuckoo search (CS) is
introduced. One of the major drawbacks of metaheuristic
algorithms is the stagnation phenomenon which leads to
a fall into local optimums and premature convergence.
To overcome this shortcoming, the idea of Darwinian
theory is incorporated with CS algorithm to increase
the diversity and quality of the individuals without
decreasing the convergence speed of CS algorithm. A
policy of encouragement and punishment is considered to
lead searching agents in the search space and reduce
the computational time. The algorithm is implemented
based on dividing the population into specified groups
and each group tries to find a better location. Ten
test images are selected to verify the ability of our
algorithm using the famous energy curve method. Two
popular entropies criteria, Otsu and Kapur, are
employed to evaluate the capability of the introduced
algorithm. Eight different search algorithms are also
implemented and compared with our method. Experimental
results manifest that DCS is a powerful tool for
multilevel thresholding and the obtained results
outperform the CS algorithm and other heuristic search
methods.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pan:2021:SID,
author = "Yongpeng Pan and Zhenxue Chen and Xianming Li and
Weikai He",
title = "Single-Image Dehazing via Dark Channel Prior and
Adaptive Threshold",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500534",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500534",
abstract = "Due to the haze weather, the outdoor image quality is
degraded, which reduces the image contrast, thereby
reducing the efficiency of computer vision systems such
as target recognition. There are two aspects of the
traditional algorithm based on the principle of dark
channel to be improved. First, the restored images
obviously contain color distortion in the sky region.
Second, the white regions in the scene easily affect
the atmospheric light estimated. To solve the above
problems, this paper proposes a single-image dehazing
and image segmentation method via dark channel prior
(DCP) and adaptive threshold. The sky region of hazing
image is relatively bright, so sky region does not meet
the DCP. The sky part is separated by the adaptive
threshold, then the scenery and the sky area are
dehazed, respectively. In order to avoid the
interference caused by white objects to the estimation
of atmospheric light, we estimate the value of
atmospheric light using the separated area of the sky.
The algorithm in this paper makes up for the
shortcoming that the algorithm based on the DCP cannot
effectively process the hazing image with sky region,
avoiding the effect of white objects on estimating
atmospheric light. Experimental results show the
feasibility and effectiveness of the improved
algorithm.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chalamalasetty:2021:RPT,
author = "Sai Pratheek Chalamalasetty and Srinivasa Rao
Giduturi",
title = "Research Perception Towards Copy-Move Image Forgery
Detection: Challenges and Future Directions",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500546",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500546",
abstract = "In digital images, Copy-Move Forgery is a general kind
of forgery techniques. The process of replicating one
part of the image within the same image is termed as
copy-move forgery. An effective and reliable approach
needs to be developed for identifying these forgeries
for restoring the image trustworthiness. The main
intent of this paper is to sort out the diverse
copy-move image forgery detection models. This survey
makes an effective literature analysis on a set of
literal works from the past 10 years. The analysis is
focused on categorizing the models based on
transformation models, machine learning algorithms, and
other advanced techniques. The main contribution and
limitations of the works are clearly pointed out. In
addition, the types of datasets and the simulation
platforms utilized by different copy-move forgery
detection (CMFD) models are analyzed. The performance
measures evaluated by different contributions have been
observed for making a concluding decision. The
utilization of optimization algorithms on copy-move
image forgery detection has also been studied. Finally,
the research gaps and challenges with future direction
are discussed, which is helpful for researchers in
developing an efficient CMFD that could attain high
performance.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hamroun:2021:NCB,
author = "Mohamed Hamroun and Karim Tamine and Frederic Claux
and Mourad Zribi",
title = "A New Content-Based Image Retrieval System Using Deep
Visual Features",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821500558",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821500558",
abstract = "Content-based image retrieval (CBIR) is a technique
for images retrieval based on their visual features,
i.e. induced by their pixels. The images are,
classically, described by the image feature vectors.
Those vectors reflect the texture, color or a
combination of them. The accuracy of the CBIR system is
highly influenced by the (i) definition of the image
feature vector describing the image, (ii) indexing and
(iii) retrieval process. In this paper, we propose a
new CBIR system entitled ISE (Image Search Engine). Our
ISE system defines the optimum combination of color and
texture features as an image feature vector, including
the Particle Swarm Optimization (PSO) algorithm and
employing an Interactive Genetic Approach (GA) for the
indexing process. The performance analysis shows that
our suggested PCM (Proposed Combination Method)
upgrades the average precision metric from 66.6\% to
89.30\% for the ``Food'' category color histogram, from
77.7\% to 100\% concerning CCVs (Color Coherence
Vectors) for the ``Flower'' category and from 58\% to
87.65\% regarding the DCD (Dominant Color Descriptor)
for the ``Building'' category using the Corel dataset.
Besides, our ISE system showcases an average precision
of 98.23\%, which is significantly higher than other
CBIR systems presented in related works.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2021:AIV,
author = "Anonymous",
title = "Author Index (Volume 21)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "04",
pages = "??--??",
month = oct,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821990011",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:54 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821990011",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Naveenkumar:2021:STJ,
author = "M. Naveenkumar and S. Domnic",
title = "Spatio Temporal Joint Distance Maps for Skeleton-Based
Action Recognition Using Convolutional Neural
Networks",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "05",
pages = "??--??",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467821400015",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:56 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400015",
abstract = "Skeleton-based action recognition has become popular
with the recent developments in sensor technology and
fast pose estimation algorithms. The existing research
works have attempted to address the action recognition
problem by considering either spatial or temporal
dynamics of the actions. But, both the features
(spatial and temporal) would contribute to solve the
problem. In this paper, we address the action
recognition problem using 3D skeleton data by
introducing eight Joint Distance Maps, referred to as
Spatio Temporal Joint Distance Maps (ST-JDMs), to
capture spatio temporal variations from skeleton data
for action recognition. Among these, four maps are
defined in spatial domain and remaining four are in
temporal domain. After construction of ST-JDMs from an
action sequence, they are encoded into color images.
This representation enables us to fine-tune the
Convolutional Neural Network (CNN) for action
classification. The empirical results on the two
datasets, UTD MHAD and NTU RGB+D, show that ST-JDMs
outperforms the other state-of-the-art skeleton-based
approaches by achieving recognition accuracies 91.63\%
and 80.16\%, respectively.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Deep Neural Networks for Medical
Image Detection, Segmentation, and Localization",
}
@Article{Sharma:2021:DDS,
author = "Moolchand Sharma and Bhanu Jain and Chetan Kargeti and
Vinayak Gupta and Deepak Gupta",
title = "Detection and Diagnosis of Skin Diseases Using
Residual Neural Networks {(RESNET)}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "05",
pages = "??--??",
month = dec,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821400027",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:56 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400027",
abstract = "Skin diseases have become prevalent in the present
times. It has been observed in a study that every year
the percentage of global population suffering from skin
diseases is 1.79\%. These diseases have a potential to
become extremely dangerous if they are not treated in
the nascent stages. It is extremely important that skin
diseases are detected and diagnosed at the starting
stages so that serious risks to life are avoided.
Often, exhaustive tests are required so as to arrive on
a conclusion regarding skin condition, the patient may
be affected with. Thus, an expert system is required
that has the ability to identify diseases and propose
the required diagnosis. Presently, only a few solutions
are available for diagnosis of skin diseases using
computerized system but this is an era which is under
extensive research and can be developed further. As the
existing system has certain loopholes, this system
attempts to override the present problems by applying a
different approach. As a result of comparison of
results from numerous research papers, an expert system
has been developed by choosing residual neural networks
(ResNet) and this system can be used to aid skin
specialists in identifying and diagnosing various major
diseases of skin like (Eczema, Psoriasis & Lichen
Planus, Benign Tumors, Fungal Infections and Viral
Infections) in more effective and efficient manner. The
causes for identified skin disease can be outlined
through this system and treatment can be provided. We
have used Python language for implementing the proposed
expert system that uses a 50-layer ResNets for training
a dataset that has been taken from DERMNET. We achieved
an accuracy of 95\% using ResNet for training of the
model and prediction of results at an epoch value of
10.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Deep Neural Networks for Medical
Image Detection, Segmentation, and Localization",
}
@Article{Gupta:2021:DCB,
author = "Isha Gupta and Sheifali Gupta and Swati Singh",
title = "Different {CNN}-based Architectures for Detection of
Invasive Ductal Carcinoma in Breast Using
Histopathology Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "05",
pages = "??--??",
month = dec,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821400039",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:56 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400039",
abstract = "In recent years, many improvements have been made in
image processing techniques which aid pathologists to
identify cancer cells. Nowadays, convolutional neural
networks (CNNs), also known as deep learning algorithms
have become popular for the applications of image
processing and examination in histopathology image
(tissue and cell images). This study aims to present
the detection of histopathology images associated to
detection of invasive ductal carcinoma (IDC) and
non-IDC in breast. However, detection of IDC is a
challenging task in histopathology image which needs
deep examination as cancer comprises of minor entities
with a diversity of forms which can be easily mixed up
with different objects or facts contained in image.
Hence, the proposed study suggests three types of CNN
architectures which is called 8-layer CNNs, 9-layer
CNNs and 19-layer CNNs, respectively, in the detecting
IDC using histopathology images. The purpose of the
study is to identify IDC from histopathology images by
taking proper layer in deep layer CNNs with the maximum
accuracy, highest sensitivity, precision and least
classification error. The result shows better
performance for deep layer-convolutional neural
networks architecture by using 19-layer CNNs.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Deep Neural Networks for Medical
Image Detection, Segmentation, and Localization",
}
@Article{Verma:2021:CBD,
author = "Parag Verma and Ankur Dumka and Anuj Bhardwaj and
Mukesh Chandra Kestwal",
title = "Classifying Breast Density in Mammographic Images
Using Wavelet-Based and Fine-Tuned Sensory Neural
Networks",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "05",
pages = "??--??",
month = dec,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821400040",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:56 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400040",
abstract = "In this modern world of biomedical medicine, the
classification of breast density has been considered a
very important part of the process of breast diagnosis.
Furthering the same research, this research aims to
determine the patient's breast density by mammogram
image with the help of modern techniques such as
computerized devices and machine learning algorithms,
which will greatly help the radiologist. To carry out
this process, this research paper introduces a
Convolutional Neural Network (CNN) model of deep
learning that will work as a basis for waveform
conversion and fine-tune. This deep learning model will
prove effective in automatically classifying a
patient's breast density. With the help of this method,
the last two layers which are fully connected are
removed and joined with two newly formed layers. This
would have helped in addressing a pre-trained AlexNet
model that further improved the classification process.
In this model, the original or preprocessed images are
used at level 1 of the input (which is in sharp
contrast to the usual methods based on the CNN model),
which also makes the model compatible with the use of
redundant wavelet coefficients. Because in the field of
radiologists it is very important to underline the
difference between scattered density and heterogeneous
density, so the main objective of this research is
focused on this end. As the proposed method has an
accuracy of 82.2\%, it shows a better performance. This
research paper further compares the effectiveness and
performance of the proposed method to traditional
fine-tuning CNN models, with satisfactory results. The
comparative results of the proposed method suggest that
the proposed method is in the field of radiologists
representing a helpful tool. This method may be
intended to act as a second eye for doctors in the
medical field with the intention of classifying the
categories of breast density in the patient during
breast cancer screening.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Deep Neural Networks for Medical
Image Detection, Segmentation, and Localization",
}
@Article{Tunga:2021:UNM,
author = "P. Prakash Tunga and Vipula Singh and V. Sri Aditya
and N. Subramanya",
title = "{U-Net} Model-Based Classification and Description of
Brain Tumor in {MRI} Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "21",
number = "05",
pages = "??--??",
month = dec,
year = "2021",
DOI = "https://doi.org/10.1142/S0219467821400052",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Mon Dec 27 07:10:56 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400052",
abstract = "In this paper, we discuss the classification of the
brain tumor in Magnetic Resonance Imaging (MRI) images
using the U-Net model, then evaluate parameters that
indicate the performance of the model. We also discuss
the extraction of the tumor region from brain image and
description of the tumor regarding its position and
size. Here, we consider the case of Gliomas, one of the
types of brain tumors, which occur in common and can be
fatal depending on their position and growth. U-Net is
a model of Convolutional Neural Network (CNN) which has
U-shaped architecture. MRI employs a non-invasive
technique and can very well provide soft-tissue
contrast and hence, for the detection and description
of the brain tumor, this imaging method can be
beneficial. Manual delineation of tumors from brain MRI
is laborious, time-consuming and can vary from expert
to expert. Our work forms a computer aided technique
which is relatively faster and reproducible, and the
accuracy is very much on par with ground truth. The
results of the work can be used for treatment planning
and further processing related to storage or
transmission of images.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Deep Neural Networks for Medical
Image Detection, Segmentation, and Localization",
}
@Article{Shrivastava:2022:BTD,
author = "Neeraj Shrivastava and Jyoti Bharti",
title = "Breast Tumor Detection in {MR} Images Based on
Density",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467822500012",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500012",
abstract = "Breast cancer is dangerous in women. It is generally
found after the symptoms appear. Detecting the breast
cancer at an early stage and understanding the
treatment are the most important strategies to prevent
death from cancer. Generally, for detection of breast
cancer, breast Magnetic Resonance Image (MRI) takes
place. It is one of the best approaches to detect tumor
in women. In this research paper, a combination of
selection methods for seed region growing image
segmentation is suggested to detect breast tumor. The
suggested method has been divided into following parts:
First, the pre-processing of breast image is performed.
Second, the automatic threshold for binarization
process is calculated. Third, the number of seed points
and its position in the breast image are determined
automatically using density of pixels value. Fourth, a
method for calculation of threshold value is proposed
for the purpose of region creation in seed region
growing. For the evaluation purpose, the proposed
method was applied and tested on the RIDER MRI breast
dataset from National Biomedical Imaging Archive
(NBIA). After the test was performed, it was observed
that proposed algorithm gives 90\% accuracy, 88\% True
Negative Fraction, 91\% True Positive Fraction, 10\%
Misclassification Rate, 94\% Precision and 86\%
Relative Overlap which is better than other existing
methods. It not only gives better evaluation measure
but also provides segmentation method for multiple
tumor detection.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Padhy:2022:CLR,
author = "Rajalaxmi Padhy and Shashwat Sourav Swain and Sanjit
Kumar Dash and Jibitesh Mishra",
title = "Classification of Low-Resolution Satellite Images
Using Fractal Augmented Descriptors",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500024",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500024",
abstract = "Satellite imagery consists of highly complex spatial
features that make it difficult for traditional image
processing techniques to use them for classification
tasks. In this paper, we propose a novel method to use
these hidden fractal information that naturally exist
in these satellite images. We have designed a
fractal-based descriptor which generates a scale
invariant fractal image for easier fractal-based
pattern extraction and uses it as an added feature
vector that is combined with the original image and fed
into a VGG-16 deep learning architecture which
successfully classifies even low-resolution satellite
images with an f1-score of 0.78.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Suresha:2022:KDB,
author = "M. Suresha and D. S. Raghukumar and S. Kuppa",
title = "{Kumaraswamy} Distribution Based Bi-histogram
Equalization for Enhancement of Microscopic Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500036",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500036",
abstract = "Among all image enhancement techniques, histogram
equalization is the most used technique. However,
preserving brightness is the main issue, and it creates
a weird look by destroying its originality. This paper
proposes a new method that has command on the
brightness issue of histogram equalization to enhance
the quality of microscopic images. The method splits
the histogram of each color channel into two
sub-histograms based on their mean as the threshold and
supplanting their cumulative distribution with
Kumaraswamy distribution. The proposed method is tested
with color microscopic images of cancer-affected lymph
nodes gathered from Biological Image Repository IICBU,
and objective and subjective assessments confirm that
the proposed approach performs more efficiently
compared to other state-of-the-art methods.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Vandana:2022:ARB,
author = "Vandana and Navdeep Kaur",
title = "Analytical Review of Biometric Technology Employing
Vivid Modalities",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500048",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500048",
abstract = "The digitalization has been challenged with the
security and privacy aspects in each and every field.
In addition to numerous authentication methods,
biometrics has been popularized as it relies on one's
individual behavioral and physical characters. In this
context, numerous unimodal and multimodal biometrics
have been proposed and tested in the last decade. In
this paper, authors have presented a comprehensive
survey of the existing biometric systems while
highlighting their respective challenges, advantage and
limitations. The paper also discusses the present
biometric technology market value, its scope, and
practical applications in vivid sectors. The goal of
this review is to offer a compact outline of various
advances in biometrics technology with potential
applications using unimodal and multimodal
bioinformatics are discussed that would prove to offer
a base for any biometric-based future research.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jameel:2022:GSI,
author = "Samer Kais Jameel and Jafar Majidpour",
title = "Generating Spectrum Images from Different Types ---
Visible, Thermal, and Infrared Based on Autoencoder
Architecture {(GVTI-AE)}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S021946782250005X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782250005X",
abstract = "Recently, numerous challenging problems have existed
for transforming different image types (thermal
infrared (TIR), visible spectrum, and near-infrared
(NIR)). Other types of cameras may lack the ability and
features of certain types of frequently-used cameras
that produce different types of images. Based on camera
features, different applications might emerge from
observing a scenario under specific conditions
(darkness, fog, night, day, and artificial light). We
need to jump from one field to another to understand
the scenario better. This paper proposes a fully
automatic model (GVTI-AE) to manipulate the
transformation into different types of vibrant,
realistic images using the AutoEncoder method, which
requires neither pre-nor post-processing or any user
input. The experiments carried out using the GVTI-AE
model showed that the perceptually realistic results
produced in the widely available datasets (Tecnocampus
Hand Image Database, Carl dataset, and IRIS
Thermal/Visible Face Database).",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{BenSalah:2022:FES,
author = "Marwa {Ben Salah} and Ameni Yengui and Mahmoud Neji",
title = "Feature Extraction and Selection in Archaeological
Images for Automatic Annotation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500061",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500061",
abstract = "In this paper, we present two steps in the process of
automatic annotation in archeological images. These
steps are feature extraction and feature selection. We
focus our research on archeological images which are
very much studied in our days. It presents the most
important steps in the process of automatic annotation
in an image. Feature extraction techniques are applied
to get the feature that will be used in classifying and
recognizing the images. Also, the selection of
characteristics reduces the number of unattractive
characteristics. However, we reviewed various images of
feature extraction techniques to analyze the
archaeological images. Each feature represents one or
more feature descriptors in the archeological images.
We focus on the descriptor shape of the archaeological
objects extraction in the images using contour
method-based shape recognition of the monuments. So,
the feature selection stage serves to acquire the most
interesting characteristics to improve the accuracy of
the classification. In the feature selection section,
we present a comparative study between feature
selection techniques. Then we give our proposal of
application of methods of selection of the
characteristics of the archaeological images. Finally,
we calculate the performance of two steps already
mentioned: the extraction of characteristics and the
selection of characteristics.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gogineni:2022:TSP,
author = "Rajesh Gogineni and Dhara J. Sangani",
title = "A Two-Stage {PAN}-Sharpening Algorithm Based on Sparse
Representation for Spectral Distortion Reduction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500073",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500073",
abstract = "Inspite of technological advancement, inherent
processing capability of current age sensors limits the
desired details in the acquired image for variety of
remote sensing applications. Pan-sharpening is a
prominent scheme to integrate the essential spatial
details inferred from panchromatic (PAN) image and the
desired spectral information of multispectral (MS)
image. This paper presents an effective two-stage
pan-sharpening method to produce high resolution
multispectral (HRMS) image. The proposed method is
based on the premise that the HRMS image can be
formulated as an amalgam of spectral and spatial
components. The spectral components are estimated by
processing the interpolated MS image with a filter
approximated with modulation transfer function (MTF) of
the sensor. Sparse representation theory is adapted to
construct the spatial components. The high-frequency
details extracted from the PAN image and its low
resolution variant are utilized to construct dual
dictionaries. The dictionaries are jointly learned by
an efficient training algorithm to enhance the
adaptability. The hypothesis of sparse coefficients
invariance over scales is also incorporated to reckon
the appropriate spatial information. Further, an
iterative filtering mechanism is developed to enhance
the quality of fused image. Four distinct datasets
generated from QuickBird, IKONOS, Pl{\'e}iades and
WorldView-2 sensors are used for experimentation. The
comprehensive assessment at reduced-scale and
full-scale persuade the effectiveness of proposed
method in the retention of spectral information and
intensification of the spatial details.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dhar:2022:SRR,
author = "Soumi Dhar and Shyamosree Pal",
title = "Surface Reconstruction: Roles in the Field of Computer
Vision and Computer Graphics",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500085",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500085",
abstract = "Surface Reconstruction is the most potent aspect of 3D
computer vision. It allows recapturing or imitating of
the shape of real objects. It also provides sufficient
knowledge regarding the mathematical foundation for
rendering applications which are widely used for
analyzing medical volume data, modeling, 3D interior
designing, architectural designing. In our paper, we
have mentioned various algorithms and approaches for
surface reconstruction and their applications.
Moreover, we have tried to emphasize the necessity of
surface reconstruction for solving image analysis
related problem.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kushwaha:2022:HAR,
author = "Arati Kushwaha and Ashish Khare and Manish Khare",
title = "Human Activity Recognition Algorithm in Video
Sequences Based on Integration of Magnitude and
Orientation Information of Optical Flow",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500097",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500097",
abstract = "Human activity recognition from video sequences has
emerged recently as pivotal research area due to its
importance in a large number of applications such as
real-time surveillance monitoring, healthcare, smart
homes, security, behavior analysis, and many more.
However, lots of challenges also exist such as
intra-class variations, object occlusion, varying
illumination condition, complex background, camera
motion, etc. In this work, we introduce a novel feature
descriptor based on the integration of magnitude and
orientation information of optical flow and histogram
of oriented gradients which gives an efficient and
robust feature vector for the recognition of human
activities for real-world environment. In the proposed
approach first we computed magnitude and orientation of
the optical flow separately then a local-oriented
histogram of magnitude and orientation of motion flow
vectors are computed using histogram of oriented
gradients followed by linear combination feature fusion
strategy. The resultant features are then processed by
a multiclass Support Vector Machine (SVM) classifier
for activity recognition. The experimental results are
performed over different publically available benchmark
video datasets such as UT interaction, CASIA, and
HMDB51 datasets. The effectiveness of the proposed
approach is evaluated in terms of six different
performance parameters such as accuracy, precision,
recall, specificity, F -measure, and Matthew's
correlation coefficient (MCC). To show the significance
of the proposed method, it is compared with the other
state-of-the-art methods. The experimental result shows
that the proposed method performs well in comparison to
other state-of-the-art methods.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ravikumar:2022:EMB,
author = "M. Ravikumar and B. J. Shivaprasad and D. S. Guru",
title = "Enhancement of {MRI} Brain Images Using Notch Filter
Based on Discrete Wavelet Transform",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500103",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500103",
abstract = "In this work, we have proposed Notch filter method to
enhance MRI brain images. The proposed method performs
better when compared with the existing methods from the
literature. The performance is evaluated using
quantitative measures like Michelon Contrast (MC),
entropy, Peak Signal-to-Noise Ratio (PSNR), Structure
Similarity Index Measurement (SSIM) and Absolute Mean
Brightness Error (AMBE), as a parameter on publically
available BRATS-2018 & 2019 dataset. Overall, the
proposed method performs well in comparison to the
other existing methods.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bhardwaj:2022:IAU,
author = "Anuj Bhardwaj and Vivek Singh Verma and Sandesh
Gupta",
title = "Image Authentication Using Block Truncation Coding in
Lifting Wavelet Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500115",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500115",
abstract = "Image watermarking is one of the most accepted
solutions protecting image authenticity. The method
presented in this paper not only provides the desired
outcome also efficient in terms of memory requirements
and preserving image characteristics. This scheme
effectively utilizes the concepts of block truncation
coding (BTC) and lifting wavelet transform (LWT). The
BTC method is applied to observe the binary watermark
image corresponding to its gray-scale image. Whereas,
the LWT is incorporated to transform the cover image
from spatial coordinates to corresponding transform
coordinates. In this, a quantization-based approach for
watermark bit embedding is applied. And, the extraction
of binary watermark data from the attacked watermarked
image is based on adaptive thresholding. To show the
effectiveness of the proposed scheme, the experiment
over different benchmark images is performed. The
experimental results and the comparison with
state-of-the-art schemes depict not only the good
imperceptibility but also high robustness against
various attacks.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ehsaeyan:2022:SDC,
author = "Ehsan Ehsaeyan and Alireza Zolghadrasli",
title = "A Study on {Darwinian} Crow Search Algorithm for
Multilevel Thresholding",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500127",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500127",
abstract = "Multilevel thresholding is a basic method in image
segmentation. The conventional image multilevel
thresholding algorithms are computationally expensive
when the number of decomposed segments is high. In this
paper, a novel and powerful technique is suggested for
Crow Search Algorithm (CSA) devoted to segmentation
applications. The main contribution of our work is to
adapt Darwinian evolutionary theory with heuristic CSA.
First, the population is divided into specified groups
and each group tries to find better location in the
search space. A policy of encouragement and punishment
is set on searching agents to avoid being trapped in
the local optimum and premature solutions. Moreover, to
increase the convergence rate of the proposed method, a
gray-scale map is applied to out-boundary agents. Ten
test images are selected to measure the ability of our
algorithm, compared with the famous procedure, energy
curve method. Two popular entropies i.e. Otsu and Kapur
are employed to evaluate the capability of the
introduced algorithm. Eight different search algorithms
are implemented and compared to the introduced method.
The obtained results show that our method, compared
with the original CSA, and other heuristic search
methods, can extract multi-level thresholding more
efficiently.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Boucetta:2022:BAU,
author = "Aldjia Boucetta and Leila Boussaad",
title = "Biometric Authentication Using Finger-Vein Patterns
with Deep-Learning and Discriminant Correlation
Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "01",
pages = "??--??",
month = jan,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500139",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Feb 9 07:11:50 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500139",
abstract = "Finger-vein identification, a biometric technology
that uses vein patterns in the human finger to identify
people. In recent years, it has received increasing
attention due to its tremendous advantages compared to
fingerprint characteristics. Moreover,
Deep-Convolutional Neural Networks (Deep-CNN) appeared
to be highly successful for feature extraction in the
finger-vein area, and most of the proposed works focus
on new Convolutional Neural Network (CNN) models, which
require huge databases for training, a solution that
may be more practicable in real world applications, is
to reuse pretrained Deep-CNN models. In this paper, a
finger-vein identification system is proposed, which
uses Squeezenet pretrained Deep-CNN model as feature
extractor from the left and the right finger vein
patterns. Then, combines this Deep-based features by
using a feature-level Discriminant Correlation Analysis
(DCA) to reduce feature dimensions and to give the most
relevant features. Finally, these composite feature
vectors are used as input data for a Support Vector
Machine (SVM) classifier, in an identification stage.
This method is tested on two widely available finger
vein databases, namely SDUMLA-HMT and FV-USM.
Experimental results show that the proposed finger vein
identification system achieves significant high mean
accuracy rates.",
acknowledgement = ack-nhfb,
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gajabe:2022:SKB,
author = "Rajashree Gajabe and Syed Taqi Ali",
title = "Secret Key-Based Image Steganography in Spatial
Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467822500140",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500140",
abstract = "Day by day, the requirement for secure communication
among users is rising in a digital world, to protect
the message from the undesirable users. Steganography
is a methodology that satisfies the user's necessity of
secure communication by inserting a message into
different formats. This paper proposes a secret
key-based image steganography to secure the message by
concealing the grayscale image inside a cover image.
The proposed technique shares the 20 characters long
secret key between two clients where the initial eight
characters of a secret key are utilized for bit
permutation of characters and pixels while the last 12
characters of secret key decide the encryption keys and
position of pixels of a grayscale image into the cover.
The grayscale image undergoes operation such as
encryption and chaotic baker followed by its hiding in
a cover to form a stego image. The execution of the
proposed strategy is performed on Matlab 2018. It shows
that the proposed approach manages to store the maximum
message of size 16 KB into the cover of size
256{\texttimes}256. The image quality of stego images
has been evaluated using PSNR, MSE. For a full payload
of 16 KB, PSNR is around 51 dB to 53 dB which is
greater than satisfactory PSNR.",
acknowledgement = ack-nhfb,
articleno = "2250014",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gagaoua:2022:HMB,
author = "Meriem Gagaoua and Hamza Ghilas and Abdelkamel Tari
and Mohamed Cheriet",
title = "Histogram of Marked Background {(HMB)} Feature
Extraction Method for {Arabic} Handwriting
Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500152",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500152",
abstract = "Features extraction is one of the most important steps
in handwriting recognition systems. In this paper, we
propose a novel features extraction method, which is
adapted to the complex nature of Arabic handwriting.
The proposed feature called histogram of marked
background (HMB) is not considering only ink pixels in
a text image, but also uses the background of the
image. Each background pixel in the text image was
marked according to the repartition of ink pixels in
its neighborhood. Feature vectors are extracted by
computing histograms from the marked images. Hidden
Markov models (HMMs) with Hidden Markov model toolkit
(HTK) were used in the recognition process. The
experiments were performed on two datasets: IBN SINA
database of historical Arabic documents and Isolated
Farsi Handwritten Character Database (IFHCDB). The
proposed feature in this study produced efficient and
promising results for Arabic handwriting recognition,
for both isolated characters and for historical
documents.",
acknowledgement = ack-nhfb,
articleno = "2250015",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rashwan:2022:MFW,
author = "Shaheera Rashwan and Walaa Sheta",
title = "A Metaheuristics Framework for Weighted Multi-band
Image Fusion",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500164",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500164",
abstract = "The main objective of hyper/multispectral image fusion
is producing a composite color image that allows for an
appropriate visualization of the relevant spatial and
spectral information. In this paper, we propose a
general framework for spectral weighting-based image
fusion. The proposed methodology relies on weight
updates conducted using nature-inspired algorithms and
a goodness-of-fit criterion defined as the average root
mean square error. Simulations on four public data sets
and a recent Landsat 8 image of Brullus Lake, Egypt, as
an area of study prove the efficiency of the proposed
framework. The purpose of the study is to present a
framework of multi-band image fusion that produces a
fused image of high quality to be further used in
computer processing and the results show that the image
produced by the presented framework has the highest
quality compared with some of the state-of-the art
algorithms. To prove the increase in the image quality,
we used general quality metrics such as Universal Image
Quality Index, Mutual Information, the Variance and
Information Measure.",
acknowledgement = ack-nhfb,
articleno = "2250016",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Vaidya:2022:HEM,
author = "Bhaumik Vaidya and Chirag Paunwala",
title = "Hardware Efficient Modified {CNN} Architecture for
Traffic Sign Detection and Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500176",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500176",
abstract = "Traffic sign recognition is a vital part for any
driver assistance system which can help in making
complex driving decision based on the detected traffic
signs. Traffic sign detection (TSD) is essential in
adverse weather conditions or when the vehicle is being
driven on the hilly roads. Traffic sign recognition is
a complex computer vision problem as generally the
signs occupy a very small portion of the entire image.
A lot of research is going on to solve this issue
accurately but still it has not been solved till the
satisfactory performance. The goal of this paper is to
propose a deep learning architecture which can be
deployed on embedded platforms for driver assistant
system with limited memory and computing resources
without sacrificing on detection accuracy. The
architecture uses various architectural modification to
the well-known Convolutional Neural Network (CNN)
architecture for object detection. It uses a trainable
Color Transformer Network (CTN) with the existing CNN
architecture for making the system invariant to
illumination and light changes. The architecture uses
feature fusion module for detecting small traffic signs
accurately. In the proposed work, receptive field
calculation is used for choosing the number of
convolutional layer for prediction and the right scales
for default bounding boxes. The architecture is
deployed on Jetson Nano GPU Embedded development board
for performance evaluation at the edge and it has been
tested on well-known German Traffic Sign Detection
Benchmark (GTSDB) and Tsinghua-Tencent 100k dataset.
The architecture only requires 11 MB for storage which
is almost ten times better than the previous
architectures. The architecture has one sixth
parameters than the best performing architecture and 50
times less floating point operations per second
(FLOPs). The architecture achieves running time of 220
ms on desktop GPU and 578 ms on Jetson Nano which is
also better compared to other similar implementation.
It also achieves comparable accuracy in terms of mean
average precision (mAP) for both the datasets.",
acknowledgement = ack-nhfb,
articleno = "2250017",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bommisetty:2022:CBV,
author = "Reddy Mounika Bommisetty and Ashish Khare and Manish
Khare and P. Palanisamy",
title = "Content-Based Video Retrieval Using Integration of
Curvelet Transform and Simple Linear Iterative
Clustering",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500188",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500188",
abstract = "Video is a rich information source containing both
audio and visual information along with motion
information embedded in it. Applications such as
e-learning, live TV, video on demand, traffic
monitoring, etc. need an efficient video retrieval
strategy. Content-based video retrieval and superpixel
segmentation are two diverse application areas of
computer vision. In this work, we are presenting an
algorithm for content-based video retrieval with help
of Integration of Curvelet transform and Simple Linear
Iterative Clustering (ICTSLIC) algorithm. Proposed
algorithm consists of two steps: off line processing
and online processing. In offline processing, keyframes
of the database videos are extracted by employing
features: Pearson Correlation Coefficient (PCC) and
color moments (CM) and on the extracted keyframes
superpixel generation algorithm ICTSLIC is applied. The
superpixels generated by applying ICTSLIC on keyframes
are used to represent database videos. On other side,
in online processing, ICTSLIC superpixel segmentation
is applied on query frame and the superpixels generated
by segmentation are used to represent query frame. Then
videos similar to query frame are retrieved through
matching done by calculation of Euclidean distance
between superpixels of query frame and database
keyframes. Results of the proposed method are
irrespective of query frame features such as camera
motion, object's pose, orientation and motion due to
the incorporation of ICTSLIC superpixels as base
feature for matching and retrieval purpose. The
proposed method is tested on the dataset comprising of
different categories of video clips such as animations,
serials, personal interviews, news, movies and songs
which is publicly available. For evaluation, the
proposed method randomly picks frames from database
videos, instead of selecting keyframes as query frames.
Experiments were conducted on the developed dataset and
the performance is assessed with different parameters
Precision, Recall, Jaccard Index, Accuracy and
Specificity. The experimental results shown that the
proposed method is performing better than the other
state-of-art methods.",
acknowledgement = ack-nhfb,
articleno = "2250018",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Khan:2022:MPS,
author = "Rafflesia Khan and Rameswar Debnath",
title = "Morphology Preserving Segmentation Method for Occluded
Cell Nuclei from Medical Microscopy Image",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S021946782250019X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782250019X",
abstract = "Nowadays, image segmentation techniques are being used
in many medical applications such as tissue culture
monitoring, cell counting, automatic measurement of
organs, etc., for assisting doctors. However,
high-level segmentation results cannot be obtained
without manual annotation or prior knowledge for high
variability, noise and other imaging artifacts in
medical images. Furthermore, unstable and continuously
changing characteristics of all human cells, tissues
and organs manipulate training-based segmentation
methods. Detecting appropriate contour of a region of
interest and single cells from overlapping condition
are extremely challenging. In this paper, we aim for a
model that can detect biological structure (e.g. cell
nuclei and lung contour) with their proper morphology
even in overlapping or occluded condition without
manual annotation or prior knowledge. We have
introduced a new optimal approach for automatic medical
image region segmentation. The method first clearly
focuses the boundaries of all object regions in a
microscopy image. Then it detects the areas by
following their contours. Our model is capable of
detecting and segmenting object regions from medial
image using less computation effort. Our experimental
results prove that our model provides better detection
on several datasets of different types of medical data
and ensures more than 98\% segmentation rate in the
case of densely connected regions.",
acknowledgement = ack-nhfb,
articleno = "2250019",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tanveer:2022:EIP,
author = "Muhammad Tanveer and Tariq Shah and Asif Ali and
Dawood Shah",
title = "An Efficient Image Privacy-Preserving Scheme Based On
Mixed Chaotic Map and Compression",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500206",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500206",
abstract = "In the digital modern era, multimedia security has
turned into a major concern by the rapid growth of
network technologies and digital communications.
Accordingly, from the last few decades, the application
of nonlinear dynamics and chaotic phenomena for
multimedia data security earn significant attention. In
this paper, an efficient image-encryption technique
based on a two-dimensional (2D) chaotic system combine
with the finite field of the specific order is
introduced. The proposed scheme consists of four
modules which are the separation of bits, compression,
2D chaotic map, and small S-boxes. Initially, the
suggested scheme separates the pixels of the image into
the least significant bits (LSB) and the most
significant bits (MSB). Subsequently, the compression
algorithm on these separated bits is applied and
instantly transformed the MSB of the image into LSB.
The key objective of the first module is to minimize
the range of the pixel value up to eight times less
than the original image, which consequently reduces the
time complexity of the scheme. In the end, a 2D chaotic
map is used to reshuffle the bytes to interrupt the
internal correlation amongst the pixels of the image.
At the tail end, the small S-boxes have been used to
substitute the permuted image. The significance of
small S-boxes plays a vital role to maintain the
optimum security level, prevent computational effort,
and reduced time complexity. The result of the
suggested encryption system is tailor-made for
instantaneous communication.",
acknowledgement = ack-nhfb,
articleno = "2250020",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chaudhari:2022:IFU,
author = "Chaitrali Prasanna Chaudhari and Satish Devane",
title = "Improved Framework using Rider Optimization Algorithm
for Precise Image Caption Generation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500218",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500218",
abstract = "``Image Captioning is the process of generating a
textual description of an image''. It deploys both
computer vision and natural language processing for
caption generation. However, the majority of the image
captioning systems offer unclear depictions regarding
the objects like ``man'', ``woman'', ``group of
people'', ``building'', etc. Hence, this paper intends
to develop an intelligent-based image captioning model.
The adopted model comprises of few steps like word
generation, sentence formation, and caption generation.
Initially, the input image is subjected to the Deep
learning classifier called Convolutional Neural Network
(CNN). Since the classifier is already trained in the
relevant words that are related to all images, it can
easily classify the associated words of the given
image. Further, a set of sentences is formed with the
generated words using Long-Short Term Memory (LSTM)
model. The likelihood of the formed sentences is
computed using the Maximum Likelihood (ML) function,
and the sentences with higher probability are taken,
which is further used for generating the visual
representation of the scene in terms of image caption.
As a major novelty, this paper aims to enhance the
performance of CNN by optimally tuning its weight and
activation function. This paper introduces a new
enhanced optimization algorithm Rider with Randomized
Bypass and Over-taker update (RR-BOU) for this optimal
selection. In the proposed RR-BOU is the enhanced
version of the Rider Optimization Algorithm (ROA).
Finally, the performance of the proposed captioning
model is compared over other conventional models with
respect to statistical analysis.",
acknowledgement = ack-nhfb,
articleno = "2250021",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hamroun:2022:MVI,
author = "Mohamed Hamroun and Karim Tamine and Beno{\^\i}t
Crespin",
title = "Multimodal Video Indexing {(MVI)}: a New Method Based
on Machine Learning and Semi-Automatic Annotation on
Large Video Collections",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S021946782250022X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782250022X",
abstract = "Indexing video by the concept is one of the most
appropriate solutions for such problems. It is based on
an association between a concept and its corresponding
visual sound, or textual features. This kind of
association is not a trivial task. It requires
knowledge about the concept and its context. In this
paper, we investigate a new concept detection approach
to improve the performance of content-based multimedia
documents retrieval systems. To achieve this goal, we
are going to tackle the problem from different plans
and make four contributions at various stages of the
indexing process. We propose a new method for
multimodal indexation based on (i) a new weakly
supervised semi-automatic method based on the genetic
algorithm (ii) the detection of concepts from the text
in the videos (iii) the enrichment of the basic
concepts thanks to the usage of our method DCM.
Subsequently, the semantic and enriched concepts allow
a better multimodal indexation and the construction of
an ontology. Finally, the different contributions are
tested and evaluated on a large dataset (TRECVID
2015).",
acknowledgement = ack-nhfb,
articleno = "2250022",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shivaprasad:2022:ABT,
author = "B. J. Shivaprasad and M. Ravikumar and D. S. Guru",
title = "Analysis of Brain Tumor Using {MR} Images: a Brief
Survey",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500231",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500231",
abstract = "In this paper, we have discussed in detail about
detection and extraction of brain tumor from MRI
technique, where the importance of using MRI is also
highlighted. Various features extraction methods and
classifiers are explained in brain tumor segmentation.
This paper mainly focuses on challenges involved in
brain tumor analysis, which is helpful for researchers
and those who are interested to carry out their
research on this topic.",
acknowledgement = ack-nhfb,
articleno = "2250023",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Khadilkar:2022:CCD,
author = "Samrat Pundalik Khadilkar",
title = "Colon Cancer Detection Using Hybrid Features and
Genetically Optimized Neural Network Classifier",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500243",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500243",
abstract = "Computer-assisted colon cancer detection on the
histopathological images has become a tedious task due
to its shape characteristics and other biological
properties. The images acquired through the
histopathological microscope may vary in magnifications
for better visibility. This may change the
morphological properties and hence an automated
magnification independent colon cancer detection system
is essential. The manual diagnosis of colon biopsy
images is subjective, sluggish, laborious leading to
nonconformity between histopathologists due to visual
evaluation at various microscopic magnifications.
Automatic detection of colon across image
magnifications is challenging due to many aspects like
tailored segmentation and varying features. This
demands techniques that take advantage of the textural,
color, and geometric properties of colon tissue. This
work exhibits a segmentation approach based on the
morphological features derived from the segmented
region. Gabor Wavelet, Harris Corner, and DWT-LBP
coefficients are extracted as it should not be
dependent on the spatial domain with respect to the
magnification. These features are fed to the
Genetically Optimized Neural Network classifier to
classify them as normal and malignant ones. Here, the
genetic algorithm is used to learn the best
hyper-parameters for a neural network.",
acknowledgement = ack-nhfb,
articleno = "2250024",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sridhar:2022:PAT,
author = "S. Sridhar and A. Kalaivani",
title = "Performance Analysis of Two-Stage Iterative Ensemble
Method over Random Oversampling Methods on Multiclass
Imbalanced Datasets",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500255",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500255",
abstract = "Data imbalance occurring among multiclass datasets is
very common in real-world applications. Existing
studies reveal that various attempts were made in the
past to overcome this multiclass imbalance problem,
which is a severe issue related to the typical
supervised machine learning methods such as
classification and regression. But, still there exists
a need to handle the imbalance problem efficiently as
the datasets include both safe and unsafe minority
samples. Most of the widely used oversampling
techniques like SMOTE and its variants face challenges
in replicating or generating the new data instances for
balancing them across multiple classes, particularly
when the imbalance is high and the number of rare
samples count is too minimal thus leading the
classifier to misclassify the data instances. To lessen
this problem, we proposed a new data balancing method
namely a two-stage iterative ensemble method to tackle
the imbalance in multiclass environment. The proposed
approach focuses on the rare minority sample's
influence on learning from imbalanced datasets and the
main idea of the proposed approach is to balance the
data without any change in class distribution before it
gets trained by the learner such that it improves the
learner's learning process. Also, the proposed approach
is compared against two widely used oversampling
techniques and the results reveals that the proposed
approach shows a much significant improvement in the
learning process among the multiclass imbalanced
data.",
acknowledgement = ack-nhfb,
articleno = "2250025",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jayaraman:2022:MFP,
author = "Kumaran @ Kumar Jayaraman and Koganti Srilakshmi and
Sasikala Jayaraman",
title = "Modified Flower Pollination-based Segmentation of
Medical Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "02",
pages = "??--??",
month = apr,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500267",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri May 6 07:27:02 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500267",
abstract = "This paper presents a modified flower
pollination-based method for performing multilevel
segmentation of medical images. The flower
pollination-based optimization (FPO) models the
pollination process of flowers. Bees serve a major role
in the pollination activity of flowers and they
memorize and recognize the best flowers producing large
pollens of nectar. Such memorizing ability of bees is
adapted in the FPO for improving the exploration
ability of the algorithm. Besides, the mechanism of
avoiding predators by pollinators is also included in
the modified FPO (MFPO) for getting away from
sub-optimal traps. The medical image segmentation
problem is transformed into an optimization problem and
solved using the modified FPO (MFPO). The method
explores for optimal thresholds in the problem space of
the given medical image. The segmented images are
presented for showing the superior performance of the
proposed method.",
acknowledgement = ack-nhfb,
articleno = "2250026",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Patel:2022:AET,
author = "Alpesh M. Patel and Anil Suthar",
title = "{AdaBoosted} Extra Trees Classifier for Object-Based
Multispectral Image Classification of Urban Fringe
Area",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "03",
pages = "??--??",
month = may,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467821400064",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue May 31 06:44:45 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400064",
abstract = "In the past decade, it is proven that satellite image
classification using an object-based technique is
better than the standard pixel-based technique. With
the increasing need for classifying multispectral
satellite images for urban planning, the accuracy of
the classification becomes a significant performance
parameter. Object-based classification (OBC) is a
technique in which group of pixels having similar
spectral properties, called objects, are generated
using image segmentation and then these objects are
classified based on their attributes. In this paper,
the combination of a multiclass AdaBoost algorithm with
extra trees classifier (ETC) is proposed with higher
prediction accuracy for the OBC of the urban fringe
area. The performance of the AdaBoost algorithm is
found to be better in terms of classification accuracy
than benchmarked SVM and RF classifiers for OBC. These
classification methods were applied to IRS-R2 LISS IV
data. The AdaBoosted extra trees classifier (ABETC) has
demonstrated the highest accuracy with overall accuracy
(OA) of 88.47\% and a kappa coefficient of 0.85. The
computational time of the ABETC is found to be much
smaller than the RF algorithm. In detail, the
sensitivity of the classifiers was investigated using
stratified random sampling with various sample sizes.",
acknowledgement = ack-nhfb,
articleno = "2140006",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Innovations in Image Processing using
Machine Learning",
}
@Article{Hans:2022:HBB,
author = "Rahul Hans and Harjot Kaur",
title = "Hybrid Biogeography-Based Optimization and Genetic
Algorithm for Feature Selection in Mammographic Breast
Density Classification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "03",
pages = "??--??",
month = may,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467821400076",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue May 31 06:44:45 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400076",
abstract = "It can be acknowledged from the literature that the
high density of breast tissue is a root cause for the
escalation of breast cancer among the women, imparting
its prime role in Cancer Death among women. Moreover,
in this era where computer-aided diagnosis systems have
become the right hand of the radiologists, the
researchers still find room for improvement in the
feature selection techniques. This research aspires to
propose hybrid versions of Biogeography-Based
Optimization and Genetic Algorithm for feature
selection in Breast Density Classification, to get rid
of redundant and irrelevant features from the dataset;
along with it to achieve the superior classification
accuracy or to uphold the same accuracy with lesser
number of features. For experimentation, 322 mammogram
images from mini-MIAS database are chosen, and then
Region of Interests (ROI) of seven different sizes are
extracted to extract a set of 45 texture features
corresponding to each ROI. Subsequently, the proposed
algorithms are used to extract an optimal subset of
features from the hefty set of features corresponding
to each ROI. The results indicate the outperformance of
the proposed algorithms when results were compared with
some of the other nature-inspired metaheuristic
algorithms using various parameters.",
acknowledgement = ack-nhfb,
articleno = "2140007",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Innovations in Image Processing using
Machine Learning",
}
@Article{Sagayam:2022:RHG,
author = "K. Martin Sagayam and A. Diana Andrushia and Ahona
Ghosh and Omer Deperlioglu and Ahmed A. Elngar",
title = "Recognition of Hand Gesture Image Using Deep
Convolutional Neural Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "03",
pages = "??--??",
month = may,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467821400088",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue May 31 06:44:45 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400088",
abstract = "In recent technology, there is tremendous growth in
computer applications that highlight human--computer
interaction (HCI), such as augmented reality (AR), and
Internet of Things (IoT). As a consequence, hand
gesture recognition was highlighted as a very
up-to-date research area in computer vision. The body
language is a vital method to communicate between
people, as well as emphasis on voice messages, or as a
complete message on its own. Thus, automatic hand
gestures recognition systems can be used to increase
human--computer interaction. Therefore, many approaches
for hand gesture recognition systems have been
designed. However, most of these methods include hybrid
processes such as image pre-processing, segmentation,
and classification. This paper describes how to create
hand gesture model easily and quickly with a well-tuned
deep convolutional neural network. Experiments were
performed using the Cambridge Hand Gesture data set for
illustration of success and efficiency of the
convolutional neural network. The accuracy was achieved
as 96.66\%, where sensitivity and specificity were
found to be 85\% and 98.12\%, respectively, according
to the average values obtained at the end of 20 times
of operation. These results were compared with the
existing works using the same dataset and it was found
to have higher values than the hybrid methods.",
acknowledgement = ack-nhfb,
articleno = "2140008",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Innovations in Image Processing using
Machine Learning",
}
@Article{Singh:2022:EMD,
author = "Swati Singh and Sheifali Gupta and Ankush Tanta and
Rupesh Gupta",
title = "Extraction of Multiple Diseases in Apple Leaf Using
Machine Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "03",
pages = "??--??",
month = may,
year = "2022",
DOI = "https://doi.org/10.1142/S021946782140009X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue May 31 06:44:45 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782140009X",
abstract = "This paper proposes a novel algorithm of segmentation
of diseased part in apple leaf images. In
agriculture-based image processing, leaf diseases
segmentation is the main processing task for region of
interest extraction. It is also extremely important to
segment the plant leaf from the background in case on
live images. Automated segmentation of plant leaves
from the background is a common challenge in the
processing of plant images. Although numerous methods
have been proposed, still it is tough to segment the
diseased part of the leaf from the live leaf images
accurately by one particular method. In the proposed
work, leaves of apple having different background have
been segmented. Firstly, the leaves have been enhanced
by using Brightness-Preserving Dynamic Fuzzy Histogram
Equalization technique and then the extraction of
diseased apple leaf part is done using a novel
extraction algorithm. Real-time plant leaf database is
used to validate the proposed approach. The results of
the proposed novel methodology give better results when
compared to existing segmentation algorithms. From the
segmented apple leaves, color and texture features are
extracted which are further classified as marsonina
coronaria or apple scab using different machine
learning classifiers. Best accuracy of 96.4\% is
achieved using K nearest neighbor classifier.",
acknowledgement = ack-nhfb,
articleno = "2140009",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Innovations in Image Processing using
Machine Learning",
}
@Article{Gupta:2022:SSC,
author = "Anuj Kumar Gupta and Manvinder Sharma and Ankit Sharma
and Vikas Menon",
title = "A Study on {SARS-CoV-2 (COVID-19)} and Machine
Learning Based Approach to Detect {COVID-19} Through
{X}-Ray Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "03",
pages = "??--??",
month = may,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467821400106",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue May 31 06:44:45 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400106",
abstract = "From origin in Wuhan city of China, a highly
communicable and deadly virus is spreading in the
entire world and is known as COVID-19. COVID-19 is a
new species of coronavirus which is affecting
respiratory system of human. The virus is known as
severe acute respiratory syndrome (SARS) coronavirus 2
abbreviated as SARS-CoV-2 and generally known as
coronavirus disease COVID-19. This is growing day by
day in countries. The symptoms include fever, cough and
difficulty in breathing. As there is no vaccine made
for this virus and COVID-19 tests are not readily
available, this is causing panic. Various Artificial
Intelligence-based algorithms and frameworks are being
developed to detect this virus, but it has not been
tested. People are taking advantages of others by
providing duplicate COVID-19 test kits. A work is
carried out with deep learning to detect presence of
COVID 19. With the use of Convolutional Neural
networks, the model is trained with dataset of COVID-19
positive and negative X-Rays. The accuracy of training
model is 99\% and the confusion matrix shows 98\%
values that are predicted truly. Hence, the model is
able to detect the presence of COVID-19.",
acknowledgement = ack-nhfb,
articleno = "2140010",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Innovations in Image Processing using
Machine Learning",
}
@Article{Rani:2022:SIP,
author = "Rajneesh Rani and Renu Dhir and Deepti Kakkar and
Nonita Sharma",
title = "Script Identification for Printed and Handwritten
{Indian} Documents: an Empirical Study of Different
Feature Classifier Combinations",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "03",
pages = "??--??",
month = may,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467821400118",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue May 31 06:44:45 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400118",
abstract = "The identification of script in a document page image
is the first step for an OCR system processing
multi-script documents. In this
multilingual/multiscript world, document processing
systems relying on the OCR that need human involvement
to select the appropriate OCR package is definitely
undesirable and inefficient. The development of robust
and efficient methods for automatic script
identification of a document is a subject of major
importance for automatic document processing in a
multilingual/multiscript environment. Thus, the basic
objective is to come up with some intuitive methods
having straightforward implementation without
compromising with efficiency. The aim of this work is
to evaluate state-of-the-art feature extraction and
classification techniques in the field of automatic
script identification of printed and handwritten
documents and to propose the best combination for the
same.",
acknowledgement = ack-nhfb,
articleno = "2140011",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Innovations in Image Processing using
Machine Learning",
}
@Article{Kaur:2022:DAD,
author = "Swapandeep Kaur and Sheifali Gupta and Swati Singh and
Isha Gupta",
title = "Detection of {Alzheimer}'s Disease Using Deep
Convolutional Neural Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "03",
pages = "??--??",
month = may,
year = "2022",
DOI = "https://doi.org/10.1142/S021946782140012X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue May 31 06:44:45 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782140012X",
abstract = "Alzheimer's disease (AD) is a disease that gradually
develops and causes degeneration of the cells of the
brain. The leading cause of AD is dementia that results
in a person's inability to work independently. In the
early stages of AD, a person forgets recent
conversations or the occurrence of an event. In the
later stages, there could be severe loss of memory such
that the person is not able to even perform everyday
tasks. The medicines currently available for AD may
improve its symptoms on a temporary basis in the early
stage of the disease. Since no treatment is available
for curing AD, its detection becomes extremely
important. As the clinical treatments are very
expensive, the need for automated diagnosis of AD is of
critical importance. In this paper, a deep learning
model based on a convolutional neural network has been
used and applied to four classes of images of AD that
is very mild demented, mild demented, average demented,
and non-demented. It was found that the moderate
demented class had the highest accuracy of 98.9\%, a
classification error rate of 0.01, and a specificity of
0.992. Also, the lowest false positive rate of 0.007
was obtained.",
acknowledgement = ack-nhfb,
articleno = "2140012",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Innovations in Image Processing using
Machine Learning",
}
@Article{Gore:2022:IBR,
author = "Sonal Gore and Jayant Jagtap",
title = "{IDH}-Based Radiogenomic Characterization of Glioma
Using Local Ternary Pattern Descriptor Integrated with
Radiographic Features and Random Forest Classifier",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "03",
pages = "??--??",
month = may,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467821400131",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue May 31 06:44:45 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400131",
abstract = "Mutations in family of Isocitrate Dehydrogenase (IDH)
gene occur early in oncogenesis, especially with glioma
brain tumor. Molecular diagnostic of glioma using
machine learning has grabbed attention to some extent
from last couple of years. The development of
molecular-level predictive approach carries great
potential in radiogenomic field. But more focused
efforts need to be put to develop such approaches. This
study aims to develop an integrative genomic diagnostic
method to assess the significant utility of textures
combined with other radiographic and clinical features
for IDH classification of glioma into IDH mutant and
IDH wild type. Random forest classifier is used for
classification of combined set of clinical features and
radiographic features extracted from axial T2-weighted
Magnetic Resonance Imaging (MRI) images of low- and
high-grade glioma. Such radiogenomic analysis is
performed on The Cancer Genome Atlas (TCGA) data of 74
patients of IDH mutant and 104 patients of IDH wild
type. Texture features are extracted using uniform,
rotation invariant Local Ternary Pattern (LTP) method.
Other features such as shape, first-order statistics,
image contrast-based, clinical data like age,
histologic grade are combined with LTP features for IDH
discrimination. Proposed random forest-assisted model
achieved an accuracy of 85.89\% with multivariate
analysis of integrated set of feature descriptors using
Glioblastoma and Low-Grade Glioma dataset available
with The Cancer Imaging Archive (TCIA). Such an
integrated feature analysis using LTP textures and
other descriptors can effectively predict molecular
class of glioma as IDH mutant and wild type.",
acknowledgement = ack-nhfb,
articleno = "2140013",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Innovations in Image Processing using
Machine Learning",
}
@Article{Singh:2022:OAI,
author = "Rishipal Singh and Rajneesh Rani and Aman Kamboj",
title = "An Optimized Approach for Intra-Class Fruit
Classification Using Deep Convolutional Neural
Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "03",
pages = "??--??",
month = may,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467821400143",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue May 31 06:44:45 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467821400143",
abstract = "Fruits classification is one of the influential
applications of computer vision. Traditional
classification models are trained by considering
various features such as color, shape, texture, etc.
These features are common for different varieties of
the same fruit. Therefore, a new set of features is
required to classify the fruits belonging to the same
class. In this paper, we have proposed an optimized
method to classify intra-class fruits using deep
convolutional layers. The proposed architecture is
capable of solving the challenges of a commercial
tray-based system in the supermarket. As the research
in intra-class classification is still in its infancy,
there are challenges that have not been tackled. So,
the proposed method is specifically designed to
overcome the challenges related to intra-class fruits
classification. The proposed method showcases an
impressive performance for intra-class classification,
which is achieved using a few parameters than the
existing methods. The proposed model consists of
Inception block, Residual connections and various other
layers in very precise order. To validate its
performance, the proposed method is compared with
state-of-the-art models and performs best in terms of
accuracy, loss, parameters, and depth.",
acknowledgement = ack-nhfb,
articleno = "2140014",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Innovations in Image Processing using
Machine Learning",
}
@Article{Zhang:2022:MDM,
author = "Qi Zhang",
title = "Medical Data and Mathematically Modeled Implicit
Surface Real-Rime Visualization in {Web} Browsers",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467822500279",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500279",
abstract = "Raycasting can display volumetric medical data in fine
details and reveal crucial inner imaging information,
while implicit surface is able to effectively model
complex objects with high flexibility, combining these
two rendering modalities together will provide
comprehensive information of the scene and has wide
applications in surgical simulation, image-guided
intervention, and medical training. However, medical
data rendering is based on texture depth at every
sampling point, while mathematically modeled implicit
surfaces do not have geometric information in texture
space. It is a challenging task to visualize both
physical scalar data and virtual implicit surfaces
simultaneously. To address this issue, in this paper,
we present a new dual-casting ray-based double modality
data rendering algorithm and web-based software
platform to visualize volumetric medical data and
implicit surface in the same browser. The algorithm
runs on graphics processing unit and casts two virtual
rays from camera to each pixel on the display panel,
where one ray travels through the mathematically
defined scene for implicit surface rendering and the
other one passes the 3D texture space for volumetric
data visualization. The proposed algorithm can detect
voxel depth information and algebraic surface models
along each casting ray and dynamically enhance the
visualized dual-modality data with the improved
lighting model and transparency adjustment function.
Moreover, auxiliary innovative techniques are also
presented to enhance the shading and rendering features
of interest. Our software platform can seamlessly
visualize volumetric medical data and implicit surfaces
in the same web browser over Internet.",
acknowledgement = ack-nhfb,
articleno = "2250027",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shah:2022:NRI,
author = "Said Khalid Shah",
title = "Non-Rigid Image Registration based on Parameterized
Surfaces: Application to {$3$D} Cardiac Motion Image
Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500280",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500280",
abstract = "This paper describes the Fast Radial Basis Function
(RBF) method for cardiac motion tracking in 3D CT using
non-rigid medical image registration based on
parameterized (regular) surfaces. The technique is a
point-based registration evaluation algorithm which
does register 3D MR or CT images in real time. We first
extract the surface of the whole heart 3D CT and its
contrast enhanced part (left ventricle (LV) blood
cavity) of each dataset with a semiautomatic contouring
and a fully-automatic triangulation method followed by
a global surface parameterization and optimization
algorithm. In second step, a set of registration
experiments are run to calculate the deformation field
at various phases of cardiac motion or cycle from CT
images, which results into significant deformation
during each phase of a cycle. The surface points of the
whole heart and LV are used to register the source
systole image to various diastole target images taken
at different phases during a heart beat. Our
registration accuracy improves with the increase in
number of salient feature points (i.e. optimized
parameterized surfaces) and it has no effect on the
speed of the algorithm (i.e. still less than a second).
The results show that the target registration error is
less than 3 mm (2.53) and the performance of the Fast
RBF algorithm is less than a second using a whole heart
CT dataset of a single patient taken over the course of
the entire cardiac cycle. At the end, the results for
recovery (or analysis) of bigger deformation in heart
CT images using the Fast RBF algorithm is compared to
the state-of-the-art Free Form Deformation (FFD)
registration technique. It is proved that the Fast RBF
method is performing better in speed and slightly less
accurate than the FFD (when measured in terms of NMI)
due to iterative nature of the latter.",
acknowledgement = ack-nhfb,
articleno = "2250028",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhang:2022:FBC,
author = "Geng Zhang and Qi Zhu and Jing Yang and Ruting Xu and
Zhiqiang Zhang and Daoqiang Zhang",
title = "Functional Brain Connectivity Hyper-Network Embedded
with Structural Information for Epilepsy Diagnosis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500292",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500292",
abstract = "Automatic diagnosis of brain diseases based on brain
connectivity network (BCN) classification is one of the
hot research fields in medical image analysis. The
functional brain network reflects the brain functional
activities and structural brain network reflects the
neural connections of the main brain regions. It is of
great significance to explore and explain the inner
mechanism of the brain and to understand and treat
brain diseases. In this paper, based on the graph
structure characteristics of brain network, the fusion
model of functional brain network and structural brain
network is designed to classify the diagnosis of brain
mental diseases. Specifically, the main work of this
paper is to use the Laplacian graph embed the
information of diffusion tensor imaging, which contains
the characteristics of structural brain networks, into
the functional brain network with hyper-order
functional connectivity information built based on
functional magnetic resonance data using the sparse
representation method, to obtain brain network with
both functional and structural characteristics.
Projection of the brain network and the two original
modes data to the kernel space respectively and then
classified by the multi-task learning method.
Experiments on the epilepsy dataset show that our
method has better performance than several
state-of-the-art methods. In addition, brain regions
and connections that are highly correlated with disease
revealed by our method are discussed.",
acknowledgement = ack-nhfb,
articleno = "2250029",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nnolim:2022:DSE,
author = "Uche A. Nnolim",
title = "Dynamic Selective Edge-Based
{Integer/Fractional-Order} Partial Differential
Equation for Degraded Document Image Binarization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500309",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500309",
abstract = "Conventional thresholding algorithms have had limited
success with degraded document images. Recently,
partial differential equations (PDEs) have been applied
with good results. However, these are usually tailored
to handle relatively few specific distortions. In this
study, we combine an edge detection term with a linear
binarization source term in a PDE formulation.
Additionally, a new proposed diffusivity function
further amplifies desired edges. It also suppresses
undesired edges that comprise bleed-through effects.
Furthermore, we develop the fractional variant of the
proposed scheme, which further improves results and
provides more flexibility. Moreover, nonlinear color
spaces are utilized to improve binarization results for
images with color distortion. The proposed scheme
removes document image degradation such as
bleed-through, stains, smudges, etc., and also restores
faded text in the images. Experimental subjective and
objective results show consistently superior
performance of the proposed approach compared to the
state-of-the-art PDE-based models.",
acknowledgement = ack-nhfb,
articleno = "2250030",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Moradi:2022:IDM,
author = "Hamid Moradi and Amir Hossein Foruzan",
title = "Integration of Dynamic Multi-Atlas and Deep Learning
Techniques to Improve Segmentation of the Prostate in
{MR} Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500310",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500310",
abstract = "Accurate delineation of the prostate in MR images is
an essential step for treatment planning and volume
estimation of the organ. Prostate segmentation is a
challenging task due to its variable size and shape.
Moreover, neighboring tissues have a low-contrast with
the prostate. We propose a robust and precise automatic
algorithm to define the prostate's boundaries in MR
images in this paper. First, we find the prostate's ROI
by a deep neural network and decrease the input image's
size. Next, a dynamic multi-atlas-based approach
obtains the initial segmentation of the prostate. A
watershed algorithm improves the initial segmentation
at the next stage. Finally, an SSM algorithm keeps the
result in the domain of allowable prostate shapes. The
quantitative evaluation of 74 prostate volumes
demonstrated that the proposed method yields a mean
Dice coefficient of 0.83{\textpm}0.05. In comparison
with recent researches, our algorithm is robust against
shape and size variations.",
acknowledgement = ack-nhfb,
articleno = "2250031",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rajalakshmi:2022:EVP,
author = "M. Rajalakshmi and K. Annapurani",
title = "Enhancement of Vascular Patterns in Palm Images Using
Various Image Enhancement Techniques for Person
Identification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500322",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500322",
abstract = "Image classification is a complicated process of
classifying an image based on its visual
representation. This paper portrays the need for
adapting and applying a suitable image enhancement and
denoising technique in order to arrive at a successful
classification of data captured remotely. Biometric
properties that are widely explored today are very
important for authentication purposes. Noise may be the
result of incorrect vein detection in the accepted
image, thus explaining the need for a better
development technique. This work provides subjective
and objective analysis of the performance of various
image enhancement filters in the spatial domain. After
performing these pre-processing steps, the vein map and
the corresponding vein graph can be easily obtained
with minimal extraction steps, in which the appropriate
Graph Matching method can be used to evaluate hand vein
graphs thus performing the person authentication. The
analysis result shows that the image enhancement filter
performs better as an image enhancement filter compared
to all other filters. Image quality measures (IQMs) are
also tabulated for the evaluation of image quality.",
acknowledgement = ack-nhfb,
articleno = "2250032",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Abedini:2022:IDU,
author = "Maryam Abedini and Horriyeh Haddad and Marzieh Faridi
Masouleh and Asadollah Shahbahrami",
title = "Image Denoising Using Sparse Representation and
Principal Component Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500334",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500334",
abstract = "This study proposes an image denoising algorithm based
on sparse representation and Principal Component
Analysis (PCA). The proposed algorithm includes the
following steps. First, the noisy image is divided into
overlapped 8{\texttimes}8 blocks. Second, the discrete
cosine transform is applied as a dictionary for the
sparse representation of the vectors created by the
overlapped blocks. To calculate the sparse vector, the
orthogonal matching pursuit algorithm is used. Then,
the dictionary is updated by means of the PCA algorithm
to achieve the sparsest representation of vectors.
Since the signal energy, unlike the noise energy, is
concentrated on a small dataset by transforming into
the PCA domain, the signal and noise can be well
distinguished. The proposed algorithm was implemented
in a MATLAB environment and its performance was
evaluated on some standard grayscale images under
different levels of standard deviations of white
Gaussian noise by means of peak signal-to-noise ratio,
structural similarity indexes, and visual effects. The
experimental results demonstrate that the proposed
denoising algorithm achieves significant improvement
compared to dual-tree complex discrete wavelet
transform and K-singular value decomposition image
denoising methods. It also obtains competitive results
with the block-matching and 3D filtering method, which
is the current state-of-the-art for image denoising.",
acknowledgement = ack-nhfb,
articleno = "2250033",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wagdy:2022:DCM,
author = "Marian Wagdy and Khaild Amin and Mina Ibrahim",
title = "Detection and Correction of Multi-Warping Document
Image",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500346",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500346",
abstract = "In this work, we aim to solve the multi-warping
document image problems. We can overcome the
limitations of the previous dewarping algorithms to
recover the shape of the document. The proposed method
is based on a well-defined pattern to simulate the
distorted and undistorted connected component of
document images. Some pairs of control points are
selected for each connected component and its ground
truth pattern to define the mapping function between
them. The dewarping process transforms the warping
connected component according to the geometric
transformation defined by the calculated mapping
function. Results on document dewarping dataset CBDAR
demonstrate the effectiveness of our method. OCR error
metrics are also used to evaluate the performance of
the proposed approach.",
acknowledgement = ack-nhfb,
articleno = "2250034",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ghilas:2022:SDI,
author = "Hamza Ghilas and Meriem Gagaoua and Abdelkamel Tari
and Mohamed Cheriet",
title = "{Spatial Distribution of Ink at Keypoints (SDIK)}: a
Novel Feature for Word Spotting in {Arabic} Documents",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500358",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500358",
abstract = "This paper addresses the challenging task of word
spotting in Arabic handwritten documents. We proposed a
novel feature that we called Spatial Distribution of
Ink at Keypoints (SDIK). The proposed feature captures
the characteristics of Arabic handwriting concentrated
at endpoints and branch points. SDIK feature quantizes
the spatial repartition of ink pixels in the
neighborhoods of keypoints. The resulting SDIK features
are very fast to match, we take this advantage to match
a query word with lines images rather than words
images. By this matching mechanism, we overcome the
hard task of segmenting an Arabic document into words.
The method proposed in this study is tested on
historical Arabic document with IBN SINA dataset and on
modern handwriting with IFN/ENIT database. The obtained
results are great of interest for retrieving query
words in an Arabic document.",
acknowledgement = ack-nhfb,
articleno = "2250035",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Salehi:2022:ISB,
author = "Hadi Salehi",
title = "Image De-Speckling Based on the Coefficient of
Variation, Improved Guided Filter, and Fast Bilateral
Filter",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S021946782250036X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782250036X",
abstract = "Images are widely used in engineering. Unfortunately,
medical ultrasound images and synthetic aperture radar
(SAR) images are mainly degraded by an intrinsic noise
called speckle. Therefore, de-speckling is a main
pre-processing stage for degraded images. In this
paper, first, an optimized adaptive Wiener filter
(OAWF) is proposed. OAWF can be applied to the input
image without the need for logarithmic transform. In
addition its performance is improved. Next, the
coefficient of variation (CV) is computed from the
input image. With the help of CV, the guided filter
converts to an improved guided filter (IGF). Next, the
improved guided filter is applied on the image.
Subsequently, the fast bilateral filter is applied on
the image. The proposed filter has a better image
detail preservation compared to some other standard
methods. The experimental outcomes show that the
proposed denoising algorithm is able to preserve image
details and edges compared with other de-speckling
methods.",
acknowledgement = ack-nhfb,
articleno = "2250036",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hassan:2022:NSI,
author = "Gaber Hassan and Khalid M. Hosny and R. M. Farouk and
Ahmed M. Alzohairy",
title = "New Set of Invariant Quaternion {Krawtchouk} Moments
for Color Image Representation and Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500371",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500371",
abstract = "One of the most often used techniques to represent
color images is quaternion algebra. This study
introduces the quaternion Krawtchouk moments, QKrMs, as
a new set of moments to represent color images.
Krawtchouk moments (KrMs) represent one type of
discrete moments. QKrMs use traditional Krawtchouk
moments of each color channel to describe color images.
This new set of moments is defined by using orthogonal
polynomials called the Krawtchouk polynomials. The
stability against the translation, rotation, and
scaling transformations for QKrMs is discussed. The
performance of the proposed QKrMs is evaluated against
other discrete quaternion moments for image
reconstruction capability, toughness against various
types of noise, invariance to similarity
transformations, color face image recognition, and CPU
elapsed times.",
acknowledgement = ack-nhfb,
articleno = "2250037",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Islam:2022:CSP,
author = "Rafiqul Islam and Md Shafiqul Islam and Muhammad
Shahin Uddin",
title = "Compressed Sensing in Parallel {MRI}: a Review",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500383",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500383",
abstract = "Magnetic resonance imaging (MRI) is a dynamic and safe
imaging technique in medical imaging. Recently,
parallel MRI (pMRI) is widely used for accelerating
conventional MRI. Both frequency and image domain-based
reconstructions are the most attractive methods for
generating the image from multi-channel k-space data.
Compressed sensing (CS) is a recently used procedure to
reduce the acquisition time of conventional MRI. This
reduction is achieved by taking fewer measurements from
the fully sampled k-space data. Therefore, applying the
CS technique in pMRI is the most emerging way for
further improving the acquisition time that is a
tremendous research interest. However, as the phase
encoding plane may be perpendicular or parallel to the
coil elements plane, finding the exact domain for CS in
pMRI reconstruction is a major challenging issue. In
this work, the application of the CS technique in pMRI
in both domains is investigated. Later some widely used
methodologies are presented as the nonlinear
reconstruction algorithm of CS in pMRI. Finally, a
discussion is performed based on CS in pMRI to perceive
the reality of different reconstruction algorithms at a
glance for finding preferred methodologies.",
acknowledgement = ack-nhfb,
articleno = "2250038",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Arora:2022:CBP,
author = "Tanvi Arora",
title = "{CNN}-based Prediction of {COVID-19} using Chest {CT}
Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500395",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500395",
abstract = "The coronavirus disease (COVID-19) pandemic that is
caused by the SARS-CoV2 has spread all over the world.
It is an infectious disease that can spread from person
to person. The severity of the disease can be
categorized into five categories namely asymptomatic,
mild, moderate, severe, and critical. From the reported
cases thus, it has been seen that 80\% of the cases
that test positive with COVID-19 infection have less
than moderate complications, whereas 20\% of the
positive cases develop severe and critical
complications. The virus infects the lungs of an
individual, therefore, it has been observed that the
X-ray and computed tomography (CT) scan images of the
infected people can be used by the machine
learning-based application programs to predict the
presence of the infection. Therefore, in the proposed
work, a Convolutional Neural Network model based upon
the DenseNet architecture is being used to predict the
presence of COVID-19 infection using the CT scan images
of the chest. The proposed work has been carried out
using the dataset of the CT images from the COVID CT
Dataset. It has 349 images marked as COVID-19 positive
and 397 images have been marked as COVID-19 negative.
The proposed system can categorize the test set images
with an accuracy of 91.4\%. The proposed method is
capable of detecting the presence of COVID-19 infection
with good accuracy using the chest CT scan images of
the humans.",
acknowledgement = ack-nhfb,
articleno = "2250039",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Varghese:2022:DLB,
author = "Prathibha Varghese and G. Arockia Selva Saroja",
title = "Deep Learning-Based Hexrep Neural Network for
Convergence Free with Operator's Efficacy in Hexagonal
Image Processing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "04",
pages = "??--??",
month = jul,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467823500328",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Aug 11 08:52:44 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500328",
abstract = "The field of hexagonal image processing is concerned
with the creation of image processing systems that
combine the advantages of biological model-based
evolutionary motivated frameworks. The structure and
functionality of artificial neural networks were
inspired by biological processes. The fundamental
framework of recording and output devices limits their
present state of the art. Prior neural networks have
used square or hexagonal style input to completely
connected layers, which resulted in a high coherence
problem between two adjacent hexagonal kernel layers
due to pooling. Previous research does not design the
self-data structure to support convolution to increase
computational efficiency, so it violates the
convolution and pooling operator, which greatly
degrades the image process performance. This paper
introduces a novel paradigm Proficient Deep
Learning-based Hexrep Neural Network that overcomes
major significant problems in image operations
structure constraint, coherence problem, and violation
of convolution and pooling operator and achieves
hexagonal image processing.",
acknowledgement = ack-nhfb,
articleno = "2350032",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kaur:2022:RND,
author = "Swapandeep Kaur and Sheifali Gupta and Swati Singh and
Tanvi Arora",
title = "A Review on Natural Disaster Detection in Social Media
and Satellite Imagery Using Machine Learning and Deep
Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467822500401",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500401",
abstract = "A disaster is a devastating incident that causes a
serious disruption of the functions of a community. It
leads to loss of human life and environmental and
financial losses. Natural disasters cause damage and
privation that could last for months and even years.
Immediate steps need to be taken and social media
platforms like Twitter help to provide relief to the
affected public. However, it is difficult to analyze
high-volume data obtained from social media posts.
Therefore, the efficiency and accuracy of useful data
extracted from the enormous posts related to disaster
are low. Satellite imagery is gaining popularity
because of its ability to cover large temporal and
spatial areas. But, both the social media and satellite
imagery require the use of automated methods to avoid
the errors caused by humans. Deep learning and machine
learning have become extremely popular for text and
image classification tasks. In this paper, a review has
been done on natural disaster detection through
information obtained from social media and satellite
images using deep learning and machine learning.",
acknowledgement = ack-nhfb,
articleno = "2250040",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2022:DLB,
author = "V. Akash Kumar and Vijaya Mishra and Monika Arora",
title = "Deep Learning-Based Classification of Malignant and
Benign Cells in Dermatoscopic Images via Transfer
Learning Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500413",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500413",
abstract = "The inhibition of healthy cells creating improper
controlling process of the human body system indicates
the occurrence of growth of cancerous cells. The
cluster of such cells leads to the development of
tumor. The observation of this type of abnormal skin
pigmentation is done using an effective tool called
Dermoscopy. However, these dermatoscopic images possess
a great challenge for diagnosis. Considering the
characteristics of dermatoscopic images, transfer
learning is an appropriate approach of automatically
classifying the images based on the respective
categories. An automatic identification of skin cancer
not only saves human life but also helps in detecting
its growth at an earlier stage which saves medical
practitioner's effort and time. A newly predicted model
has been proposed for classifying the skin cancer as
benign or malignant by DCNN with transfer learning and
its pre-trained models such as VGG 16, VGG 19, ResNet
50, ResNet 101, and Inception V3. The proposed
methodology aims at examining the efficiency of
pre-trained models and transfer learning approach for
the classification tasks and opens new dimensions of
research in the field of medicines using imaging
technique which can be implementable in real-time
applications.",
acknowledgement = ack-nhfb,
articleno = "2250041",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sasikumar:2022:CAD,
author = "K. Sasikumar and B. Vijayakumar",
title = "Comparative Analysis of Different Data Replication
Strategies in Cloud Environment",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500425",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib;
https://www.math.utah.edu/pub/tex/bib/java2020.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500425",
abstract = "In this paper, we performed a comparative study of the
different data replication strategies such as Adaptive
Data Replication Strategy (ADRS), Dynamic Cost Aware
Re-Replication and Rebalancing Strategy (DCR2S) and
Efficient Placement Algorithm (EPA) in the cloud
environment. The implementation of these three
techniques is done in JAVA and the performance analysis
is conducted to study the performance of those
replication techniques by various parameters. The
parameters used for the performance analysis of these
three techniques are Load Variance, Response Time,
Probability of File Availability, System Byte Effective
Rate (SBER), Latency, and Fault Ratio. From the
analysis, it is evaluated that by varying the number of
file replicas, it shows deviations in the outcomes of
these parameters. The comparative results were also
analyzed.",
acknowledgement = ack-nhfb,
articleno = "2250042",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Padalkar:2022:FBS,
author = "Ganesh R. Padalkar and Madhuri B. Khambete",
title = "Fusion-Based Semantic Segmentation Using Deep Learning
Architecture in Case of Very Small Training Dataset",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500437",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500437",
abstract = "Semantic segmentation is a pre-processing step in
computer vision-based applications. It is the task of
assigning a predefined class label to every pixel of an
image. Several supervised and unsupervised algorithms
are available to classify pixels of an image into
predefined object classes. The algorithms, such as
random forest and SVM are used to obtain the semantic
segmentation. Recently, convolutional neural network
(CNN)-based architectures have become popular for the
tasks of object detection, object recognition, and
segmentation. These deep architectures perform semantic
segmentation with far better accuracy than the
algorithms that were used earlier. CNN-based deep
learning architectures require a large dataset for
training. In real life, some of the applications may
not have sufficient good quality samples for training
of deep learning architectures e.g. medical
applications. Such a requirement initiated a need to
have a technique of effective training of deep learning
architecture in case of a very small dataset. Class
imbalance is another challenge in the process of
training deep learning architecture. Due to class
imbalance, the classifier overclassifies classes with
large samples. In this paper, the challenge of training
a deep learning architecture with a small dataset and
class imbalance is addressed by novel fusion-based
semantic segmentation technique which improves
segmentation of minor and major classes.",
acknowledgement = ack-nhfb,
articleno = "2250043",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Elyounsi:2022:FAO,
author = "Asma Elyounsi and Hatem Tlijani and Mohamed Salim
Bouhlel",
title = "Firefly Algorithm Optimized Functional Link Artificial
Neural Network for {ISA}-Radar Image Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500449",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500449",
abstract = "Traditional neural networks are very diverse and have
been used during the last decades in the fields of data
classification. These networks like MLP, back
propagation neural networks (BPNN) and feed forward
network have shown inability to scale with problem size
and with the slow convergence rate. So in order to
overcome these numbers of drawbacks, the use of higher
order neural networks (HONNs) becomes the solution by
adding input units along with a stronger functioning of
other neural units in the network and transforms easily
these input units to hidden layers. In this paper, a
new metaheuristic method, Firefly (FFA), is applied to
calculate the optimal weights of the Functional Link
Artificial Neural Network (FLANN) by using the flashing
behavior of fireflies in order to classify ISA-Radar
target. The average classification result of FLANN-FFA
which reached 96\% shows the efficiency of the process
compared to other tested methods.",
acknowledgement = ack-nhfb,
articleno = "2250044",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ravikumar:2022:MLT,
author = "S. Ravikumar and E. Kannan",
title = "Machine Learning Techniques for Identifying Fetal Risk
During Pregnancy",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500450",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500450",
abstract = "Cardiotocography (CTG) is a biophysical method for
assessing fetal condition that primarily relies on the
recording and automated analysis of fetal heart
activity. The quantitative description of the CTG
signals is provided by computerized fetal monitoring
systems. Even though effective conclusion generation
methods for decision process support are still required
to find out the fetal risk such as premature embryo,
this proposed method and outcome data can confirm the
assessment of the fetal state after birth. Low birth
weight is quite possibly the main attribute that
significantly depicts an unusual fetal result. These
expectations are assessed in a constant experimental
decision support system, providing valuable information
that can be used to obtain additional information about
the fetal state using machine learning techniques. The
advancements in modern obstetric practice enabled the
use of numerous reliable and robust machine learning
approaches in classifying fetal heart rate signals. The
Na{\"\i}ve Bayes (NB) classifier, support vector
machine (SVM), decision trees (DT), and random forest
(RF) are used in the proposed method. To assess these
outcomes in the proposed method, some of the metrics
such as precision, accuracy, F1 score, recall,
sensitivity, logarithmic loss and mean absolute error
have been taken. The above mentioned metrics will be
helpful to predict the fetal risk.",
acknowledgement = ack-nhfb,
articleno = "2250045",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bhasha:2022:AIS,
author = "A. Valli Bhasha and B. D. Venkatramana Reddy",
title = "Automated Image Super Resolution with the Aid of
Activation Function Optimized Deep {CNN} and Adaptive
Wavelet Lifting Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500462",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500462",
abstract = "Diverse image super-resolution (SR) techniques have
been implemented to reconstruct the high-resolution
(HR) images from input images through lower spatial
resolutions. However, the evaluation of the perceptual
quality of SR images remains an important and complex
research problem. This paper proposes a new image SR
model with the intention of attaining maximum Peak
Signal-to-Noise Ratio (PSNR). The conversion of
low-resolution (LR) images from the HR images is
performed by bicubic interpolation-based downsampling
and upsampling. Then, the four sub-bands of LR and HR
images are generated by the novel Adaptive Wavelet
Lifting approach, in which the filter modes are
optimized using the proposed SA-CBO. From this
technique, LR wavelet sub-bands (LRSB) for LR images
and HR wavelet sub-bands (HRSB) for HR images are
formed. With the help of the LRSB and HRSB images, the
residual images are formed by the adoption of the
optimized Activation function and optimized hidden
neurons in a deep convolutional neural network (CNN).
The improvement in both the adaptive wavelet lifting
approach and deep CNN is made by the
self-adaptive-colliding bodies optimization (SA-CBO).
Finally, the inverse adaptive wavelet lifting approach
is used to produce the final SR image. Experimental
results on publicly available SR image quality
databases confirm the effectiveness and generalization
ability of the proposed method compared with the
traditional image quality assessment algorithms.",
acknowledgement = ack-nhfb,
articleno = "2250046",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2022:ICB,
author = "Gangavarapu Venkata Satya Kumar and P. G. Krishna
Mohan",
title = "Improved Content Based Image Retrieval Process Based
on Deep Convolutional Neural Network and Salp Swarm
Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500474",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500474",
abstract = "Digital image and medical image retrieval from several
repositories are improving gradually, so the capacity
of repositories increases rapidly. The semantic space
is the main issue on content-based image retrieval
(CBIR), which exists among the semantic level as well
as increases the data recognized through human and low
level visible data obtained through the image. The CBIR
system utilizes the deep convolutional neural network
(DCNN), which is trained to medical image
characterization and the digital image by salp swarm
optimization algorithm (SSA). The average
classification accuracy for medical image is 86.805\%,
a mean average precision is 79\%, Average Recall Rate
(ARR) is 91.7\% and F -measure is 84.9\%, are achieved
during retrieval task. For image retrieval, the Average
Precision Rate (APR) improved from 39\%, 40\%, 36\% and
42.5\% to 86.8\% and the ARR enhanced from 39.5\%,
40.5\%, 35.5\% and 42.5\% to 86.8\%. The F -measure is
improved from 39.5\%, 40.5\%, 35.5\% and 42.5\% to
86.8\% as different with Local tetra patterns (LTrP),
LOOP, local derivative pattern (LDP) and local mean
differential excitation pattern (LMDeP) separately on
Corel-1K dataset. The presented method is most suitable
for multimodal digital images and medical image
retrieval for various parts of the body.",
acknowledgement = ack-nhfb,
articleno = "2250047",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shahrokhi:2022:ICM,
author = "Marziye Shahrokhi and Alireza Akoushideh and Asadollah
Shahbahrami",
title = "Image Copy--Move Forgery Detection Using Combination
of Scale-Invariant Feature Transform and Local Binary
Pattern Features",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500486",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500486",
abstract = "Today, manipulating, storing, and sending digital
images are simple and easy because of the development
of digital imaging devices from hardware and software
points of view. Digital images are used in different
contexts of people's lives such as news, forensics, and
so on. Therefore, the reliability of received images is
a question that often occupies the viewer's mind and
the authenticity of digital images is increasingly
important. Detecting a forged image as a genuine one as
well as detecting a genuine image as a forged one can
sometimes have irreparable consequences. For example,
an image that is available from the scene of a crime
can lead to a wrong decision if it is detected
incorrectly. In this paper, we propose a combination
method to improve the accuracy of copy--move forgery
detection (CMFD) reducing the false positive rate (FPR)
based on texture attributes. The proposed method uses a
combination of the scale-invariant feature transform
(SIFT) and local binary pattern (LBP). Consideration of
texture features around the keypoints detected by the
SIFT algorithm can be effective to reduce the incorrect
matches and improve the accuracy of CMFD. In addition,
to find more and better keypoints some pre-processing
methods have been proposed. This study was evaluated on
the COVERAGE, GRIP, and MICC-F220 databases.
Experimental results show that the proposed method
without clustering or segmentation and only with simple
matching operations, has been able to earn the true
positive rates of 98.75\%, 95.45\%, and 87\% on the
GRIP, MICC-F220, and COVERAGE datasets, respectively.
Also, the proposed method, with FPRs from 17.75\% to
3.75\% on the GRIP dataset, has been able to achieve
the best results.",
acknowledgement = ack-nhfb,
articleno = "2250048",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Saha:2022:TBF,
author = "Priya Saha and Debotosh Bhattacharjee and Barin Kumar
De and Mita Nasipuri",
title = "A Thermal Blended Facial Expression Analysis and
Recognition System Using Deformed Thermal Facial
Areas",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500498",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500498",
abstract = "There are many research works in visible as well as
thermal facial expression analysis and recognition.
Several facial expression databases have been designed
in both modalities. However, little attention has been
given for analyzing blended facial expressions in the
thermal infrared spectrum. In this paper, we have
introduced a Visual-Thermal Blended Facial Expression
Database (VTBE) that contains visual and thermal face
images with both basic and blended facial expressions.
The database contains 12 posed blended facial
expressions and spontaneous six basic facial
expressions in both modalities. In this paper, we have
proposed Deformed Thermal Facial Area (DTFA) in thermal
expressive face image and make an analysis to
differentiate between basic and blended expressions
using DTFA. Here, the fusion of DTFA and Deformed
Visual Facial Area (DVFA) has been proposed combining
the features of both modalities and experiments and has
been conducted on this new database. However, to show
the effectiveness of our proposed approach, we have
compared our method with state-of-the-art methods using
USTC-NVIE database. Experiment results reveal that our
approach is superior to state-of-the-art methods.",
acknowledgement = ack-nhfb,
articleno = "2250049",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jadav:2022:DSD,
author = "Kalpesh R. Jadav and Arvind R. Yadav",
title = "Dynamic Shadow Detection and Removal for Vehicle
Tracking System",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500504",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500504",
abstract = "Shadow leads to failure of moving target positioning,
segmentation, tracking, and classification in the video
surveillance system thus shadow detection and removal
is essential for further computer vision process. The
existing state-of-the-art methods for dynamic shadow
detection have produced a high discrimination rate but
a poor detection rate (foreground pixels are classified
as shadow pixels). This paper proposes an effective
method for dynamic shadow detection and removal based
on intensity ratio along with frame difference, gamma
correction, and morphology operations. The performance
of the proposed method has been tested on two outdoor
ATON datasets, namely, highway-I and highway-III for
vehicle tracking systems. The proposed method has
produced a discrimination rate of 89.07\% and a
detection rate of 80.79\% for highway-I video
sequences. Similarly, for a highway-III video sequence,
the discrimination rate of 85.60\% and detection rate
of 84.05\% have been obtained. Investigational outcomes
show that the proposed method is the simple, steadiest,
and robust for dynamic shadow detection on the dataset
used in this work.",
acknowledgement = ack-nhfb,
articleno = "2250050",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Indhumathi:2022:HAR,
author = "C. Indhumathi and V. Murugan and G. Muthulakshmii",
title = "Human Action Recognition Using Spatio-Temporal
Multiplier Network and Attentive Correlated Temporal
Feature",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500516",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500516",
abstract = "Nowadays, action recognition has gained more attention
from the computer vision community. Normally for
recognizing human actions, spatial and temporal
features are extracted. Two-stream convolutional neural
network is used commonly for human action recognition
in videos. In this paper, Adaptive motion Attentive
Correlated Temporal Feature (ACTF) is used for temporal
feature extractor. The temporal average pooling in
inter-frame is used for extracting the inter-frame
regional correlation feature and mean feature. This
proposed method has better accuracy of 96.9\% for
UCF101 and 74.6\% for HMDB51 datasets, respectively,
which are higher than the other state-of-the-art
methods.",
acknowledgement = ack-nhfb,
articleno = "2250051",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jatain:2022:EFR,
author = "Rashmi Jatain and Manisha Jailia",
title = "Enhanced Face Recognition Using Adaptive Local Tri
{Weber} Pattern with Improved Deep Learning
Architecture",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "22",
number = "05",
pages = "??--??",
month = oct,
year = "2022",
DOI = "https://doi.org/10.1142/S0219467822500528",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Tue Nov 8 11:46:54 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822500528",
abstract = "Effective face recognition is accomplished using the
extraction of features and classification. Though there
are multiple techniques for face image recognition,
full face recognition in real-time is quite difficult.
One of the emerging and promising methods to address
this challenge in face recognition is deep learning
networks. The inevitable network tool associated with
the face recognition method with deep learning systems
is convolutional neural networks (CNNs). This research
intends to develop a new method for face recognition
using adaptive intelligent methods. The main phases of
the proposed method are (a) data collection, (b) image
pre-processing, (c) normalization, (d) pattern
extraction, and (e) recognition. Initially, the images
for face recognition are gathered from CPFW, Yale
datasets, and the MIT-CBCL dataset. The image
pre-processing is performed by the Gaussian filtering
method. Further, the normalization of the image will be
done, which is a process that alters the range of pixel
intensities and can handle the poor contrast due to
glare. Then a new descriptor called adaptive local tri
Weber pattern (ALTrWP) acts as a pattern extractor. In
the recognition phase, the VGG16 architecture with new
chick updated-chicken swarm optimization (NSU-CSO) is
used. As the modification, VGG16 architecture will be
enhanced by this optimization technique. The
performance of the developed method is analyzed on two
standards face database. Experimental results are
compared with different machine learning approaches
concerned with noteworthy measures, which demonstrate
the efficiency of the considered classifier.",
acknowledgement = ack-nhfb,
articleno = "2250052",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Raj:2023:DRV,
author = "R. Jisha Raj and Smitha Dharan and T. T. Sunil",
title = "Dimensionality Reduction and Visualization of {{\em
Bharatanatyam Mudras}}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467823500018",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500018",
abstract = "Cultural dances are practiced all over the world. The
study of various gestures of the performer using
computer vision techniques can help in better
understanding of these dance forms and for annotation
purposes. {\em Bharatanatyam\/} is a classical dance
that originated in South India. {\em Bharatanatyam\/}
performer uses hand gestures ( {\em mudras\/} ), facial
expressions and body movements to communicate to the
audience the intended meaning. According to {\em
Natyashastra}, a classical text on Indian dance, there
are 28 {\em Asamyukta Hastas\/} (single-hand gestures)
and 23 {\em Samyukta Hastas\/} (Double-hand gestures)
in {\em Bharatanatyam}. Open datasets on {\em
Bharatanatyam\/} dance gestures are not presently
available. An exhaustive open dataset comprising of
various {\em mudras\/} in {\em Bharatanatyam\/} was
created. The dataset consists of 15\,396 distinct
single-hand {\em mudra\/} images and 13\,035 distinct
double-hand {\em mudra\/} images. In this paper, we
explore the dataset using various multidimensional
visualization techniques. PCA, Kernel PCA, Local Linear
Embedding, Multidimensional Scaling, Isomap, t-SNE and
PCA--t-SNE combination are being investigated. The best
visualization for exploration of the dataset is
obtained using PCA--t-SNE combination.",
acknowledgement = ack-nhfb,
articleno = "2350001",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Adhikari:2023:PEC,
author = "Ramesh Adhikari and Suresh Pokharel",
title = "Performance Evaluation of Convolutional Neural Network
Using Synthetic Medical Data Augmentation Generated by
{GAN}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S021946782350002X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782350002X",
abstract = "Data augmentation is widely used in image processing
and pattern recognition problems in order to increase
the richness in diversity of available data. It is
commonly used to improve the classification accuracy of
images when the available datasets are limited. Deep
learning approaches have demonstrated an immense
breakthrough in medical diagnostics over the last
decade. A significant amount of datasets are needed for
the effective training of deep neural networks. The
appropriate use of data augmentation techniques
prevents the model from over-fitting and thus increases
the generalization capability of the network while
testing afterward on unseen data. However, it remains a
huge challenge to obtain such a large dataset from rare
diseases in the medical field. This study presents the
synthetic data augmentation technique using Generative
Adversarial Networks to evaluate the generalization
capability of neural networks using existing data more
effectively. In this research, the convolutional neural
network (CNN) model is used to classify the X-ray
images of the human chest in both normal and pneumonia
conditions; then, the synthetic images of the X-ray
from the available dataset are generated by using the
deep convolutional generative adversarial network
(DCGAN) model. Finally, the CNN model is trained again
with the original dataset and augmented data generated
using the DCGAN model. The classification performance
of the CNN model is improved by 3.2\% when the
augmented data were used along with the originally
available dataset.",
acknowledgement = ack-nhfb,
articleno = "2350002",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumawat:2023:IDH,
author = "Anchal Kumawat and Sucheta Panda",
title = "An Integrated Double Hybrid Fusion Approach for Image
Smoothing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500031",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500031",
abstract = "Often in practice, during the process of image
acquisition, the acquired image gets degraded due to
various factors like noise, motion blur, mis-focus of a
camera, atmospheric turbulence, etc. resulting in the
image unsuitable for further analysis or processing. To
improve the quality of these degraded images, a double
hybrid restoration filter is proposed on the two same
sets of input images and the output images are fused to
get a unified filter in combination with the concept of
image fusion. First image set is processed by applying
deconvolution using Wiener Filter (DWF) twice and
decomposing the output image using Discrete Wavelet
Transform (DWT). Similarly, second image set is also
processed simultaneously by applying Deconvolution
using Lucy--Richardson Filter (DLR) twice followed by
the above procedure. The proposed filter gives a better
performance as compared to DWF and DLR filters in case
of both blurry as well as noisy images. The proposed
filter is compared with some standard deconvolution
algorithms and also some state-of-the-art restoration
filters with the help of seven image quality assessment
parameters. Simulation results prove the success of the
proposed algorithm and at the same time, visual and
quantitative results are very impressive.",
acknowledgement = ack-nhfb,
articleno = "2350003",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Antony:2023:TFE,
author = "Joycy K. Antony and K. Kanagalakshmi",
title = "{T2FRF} Filter: an Effective Algorithm for the
Restoration of Fingerprint Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500043",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500043",
abstract = "Images captured in dim light are hardly satisfactory
and increasing the International Organization for
Standardization (ISO) for a short duration of exposure
makes them noisy. The image restoration methods have a
wide range of applications in the field of medical
imaging, computer vision, remote sensing, and graphic
design. Although the use of flash improves the
lighting, it changed the image tone besides developing
unnecessary highlight and shadow. Thus, these drawbacks
are overcome using the image restoration methods that
recovered the image with high quality from the degraded
observation. The main challenge in the image
restoration approach is recovering the degraded image
contaminated with the noise. In this research, an
effective algorithm, named T2FRF filter, is developed
for the restoration of the image. The noisy pixel is
identified from the input fingerprint image using Deep
Convolutional Neural Network (Deep CNN), which is
trained using the neighboring pixels. The Rider
Optimization Algorithm (ROA) is used for the removal of
the noisy pixel in the image. The enhancement of the
pixel is performed using the type II fuzzy system. The
developed T2FRF filter is measured using the metrics,
such as correlation coefficient and Peak Signal to
Noise Ratio (PSNR) for evaluating the performance. When
compared with the existing image restoration method,
the developed method obtained a maximum correlation
coefficient of 0.7504 and a maximum PSNR of 28.2467dB,
respectively.",
acknowledgement = ack-nhfb,
articleno = "2350004",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sharma:2023:PAS,
author = "Sandhya Sharma and Sheifali Gupta and Neeraj Kumar and
Tanvi Arora",
title = "Postal Automation System in {Gurmukhi} Script using
Deep Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500055",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500055",
abstract = "Nowadays in the era of automation, the postal
automation system is one of the major research areas.
Developing a postal automation system for a nation like
India is much troublesome than other nations because of
India's multi-script and multi-lingual behavior. This
proposed work will be helpful in the postal automation
of district names of Punjab (state) written in Gurmukhi
script, which is the official language of the state in
North India. For this, a holistic approach i.e. a
segmentation-free technique has been used with the help
of Convolutional Neural Network (CNN) and Deep learning
(DL). For the purpose of recognition, a database of 22
000 images (samples) which are handwritten in Gurmukhi
script for all the 22 districts of Punjab is prepared.
Each sample is written two times by 500 different
writers generating 1000 samples for each district name.
Two CNN models are proposed which are named as
ConvNetGuru and ConvNetGuruMod for the purpose of
recognition. Maximum validation accuracy achieved by
ConvNetGuru is 90\% and ConvNetGuruMod is 98\%.",
acknowledgement = ack-nhfb,
articleno = "2350005",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Erwin:2023:RBV,
author = "Erwin and Hadrians Kesuma Putra and Bambang Suprihatin
and Fathoni",
title = "Retinal Blood Vessel Extraction Using a New
Enhancement Technique of Modified Convolution Filters
and {Sauvola} Thresholding",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500067",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500067",
abstract = "The retinal blood vessels in humans are major
components with different shapes and sizes. The
extraction of the blood vessels from the retina is an
important step to identify the type or nature of the
pattern of the diseases in the retina. Furthermore, the
retinal blood vessel was also used for diagnosis,
detection, and classification. The most recent solution
in this topic is to enable retinal image improvement or
enhancement by a convolution filter and Sauvola
threshold. In image enhancement, gamma correction is
applied before filtering the retinal fundus. After
that, the image should be transformed to a gray channel
to enhance pictorial clarity using contrast-limited
histogram equalization. For filter, this paper combines
two convolution filters, namely sharpen and smooth
filters. The Sauvola threshold, the morphology, and the
medium filter are applied to extract blood vessels from
the retinal image. This paper uses DRIVE and STARE
datasets. The accuracies of the proposed method are
95.37\% for DRIVE with a runtime of 1.77s and 95.17\%
for STARE with 2.05s runtime. Based on the result, it
concludes that the proposed method is good enough to
achieve average calculation parameters of a low time
quality, quick, and significant.",
acknowledgement = ack-nhfb,
articleno = "2350006",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Tudavekar:2023:STI,
author = "Gajanan Tudavekar and Santosh S. Saraf and Sanjay R.
Patil",
title = "Spatio-Temporal Inference Transformer Network for
Video Inpainting",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500079",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500079",
abstract = "Video inpainting aims to complete in a visually
pleasing way the missing regions in video frames. Video
inpainting is an exciting task due to the variety of
motions across different frames. The existing methods
usually use attention models to inpaint videos by
seeking the damaged content from other frames.
Nevertheless, these methods suffer due to irregular
attention weight from spatio-temporal dimensions, thus
giving rise to artifacts in the inpainted video. To
overcome the above problem, Spatio-Temporal Inference
Transformer Network (STITN) has been proposed. The
STITN aligns the frames to be inpainted and
concurrently inpaints all the frames, and a
spatio-temporal adversarial loss function improves the
STITN. Our method performs considerably better than the
existing deep learning approaches in quantitative and
qualitative evaluation.",
acknowledgement = ack-nhfb,
articleno = "2350007",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Devi:2023:DSF,
author = "Bhagyashri Devi and M. Mary Synthuja Jain Preetha",
title = "A Descriptive Survey on Face Emotion Recognition
Techniques",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500080",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500080",
abstract = "Recognition of natural emotion from human faces has
applications in Human--Computer Interaction, image and
video retrieval, automated tutoring systems, smart
environment as well as driver warning systems. It is
also a significant indication of nonverbal
communication among the individuals. The assignment of
Face Emotion Recognition (FER) is predominantly complex
for two reasons. The first reason is the nonexistence
of a large database of training images, and the second
one is about classifying the emotions, which can be
complex based on the static input image. In addition,
robust unbiased FER in real time remains the foremost
challenge for various supervised learning-based
techniques. This survey analyzes diverse techniques
regarding the FER systems. It reviews a bunch of
research papers and performs a significant analysis.
Initially, the analysis depicts various techniques that
are contributed in different research papers. In
addition, this paper offers a comprehensive study
regarding the chronological review and performance
achievements in each contribution. The analytical
review is also concerned about the measures for which
the maximum performance was achieved in several
contributions. Finally, the survey is extended with
various research issues and gaps that can be useful for
the researchers to promote improved future works on the
FER models.",
acknowledgement = ack-nhfb,
articleno = "2350008",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Canejo:2023:EDN,
author = "Marcos Jos{\'e} Can{\^e}jo and Carlos Alexandre
{Barros De Mello}",
title = "Edge Detection in Natural Scenes Inspired by the Speed
Drawing Challenge",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500092",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500092",
abstract = "Edge detection is a major step in several computer
vision applications. Edges define the shape of objects
to be used in a recognition system, for example. In
this work, we introduce an approach to edge detection
inspired by a challenge for artists: the Speed Drawing
Challenge. In this challenge, a person is asked to draw
the same figure in different times (as 10 min, 1 min
and 10 s); at each time, different levels of details
are drawn by the artist. In a short time stamp, just
the major elements remain. This work proposes a new
approach for producing images with different amounts of
edges representing different levels of relevance. Our
method uses superpixel to suppress image details,
followed by Globalized Probability of Boundary (gPb)
and Canny edge detection algorithms to create an image
containing different number of edges. After that, an
edge analysis step detects whose edges are the most
relevant for the scene. The results are presented for
the BSDS500 dataset and they are compared to other edge
and contour detection algorithms by quantitative and
qualitative means with very satisfactory results.",
acknowledgement = ack-nhfb,
articleno = "2350009",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gaddour:2023:NMA,
author = "Houda Gaddour and Slim Kanoun and Nicole Vincent",
title = "A New Method for {Arabic} Text Detection in Natural
Scene Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500109",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500109",
abstract = "Text in scene images can provide useful and vital
information for content-based image analysis.
Therefore, text detection and script identification in
images are an important task. In this paper, we propose
a new method for text detection in natural scene
images, particularly for Arabic text, based on a
bottom-up approach where four principal steps can be
highlighted. The detection of extremely stable and
homogeneous regions of interest (ROIs) is based on the
Color Stability and Homogeneity Regions (CSHR) proposed
technique. These regions are then labeled as textual or
non-textual ROI. This identification is based on a
structural approach. The textual ROIs are grouped to
constitute zones according to spatial relations between
them. Finally, the textual or non-textual nature of the
constituted zones is refined. This last identification
is based on handcrafted features and on features built
from a Convolutional Neural Network (CNN) after
learning. The proposed method was evaluated on the
databases used for text detection in natural scene
images: the competitions organized in 2017 edition of
the International Conference on Document Analysis and
Recognition (ICDAR2017), the Urdu-text database and our
Natural Scene Image Database for Arabic Text detection
(NSIDAT) database. The obtained experimental results
seem to be interesting.",
acknowledgement = ack-nhfb,
articleno = "2350010",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lepcha:2023:IMC,
author = "Dawa Chyophel Lepcha and Bhawna Goyal and Ayush
Dogra",
title = "Image Matting: a Comprehensive Survey on Techniques,
Comparative Analysis, Applications and Future Scope",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500110",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500110",
abstract = "In the era of rapid growth of technologies, image
matting plays a key role in image and video editing
along with image composition. In many significant
real-world applications such as film production, it has
been widely used for visual effects, virtual zoom,
image translation, image editing and video editing.
With recent advancements in digital cameras, both
professionals and consumers have become increasingly
involved in matting techniques to facilitate image
editing activities. Image matting plays an important
role to estimate {\em alpha matte\/} in the {\em
unknown\/} region to distinguish {\em foreground\/}
from the {\em background\/} region of an image using an
input image and the corresponding trimap of an image
which represents a {\em foreground\/} and {\em
unknown\/} region. Numerous image matting techniques
have been proposed recently to extract high-quality
{\em matte\/} from image and video sequences. This
paper illustrates a systematic overview of the current
image and video matting techniques mostly emphasis on
the current and advanced algorithms proposed recently.
In general, image matting techniques have been
categorized according to their underlying approaches,
namely, sampling-based, propagation-based, combination
of sampling and propagation-based and deep
learning-based algorithms. The traditional image
matting algorithms depend primarily on color
information to predict {\em alpha matte\/} such as
sampling-based, propagation-based or combination of
sampling and propagation-based algorithms. However,
these techniques mostly use low-level features and
suffer from high-level {\em background\/} which tends
to produce unwanted artifacts when color is same or
semi-transparent in the {\em foreground\/} object.
Image matting techniques based on deep learning have
recently introduced to address the shortcomings of
traditional algorithms. Rather than simply depending on
the color information, it uses deep learning mechanism
to estimate the {\em alpha matte\/} using an input
image and the trimap of an image. A comprehensive
survey on recent image matting algorithms and in-depth
comparative analysis of these algorithms has been
thoroughly discussed in this paper.",
acknowledgement = ack-nhfb,
articleno = "2350011",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Elmoufidi:2023:CMI,
author = "Abdelali Elmoufidi and Ayoub Skouta and Said
Jai-andaloussi and Ouail Ouchetto",
title = "{CNN} with Multiple Inputs for Automatic Glaucoma
Assessment Using Fundus Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "01",
pages = "??--??",
month = jan,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500122",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:33 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500122",
abstract = "In the area of ophthalmology, glaucoma affects an
increasing number of people. It is a major cause of
blindness. Early detection avoids severe ocular
complications such as glaucoma, cystoid macular edema,
or diabetic proliferative retinopathy. Intelligent
artificial intelligence has been confirmed beneficial
for glaucoma assessment. In this paper, we describe an
approach to automate glaucoma diagnosis using funds
images. The setup of the proposed framework is in
order: The Bi-dimensional Empirical Mode Decomposition
(BEMD) algorithm is applied to decompose the Regions of
Interest (ROI) to components (BIMFs+residue). CNN
architecture VGG19 is implemented to extract features
from decomposed BEMD components. Then, we fuse the
features of the same ROI in a bag of features. These
last very long; therefore, Principal Component Analysis
(PCA) are used to reduce features dimensions. The bags
of features obtained are the input parameters of the
implemented classifier based on the Support Vector
Machine (SVM). To train the built models, we have used
two public datasets, which are ACRIMA and REFUGE. For
testing our models, we have used a part of ACRIMA and
REFUGE plus four other public datasets, which are
RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF.
The overall precision of 98.31\%, 98.61\%, 96.43\%,
96.67\%, 95.24\%, and 98.60\% is obtained on ACRIMA,
REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and
sjchoi86-HRF datasets, respectively, by using the model
trained on REFUGE. Again an accuracy of 98.92\%,
99.06\%, 98.27\%, 97.10\%, 96.97\%, and 96.36\% is
obtained in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light,
Drishti-GS1, and sjchoi86-HRF datasets, respectively,
using the model training on ACRIMA. The experimental
results obtained from different datasets demonstrate
the efficiency and robustness of the proposed approach.
A comparison with some recent previous work in the
literature has shown a significant advancement in our
proposal.",
acknowledgement = ack-nhfb,
articleno = "2350012",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sangani:2023:PSS,
author = "Dhara J. Sangani and Rajesh A. Thakker and S. D.
Panchal and Rajesh Gogineni",
title = "{Pan}-Sharpening for Spectral Details Preservation Via
Convolutional Sparse Coding in Non-Subsampled Shearlet
Space",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467823500134",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500134",
abstract = "The optical satellite sensors encounter certain
constraints on producing high-resolution multispectral
(HRMS) images. Pan-sharpening (PS) is a remote sensing
image fusion technique, which is an effective mechanism
to overcome the limitations of available imaging
products. The prevalent issue in PS algorithms is the
imbalance between spatial quality and spectral details
preservation, thereby producing intensity variations in
the fused image. In this paper, a PS method is proposed
based on convolutional sparse coding (CSC) implemented
in the non-subsampled shearlet transform (NSST) domain.
The source images, panchromatic (PAN) and multispectral
(MS) images, are decomposed using NSST. The resultant
high-frequency bands are fused using adaptive weights
determined from chaotic grey wolf optimization (CGWO)
algorithm. The CSC-based model is employed to fuse the
low-frequency bands. Further, an iterative filtering
mechanism is developed to enhance the quality of fused
image. Four datasets with different geographical
content like urban area, vegetation, etc. and eight
existing algorithms are used for evaluation of the
proposed PS method. The comprehensive visual and
quantitative results approve that the proposed method
accomplishes considerable improvement in spatial and
spectral details equivalence in the pan-sharpened
image.",
acknowledgement = ack-nhfb,
articleno = "2350013",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Fathima:2023:MIC,
author = "M. Dhilsath Fathima and R. Hariharan and S. P. Raja",
title = "Multiple Imputation by Chained Equations ---
{$K$}-Nearest Neighbors and Deep Neural Network
Architecture for Kidney Disease Prediction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500146",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500146",
abstract = "Chronic kidney disease (CKD) is a health concern that
affects people all over the world. Kidney dysfunction
or impaired kidney functions are the causes of CKD. The
machine learning-based prediction models are used to
determine the risk level of CKD and assist healthcare
practitioners in delaying and preventing the disease's
progression. The researchers proposed many prediction
models for determining the CKD risk level. Although
these models performed well, their precision is limited
since they do not handle missing values in the clinical
dataset adequately. The missing values of a clinical
dataset can degrade the training outcomes that leads to
false predictions. Thus, imputing missing values
increases the prediction model performance. This
proposed work developed a novel imputation technique by
combining Multiple Imputation by Chained Equations and
K -Nearest Neighbors (MICE--KNN) for imputing the
missing values. The experimental results show that
MICE--KNN accurately predicts the missing values, and
the Deep Neural Network (DNN) improves the prediction
performance of the CKD model. Various metrics like mean
absolute error, accuracy, specificity, Matthews
correlation coefficient, the area under the curve, $
F_1$-score, sensitivity, and precision have been used
to evaluate the proposed CKD model performance. The
performance analysis exhibits that MICE--KNN with deep
learning outperforms other classifiers. According to
our experimental study, the MICE--KNN imputation
algorithm with DNN is more appropriate for predicting
the kidney disease.",
acknowledgement = ack-nhfb,
articleno = "2350014",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jahnavi:2023:NES,
author = "Yeturu Jahnavi and Poongothai Elango and S. P. Raja
and P. Nagendra Kumar",
title = "A Novel Ensemble Stacking Classification of Genetic
Variations Using Machine Learning Algorithms",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500158",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500158",
abstract = "Genetics is the clinical review of congenital
mutation, where the principal advantage of analyzing
genetic mutation of humans is the exploration,
analysis, interpretation and description of the genetic
transmitted and inherited effect of several diseases
such as cancer, diabetes and heart diseases. Cancer is
the most troublesome and disordered affliction as the
proportion of cancer sufferers is growing massively.
Identification and discrimination of the mutations that
impart to the enlargement of tumor from the unbiased
mutations is difficult, as majority tumors of cancer
are able to exercise genetic mutations. The genetic
mutations are systematized and categorized to sort the
cancer by way of medical observations and considering
clinical studies. At the present time, genetic
mutations are being annotated and these interpretations
are being accomplished either manually or using the
existing primary algorithms. Evaluation and
classification of each and every individual genetic
mutation was basically predicated on evidence from
documented content built on medical literature.
Consequently, as a means to build genetic mutations,
basically, depending on the clinical evidences persists
a challenging task. There exist various algorithms such
as one hot encoding technique is used to derive
features from genes and their variations, TF-IDF is
used to extract features from the clinical text data.
In order to increase the accuracy of the
classification, machine learning algorithms such as
support vector machine, logistic regression, Naive
Bayes, etc., are experimented. A stacking model
classifier has been developed to increase the accuracy.
The proposed stacking model classifier has obtained the
log loss 0.8436 and 0.8572 for cross-validation data
set and test data set, respectively. By the
experimentation, it has been proved that the proposed
stacking model classifier outperforms the existing
algorithms in terms of log loss. Basically, minimum log
loss refers to the efficient model. Here the log loss
has been reduced to less than 1 by using the proposed
stacking model classifier. The performance of these
algorithms can be gauged on the basis of the various
measures like multi-class log loss.",
acknowledgement = ack-nhfb,
articleno = "2350015",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shashikala:2023:SSM,
author = "T. D. Shashikala and B. L. Sunitha and S.
Basavarajappa and J. P. Davim",
title = "Some Studies on Measurement of Worn Surface by Digital
Image Processing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S021946782350016X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782350016X",
abstract = "Digital image processing (DIP) becomes a common tool
for analyzing engineering problems by fast, frequent
and noncontact method of identification and
measurement. An attempt has been made in the present
investigation to use this method for automatically
detecting the worn regions on the material surface and
also its measurement. Brass material has been used for
experimentation as it is used generally as a bearing
material. A pin on disc dry sliding wear testing
machine has been used for conducting the experiments by
applying loads from 10 N to 50 N and by keeping sliding
distance and sliding speed constant. After testing,
images are acquired by using 1/2 inch interline
transfer CCD image sensor with 795(H) {\^a} 896(V)
spatial resolution of 8.6 {\textmu} m (H) {\^a} 8.3
{\textmu} m (V) unit cell. Denoising has been done to
remove any possible noise followed by contrast
stretching to enhance image for wear region extraction.
Segmentation tool was used to divide the worn and
unworn regions by identifying white regions greater
than a threshold value with an objective of quantifying
the worn surface for tested specimen. Canny edge
detection and granulometry techniques have been used to
quantify the wear region. The results revel that the
specific wear rate increases with increase in applied
load, at constant sliding speed and sliding distance.
Similarly, the area of worn region as identified by DIP
also increased from 42.7\% to 69.97\%. This is because
of formation of deeper groves in the worn material.",
acknowledgement = ack-nhfb,
articleno = "2350016",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Spoorthi:2023:FCS,
author = "B. Spoorthi and Shanthi Mahesh",
title = "Firefly Competitive Swarm Optimization Based
Hierarchical Attention Network for Lung Cancer
Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500171",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500171",
abstract = "Lung cancer is a severe disease, which causes high
deaths in the world. Earlier discovery of lung cancer
is useful to enhance the rate of survival in patients.
Computed Tomography (CT) is utilized for determining
the tumor and identifying the cancer level in the body.
However, the issues of CT images cause less tumor
visibility areas and unconstructive rates in tumor
regions. This paper devises an optimization-driven
technique for classifying lung cancer. The CT image is
utilized for determining the position of the tumor.
Here, the CT image undergoes segmentation, which is
performed using the DeepJoint model. Furthermore, the
feature extraction is carried out, wherein features
such as local ternary pattern-based features, Histogram
of Gradients (HoG) features, and statistical features,
like variance, mean, kurtosis, energy, entropy, and
skewness. The categorization of lung cancer is
performed using Hierarchical Attention Network (HAN).
The training of HAN is carried out using proposed
Firefly Competitive Swarm Optimization (FCSO), which is
devised by combining firefly algorithm (FA), and
Competitive Swarm Optimization (CSO). The proposed
FCSO-based HAN provided effective performance with high
accuracy of 91.3\%, sensitivity of 88\%, and
specificity of 89.1\%.",
acknowledgement = ack-nhfb,
articleno = "2350017",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mondal:2023:DCW,
author = "Saorabh Kumar Mondal and Arpitam Chatterjee and Bipan
Tudu",
title = "{DCT} Coefficients Weighting ({DCTCW})-Based {Gray
Wolf Optimization (GWO)} for Brightness Preserving
Image Contrast Enhancement",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500183",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500183",
abstract = "Image contrast enhancement (CE) is a frequent image
enhancement requirement in diverse applications.
Histogram equalization (HE), in its conventional and
different further improved ways, is a popular technique
to enhance the image contrast. The conventional as well
as many of the later versions of HE algorithms often
cause loss of original image characteristics
particularly brightness distribution of original image
that results artificial appearance and feature loss in
the enhanced image. Discrete Cosine Transform (DCT)
coefficient mapping is one of the recent methods to
minimize such problems while enhancing the image
contrast. Tuning of DCT parameters plays a crucial role
towards avoiding the saturations of pixel values.
Optimization can be a possible solution to address this
problem and generate contrast enhanced image preserving
the desired original image characteristics. Biological
behavior-inspired optimization techniques have shown
remarkable betterment over conventional optimization
techniques in different complex engineering problems.
Gray wolf optimization (GWO) is a comparatively new
algorithm in this domain that has shown promising
potential. The objective function has been formulated
using different parameters to retain original image
characteristics. The objective evaluation against CEF,
PCQI, FSIM, BRISQUE and NIQE with test images from
three standard databases, namely, SIPI, TID and CSIQ
shows that the presented method can result in values up
to 1.4, 1.4, 0.94, 19 and 4.18, respectively, for the
stated metrics which are competitive to the reported
conventional and improved techniques. This paper can be
considered a first-time application of GWO towards
DCT-based image CE.",
acknowledgement = ack-nhfb,
articleno = "2350018",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lohith:2023:MBP,
author = "M. S. Lohith and Yoga Suhas Kuruba Manjunath and M. N.
Eshwarappa",
title = "Multimodal Biometric Person Authentication Using Face,
Ear and Periocular Region Based on Convolution Neural
Networks",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500195",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500195",
abstract = "Biometrics is an active area of research because of
the increase in need for accurate person identification
in numerous applications ranging from entertainment to
security. Unimodal and multimodal are the well-known
biometric methods. Unimodal biometrics uses one
biometric modality of a person for person
identification. The performance of an unimodal
biometric system is degraded due to certain limitations
such as: intra-class variations and nonuniversality.
The person identification using more than one biometric
modality of a person is multimodal biometrics. This
method of identification has gained more interest due
to resistance on spoof attacks and more recognition
rate. Conventional methods of feature extraction have
difficulty in engineering features that are liable to
more variations such as illumination, pose and age
variations. Feature extraction using convolution neural
network (CNN) can overcome these difficulties because
large dataset with robust variations can be used for
training, where CNN can learn these variations. In this
paper, we propose multimodal biometrics at feature
level horizontal fusion using face, ear and periocular
region biometric modalities and apply deep learning CNN
for feature representation and also we propose face,
ear and periocular region dataset that are robust to
intra-class variations. The evaluation of the system is
made by using proposed database. Accuracy, Precision,
Recall and F1 score are calculated to evaluate the
performance of the system and had shown remarkable
improvement over existing biometric system.",
acknowledgement = ack-nhfb,
articleno = "2350019",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sudha:2023:CES,
author = "K. Antony Sudha and V. Cibi Castro and G. Muthulakshmi
and T. Ilam Parithi and S. P. Raja",
title = "A Chaotic Encryption System Based on {DNA} Coding
Using a Deep Neural Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500201",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500201",
abstract = "Critical to computer vision applications, deep
learning demands a massive volume of training data for
great performance. However, encrypting the sensitive
information in a photograph is fraught with difficulty,
despite rapid technological advancements. The Advanced
Encryption System (AES) is the bedrock of classical
encryption technologies. The Data Encryption Standard
(DES) has low sensitivity, with weak anti-hacking
capabilities. In a chaotic encryption system, a chaotic
logistic map is employed to generate a key double
logistic sequence, and deoxyribonucleic acid (DNA)
matrices are created by DNA coding. The XOR operation
is carried out between the DNA sequence matrix and the
key matrix. Finally, the DNA matrix is decoded to
obtain an encrypted image. Given that encrypted images
are susceptible to attacks, a rapid and efficient
Convolutional Neural Network (CNN) denoiser is used
that enhances the robustness of the algorithm by
maximizing the resolution of rebuilt images. The use of
a key mixing percentage factor gives the proposed
system vast key space and great key sensitivity. Its
implementation is examined using statistical techniques
such as histogram analysis, information entropy, key
space analysis and key sensitivity. Experiments have
shown that the suggested system is secure and robust to
statistical and noise attacks.",
acknowledgement = ack-nhfb,
articleno = "2350020",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Das:2023:NSE,
author = "Gyanesh Das and Rutuparna Panda and Leena Samantaray
and Sanjay Agrawal",
title = "A Novel Segmentation Error Minimization-Based Method
for Multilevel Optimal Threshold Selection Using
Opposition Equilibrium Optimizer",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500213",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500213",
abstract = "Image segmentation is imperative for image processing
applications. Thresholding technique is the easiest way
of partitioning an image into different regions.
Mostly, entropy-based threshold selection methods are
used for multilevel thresholding. However, these
methods suffer from their dependencies on spatial
distribution of gray values. To solve this issue, a
novel segmentation error minimization (SEM)-based
method for multilevel optimal threshold selection using
opposition equilibrium optimizer (OEO) is suggested. In
this contribution, a new segmentation score (SS)
(objective function) is derived while minimizing the
segmentation error function. Our proposal is explicitly
free from gray level spatial distribution of an image.
Optimal threshold values are achieved by maximizing the
SS (fitness value) using OEO. The key to success is the
maximization of score among classes, ensuring the
sharpening of the shred boundary between classes,
leading to an improved threshold selection method. It
is empirically demonstrated how the optimal threshold
selection is made. Experimental results are presented
using standard test images. Standard measures like
PSNR, SSIM and FSIM are used for validation The results
are compared with state-of-the-art entropy-based
technique. Our method performs well both qualitatively
and quantitatively. The suggested technique would be
useful for biomedical image segmentation.",
acknowledgement = ack-nhfb,
articleno = "2350021",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pemula:2023:GRF,
author = "Rambabu Pemula and Sagenela Vijaya Kumar and C.
Nagaraju",
title = "Generation of Random Fields for Image Segmentation
Techniques: a Review",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500225",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500225",
abstract = "Generation of random fields (GRF) for image
segmentation represents partitioning an image into
different regions that are homogeneous or have similar
facets of the image. It is one of the most challenging
tasks in image processing and a very important
pre-processing step in the fields of computer vision,
image analysis, medical image processing, pattern
recognition, remote sensing, and geographical
information system. Many researchers have presented
numerous image segmentation approaches, but still,
there are challenges like segmentation of low contrast
images, removal of shadow in the images, reduction of
high dimensional images, and computational complexity
of segmentation techniques. In this review paper, the
authors address these issues. The experiments are
conducted and tested on the Berkely dataset (BSD500),
Semantic dataset, and our own dataset, and the results
are shown in the form of tables and graphs.",
acknowledgement = ack-nhfb,
articleno = "2350022",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Raju:2023:PAC,
author = "Ayalapogu Ratna Raju and Suresh Pabboju and Rajeswara
Rao Ramisetty",
title = "Performance Analysis and Critical Review on
Segmentation Techniques for Brain Tumor
Classification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500237",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500237",
abstract = "An irregular growth in brain cells causes brain
tumors. In recent years, a considerable rate of
increment in medical cases regarding brain tumors has
been observed, affecting adults and children. However,
it is highly curable in recent times only if detected
in the early time of tumor growth. Moreover, there are
many sophisticated approaches devised by researchers
for predicting the tumor regions and their stages. In
addition, Magnetic Resonance Imaging (MRI) is utilized
commonly by radiologists to evaluate tumors. In this
paper, the input image is from a database, and brain
tumor segmentation is performed using various
segmentation techniques. Here, the comparative analysis
is performed by comparing the performance of
segmentation approaches, like Hybrid Active Contour
(HAC) model, Bayesian Fuzzy Clustering (BFC), Active
Contour (AC), Fuzzy C-Means (FCM) clustering technique,
Sparse (Sparse FCM), and Black Hole Entropy Fuzzy
Clustering (BHEFC) model. Moreover, segmentation
technique performance is evaluated with the Dice
coefficient, Jaccard coefficient, and segmentation
accuracy. The proposed method shows high Dice and
Jaccard coefficients of 0.7809 and 0.6456 by varying
iteration with the REMBRANDT dataset and a better
segmentation accuracy of 0.9789 by changing image size
in the Brats-2015 database.",
acknowledgement = ack-nhfb,
articleno = "2350023",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chamundeshwari:2023:HPE,
author = "Chamundeshwari and Nagashetteppa Biradar and
Udaykumar",
title = "Hybrid Pattern Extraction with Deep Learning-Based
Heart Disease Diagnosis Using Echocardiogram Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "02",
pages = "??--??",
month = mar,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500249",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Mar 25 07:40:34 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500249",
abstract = "Echocardiography represents a noninvasive diagnostic
approach that offers information concerning
hemodynamics and cardiac function. It is a familiar
cardiovascular diagnostic test apart from chest X-ray
and echocardiography. The medical knowledge is enhanced
by the Artificial Intelligence (AI) approaches like
deep learning and machine learning because of the
increase in the complexity as well as the volume of the
data that in turn unlocks the clinically significant
information. Similarly, the usage of developing
information as well as communication technologies is
becoming important for generating a persistent
healthcare service via which the chronic disease and
elderly patients get their medical facility at their
home that in turn enhances the life quality and avoids
hospitalizations. The main intention of this paper is
to design and develop a novel heart disease diagnosis
using speckle-noise reduction and deep learning-based
feature learning and classification. The datasets
gathered from the hospital are composed of both the
images and the video frames. Since echocardiogram
images suffer from speckle noise, the initial process
is the speckle-noise reduction technique. Then, the
pattern extraction is performed by combining the Local
Binary Pattern (LBP), and Weber Local Descriptor (WLD)
referred to as the hybrid pattern extraction. The deep
feature learning is conducted by the optimized
Convolutional Neural Network (CNN), in which the
features are extracted from the max-pooling layer, and
the fully connected layer is replaced by the optimized
Recurrent Neural Network (RNN) for handling the
diagnosis of heart disease, thus proposed model is
termed as CRNN. The novel Adaptive Electric Fish
Optimization (A-EFO) is used for performing feature
learning and classification. In the final step, the
best accuracy is achieved with the introduced model,
while a comparative analysis is accomplished over the
traditional models. From the experimental analysis, FDR
of A-EFO-CRNN at 75\% learning percentage is 21.05\%,
15\%, 48.89\%, and 71.95\% progressed than CRNN, CNN,
RNN, and NN, respectively. Thus, the performance of the
A-EFO-CRNN is enriched than the existing
heuristic-oriented and classifiers in terms of the
image dataset.",
acknowledgement = ack-nhfb,
articleno = "2350024",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Vasantharaj:2023:ABI,
author = "A. Vasantharaj and Pacha Shoba Rani and Sirajul Huque
and K. S. Raghuram and R. Ganeshkumar and Sebahadin
Nasir Shafi",
title = "Automated Brain Imaging Diagnosis and Classification
Model using Rat Swarm Optimization with Deep Learning
based Capsule Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467822400010",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400010",
abstract = "Earlier identification of brain tumor (BT) is
essential to increase the survival rate of the
patients. The commonly used imaging technique for BT
diagnosis is magnetic resonance imaging (MRI).
Automated BT classification model is required for
assisting the radiologists to save time and enhance
efficiency. The classification of BT is difficult owing
to the non-uniform shapes of tumors and location of
tumors in the brain. Therefore, deep learning (DL)
models can be employed for the effective
identification, prediction, and diagnosis of diseases.
In this view, this paper presents an automated BT
diagnosis using rat swarm optimization (RSO) with deep
learning based capsule network (DLCN) model, named
RSO-DLCN model. The presented RSO-DLCN model involves
bilateral filtering (BF) based preprocessing to enhance
the quality of the MRI. Besides, non-iterative grabcut
based segmentation (NIGCS) technique is applied to
detect the affected tumor regions. In addition, DLCN
model based feature extractor with RSO algorithm based
parameter optimization processes takes place. Finally,
extreme learning machine with stacked autoencoder
(ELM-SA) based classifier is employed for the effective
classification of BT. For validating the BT diagnostic
performance of the presented RSO-DLCN model, an
extensive set of simulations were carried out and the
results are inspected under diverse dimensions. The
simulation outcome demonstrated the promising results
of the RSO-DLCN model on BT diagnosis with the
sensitivity of 98.4\%, specificity of 99\%, and
accuracy of 98.7\%.",
acknowledgement = ack-nhfb,
articleno = "2240001",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Kiran:2023:MLD,
author = "S. Vishwa Kiran and Inderjeet Kaur and K. Thangaraj
and V. Saveetha and R. Kingsy Grace and N. Arulkumar",
title = "Machine Learning with Data Science-Enabled Lung Cancer
Diagnosis and Classification Using Computed Tomography
Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400022",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400022",
abstract = "In recent times, the healthcare industry has been
generating a significant amount of data in distinct
formats, such as electronic health records (EHR),
clinical trials, genetic data, payments, scientific
articles, wearables, and care management databases.
Data science is useful for analysis (pattern
recognition, hypothesis testing, risk valuation) and
prediction. The major, primary usage of data science in
the healthcare domain is in medical imaging. At the
same time, lung cancer diagnosis has become a hot
research topic, as automated disease detection poses
numerous benefits. Although numerous approaches have
existed in the literature for lung cancer diagnosis,
the design of a novel model to automatically identify
lung cancer is a challenging task. In this view, this
paper designs an automated machine learning (ML) with
data science-enabled lung cancer diagnosis and
classification (MLDS-LCDC) using computed tomography
(CT) images. The presented model initially employs
Gaussian filtering (GF)-based pre-processing technique
on the CT images collected from the lung cancer
database. Besides, they are fed into the normalized
cuts (Ncuts) technique where the nodule in the
pre-processed image can be determined. Moreover, the
oriented FAST and rotated BRIEF (ORB) technique is
applied as a feature extractor. At last, sunflower
optimization-based wavelet neural network (SFO-WNN)
model is employed for the classification of lung
cancer. In order to examine the diagnostic outcome of
the MLDS-LCDC model, a set of experiments were carried
out and the results are investigated in terms of
different aspects. The resultant values demonstrated
the effectiveness of the MLDS-LCDC model over the other
state-of-the-art methods with the maximum sensitivity
of 97.01\%, specificity of 98.64\%, and accuracy of
98.11\%.",
acknowledgement = ack-nhfb,
articleno = "2240002",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Sammeta:2023:DOS,
author = "Naresh Sammeta and Latha Parthiban",
title = "Data Ownership and Secure Medical Data Transmission
using Optimal Multiple Key-Based Homomorphic Encryption
with Hyperledger Blockchain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400034",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400034",
abstract = "Recent healthcare systems are defined as highly
complex and expensive. But it can be decreased with
enhanced electronic health records (EHR) management,
using blockchain technology. The healthcare sector in
today's world needs to address two major issues, namely
data ownership and data security. Therefore, blockchain
technology is employed to access and distribute the
EHRs. With this motivation, this paper presents novel
data ownership and secure medical data transmission
model using optimal multiple key-based homomorphic
encryption (MHE) with Hyperledger blockchain
(OMHE-HBC). The presented OMHE-HBC model enables the
patients to access their own data, provide permission
to hospital authorities, revoke permission from
hospital authorities, and permit emergency contacts.
The proposed model involves the MHE technique to
securely transmit the data to the cloud and prevent
unauthorized access to it. Besides, the optimal key
generation process in the MHE technique takes place
using a hosted cuckoo optimization (HCO) algorithm. In
addition, the proposed model enables sharing of EHRs by
the use of multi-channel HBC, which makes use of one
blockchain to save patient visits and another one for
the medical institutions in recoding links that point
to EHRs stored in external systems. A complete set of
experiments were carried out in order to validate the
performance of the suggested model, and the results
were analyzed under many aspects. A comprehensive
comparison of results analysis reveals that the
suggested model outperforms the other techniques.",
acknowledgement = ack-nhfb,
articleno = "2240003",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Madhusudhan:2023:FVR,
author = "M. V. Madhusudhan and V. Udaya Rani and Chetana
Hegde",
title = "Finger Vein Recognition Model for Biometric
Authentication Using Intelligent Deep Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400046",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400046",
abstract = "In recent years, biometric authentication systems have
remained a hot research topic, as they can recognize or
authenticate a person by comparing their data to other
biometric data stored in a database. Fingerprints, palm
prints, hand vein, finger vein, palm vein, and other
anatomic or behavioral features have all been used to
develop a variety of biometric approaches. Finger vein
recognition (FVR) is a common method of examining the
patterns of the finger veins for proper authentication
among the various biometrics. Finger vein acquisition,
preprocessing, feature extraction, and authentication
are all part of the proposed intelligent deep
learning-based FVR (IDL-FVR) model. Infrared imaging
devices have primarily captured the use of finger
veins. Furthermore, a region of interest extraction
process is carried out in order to save the finger
part. The shark smell optimization algorithm is used to
tune the hyperparameters of the bidirectional
long--short-term memory model properly. Finally, an
authentication process based on Euclidean distance is
performed, which compares the features of the current
finger vein image to those in the database. The IDL-FVR
model surpassed the earlier methods by accomplishing a
maximum accuracy of 99.93\%. Authentication is
successful when the Euclidean distance is small and
vice versa.",
acknowledgement = ack-nhfb,
articleno = "2240004",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Banerjee:2023:SVD,
author = "Rudranath Banerjee and Sourav De and Shouvik Dey",
title = "A Survey on Various Deep Learning Algorithms for an
Efficient Facial Expression Recognition System",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400058",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400058",
abstract = "Facial Expression (FE) encompasses information
concerning the emotional together with the physical
state of a human. In the last few years, FE Recognition
(FER) has turned out to be a propitious research field.
FER is the chief processing technique for non-verbal
intentions, and also it is a significant and propitious
computer vision together with the artificial
intelligence field. As a novel machine learning theory,
Deep Learning (DL) not only highlights the depth of the
learning model but also emphasizes the significance of
Feature Learning (FL) for the network model, and it has
made several research achievements in FER. Here, the
present research states are examined typically from the
latest FE extraction algorithm as well as the FER
centered on DL. The research on classifiers gathered
from recent papers discloses a more powerful as well as
reliable comprehending of the peculiar traits of
classifiers for research fellows. At the ending of the
survey, few problems in addition to chances that are
required to be tackled in the upcoming future are
presented.",
acknowledgement = ack-nhfb,
articleno = "2240005",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Thushara:2023:GTB,
author = "A. Thushara and C. Ushadevi Amma and Ansamma John",
title = "Graph Theory-Based Brain Network Connectivity Analysis
and Classification of {Alzheimer}'s Disease",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S021946782240006X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782240006X",
abstract = "Alzheimer's Disease (AD) is basically a progressive
neurodegenerative disorder associated with abnormal
brain networks that affect millions of elderly people
and degrades their quality of life. The abnormalities
in brain networks are due to the disruption of White
Matter (WM) fiber tracts that connect the brain
regions. Diffusion-Weighted Imaging (DWI) captures the
brain's WM integrity. Here, the correlation betwixt the
WM degeneration and also AD is investigated by
utilizing graph theory as well as Machine Learning (ML)
algorithms. By using the DW image obtained from
Alzheimer's Disease Neuroimaging Initiative (ADNI)
database, the brain graph of each subject is
constructed. The features extracted from the brain
graph form the basis to differentiate between Mild
Cognitive Impairment (MCI), Control Normal (CN) and AD
subjects. Performance evaluation is done using binary
and multiclass classification algorithms and obtained
an accuracy that outperforms the current top-notch
DWI-based studies.",
acknowledgement = ack-nhfb,
articleno = "2240006",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Malathi:2023:RRC,
author = "V. Malathi and M. P. Gopinath",
title = "A Review on Rice Crop Disease Classification Using
Computational Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400071",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400071",
abstract = "Rice is a significant cereal crop across the world. In
rice cultivation, different types of sowing methods are
followed, and thus bring in issues regarding sampling
collection. Climate, soil, water level, and a
diversified variety of crop seeds (hybrid and
traditional varieties) and the period of growth are
some of the challenges. This survey mainly focuses on
rice crop diseases which affect the parts namely
leaves, stems, roots, and spikelet; it mainly focuses
on leaf-based diseases. Existing methods for diagnosing
leaf disease include statistical approaches, data
mining, image processing, machine learning, and deep
learning techniques. This review mainly addresses
diseases of the rice crop, a framework to diagnose rice
crop diseases, and computational approaches in Image
Processing, Machine Learning, Deep Learning, and
Convolutional Neural Networks. Based on performance
indicators, interpretations were made for the following
algorithms namely support vector machine (SVM),
convolutional neural network (CNN), backpropagational
neural network (BPNN), and feedforward neural network
(FFNN).",
acknowledgement = ack-nhfb,
articleno = "2240007",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Kumar:2023:HBV,
author = "T. Satish Kumar and S. Jothilakshmi and Batholomew C.
James and M. Prakash and N. Arulkumar and C. Rekha",
title = "{HHO}-Based Vector Quantization Technique for
Biomedical Image Compression in Cloud Computing",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400083",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400083",
abstract = "In the present digital era, the exploitation of
medical technologies and massive generation of medical
data using different imaging modalities, adequate
storage, management, and transmission of biomedical
images necessitate image compression techniques. Vector
quantization (VQ) is an effective image compression
approach, and the widely employed VQ technique is
Linde--Buzo--Gray (LBG), which generates local optimum
codebooks for image compression. The codebook
construction is treated as an optimization issue solved
with utilization of metaheuristic optimization
techniques. In this view, this paper designs an
effective biomedical image compression technique in the
cloud computing (CC) environment using Harris Hawks
Optimization (HHO)-based LBG techniques. The HHO-LBG
algorithm achieves a smooth transition among
exploration as well as exploitation. To investigate the
better performance of the HHO-LBG technique, an
extensive set of simulations was carried out on
benchmark biomedical images. The proposed HHO-LBG
technique has accomplished promising results in terms
of compression performance and reconstructed image
quality.",
acknowledgement = ack-nhfb,
articleno = "2240008",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Kumar:2023:WTO,
author = "K. Praveen Kumar and C. Venkata Narasimhulu and K.
Satya Prasad",
title = "{$2$D} Wavelet Tree Ordering Based Localized Total
Variation Model for Efficient Image Restoration",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400095",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400095",
abstract = "The degraded image during the process of image
analysis needs more number of iterations to restore it.
These iterations take long waiting time and slow
scanning, resulting in inefficient image restoration. A
few numbers of measurements are enough to recuperate an
image with good condition. Due to tree sparsity, a 2D
wavelet tree reduces the number of coefficients and
iterations to restore the degraded image. All the
wavelet coefficients are extracted with overlaps as low
and high sub-band space and ordered them such that they
are decomposed in the tree ordering structured path.
Some articles have addressed the problems with tree
sparsity and total variation (TV), but few authors
endorsed the benefits of tree sparsity. In this paper,
a spatial variation regularization algorithm based on
tree order is implemented to change the window size and
variation estimators to reduce the loss of image
information and to solve the problem of image smoothing
operation. The acceptance rate of the tree-structured
path relies on local variation estimators to regularize
the performance parameters and update them to restore
the image. For this, the Localized Total Variation
(LTV) method is proposed and implemented on a 2D
wavelet tree ordering structured path based on the
proposed image smooth adjustment scheme. In the end, a
reliable reordering algorithm proposed to reorder the
set of pixels and to increase the reliability of the
restored image. Simulation results clearly show that
the proposed method improved the performance compared
to existing methods of image restoration.",
acknowledgement = ack-nhfb,
articleno = "2240009",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Reddy:2023:MMM,
author = "Mummadi Gowthami Reddy and Palagiri Veera Narayana
Reddy and Patil Ramana Reddy",
title = "Multi-Modal Medical Image Fusion Using 3-Stage
Multiscale Decomposition and {PCNN} with Adaptive
Arguments",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400101",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400101",
abstract = "In the current era of technological development,
medical imaging plays an important role in many
applications of medical diagnosis and therapy. In this
regard, medical image fusion could be a powerful tool
to combine multi-modal images by using image processing
techniques. But, conventional approaches failed to
provide the effective image quality assessments and
robustness of fused image. To overcome these drawbacks,
in this work three-stage multiscale decomposition
(TSMSD) using pulse-coupled neural networks with
adaptive arguments (PCNN-AA) approach is proposed for
multi-modal medical image fusion. Initially,
nonsubsampled shearlet transform (NSST) is applied onto
the source images to decompose them into low frequency
and high frequency bands. Then, low frequency bands of
both the source images are fused using nonlinear
anisotropic filtering with discrete Karhunen--Loeve
transform (NLAF-DKLT) methodology. Next, high frequency
bands obtained from NSST are fused using PCNN-AA
approach. Now, fused low frequency and high frequency
bands are reconstructed using NSST reconstruction.
Finally, band fusion rule algorithm with pyramid
reconstruction is applied to get final fused medical
image. Extensive simulation outcome discloses the
superiority of proposed TSMSD using PCNN-AA approach as
compared to state-of-the-art medical image fusion
methods in terms of fusion quality metrics such as
entropy (E), mutual information (MI), mean (M),
standard deviation (STD), correlation coefficient (CC)
and computational complexity.",
acknowledgement = ack-nhfb,
articleno = "2240010",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Naveen:2023:FDA,
author = "J. Naveen and Sheba Selvam and Blessy Selvam",
title = "{FO-DPSO} Algorithm for Segmentation and Detection of
Diabetic Mellitus for Ulcers",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400113",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400113",
abstract = "In recent days, the major concern for diabetic
patients is foot ulcers. According to the survey, among
15 people among 100 are suffering from this foot ulcer.
The wound or ulcer found which is found in diabetic
patients consumes more time to heal, also required more
conscious treatment. Foot ulcers may lead to
deleterious danger condition and also may be the cause
for loss of limb. By understanding this grim condition,
this paper proposes Fractional-Order Darwinian Particle
Swarm Optimization (FO-DPSO) technique for analyzing
foot ulcer 2D color images. This paper deals with
standard image processing, i.e. efficient segmentation
using FO-DPSO algorithm and extracting textural
features using Gray Level Co-occurrence Matrix (GLCM)
technique. The whole effort projected results as
accuracy of 91.2\%, sensitivity of 100\% and
specificity as 96.7\% for Na{\"\i}ve Bayes classifier
and accuracy of 91.2\%, sensitivity of 100\% and
sensitivity of 79.6\% for Hoeffding tree classifier.",
acknowledgement = ack-nhfb,
articleno = "2240011",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Gayatri:2023:CID,
author = "Erapaneni Gayatri and S. L. Aarthy",
title = "Challenges and Imperatives of Deep Learning Approaches
for Detection of Melanoma: a Review",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400125",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400125",
abstract = "Recently, melanoma became one of the deadliest forms
of skin cancer due to ultraviolet rays. The diagnosis
of melanoma is very crucial if it is not identified in
the early stages and later on, in the advanced stages,
it affects the other organs of the body, too. Earlier
identification of melanoma plays a major role in the
survival chances of a human. The manual detection of
tumor thickness is a very difficult task so dermoscopy
is used to measure the thickness of the tumor which is
a non-invasive method. Computer-aided diagnosis is one
of the greatest evolutions in the medical sector, this
system helps the doctors for the automated diagnosis of
the disease because it improves accurate disease
detection. In the world of digital images, some phases
are required to remove the artifacts for achieving the
best accurate diagnosis results such as the acquisition
of an image, pre-processing, segmentation, feature
selection, extraction and finally classification phase.
This paper mainly focuses on the various deep learning
techniques like convolutional neural networks,
recurrent neural networks, You Only Look Once for the
purpose of classification and prediction of the
melanoma and is also focuses on the other variant of
melanomas, i.e. ocular melanoma and mucosal melanoma
because it is not a matter where the melanoma starts in
the body.",
acknowledgement = ack-nhfb,
articleno = "2240012",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Rao:2023:DLB,
author = "E. Srinivasa Rao and Ch. Raghava Prasad",
title = "Deep Learning-Based Medical Image Fusion Using
Integrated Joint Slope Analysis with Probabilistic
Parametric Steered Image Filter",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "03",
pages = "??--??",
month = may,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467822400137",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Jun 2 06:51:21 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467822400137",
abstract = "Medical image fusion plays a significant role in
medical diagnosis applications. Although the
conventional approaches have produced moderate visual
analysis, still there is a scope to improve the
performance parameters and reduce the computational
complexity. Thus, this article implemented the hybrid
fusion method by using the novel implementation of
joint slope analysis (JSA), probabilistic parametric
steered image filtration (PPSIF), and deep learning
convolutional neural networks (DLCNNs)-based SR Fusion
Net. Here, JSA decomposes the images to estimate
edge-based slopes and develops the edge-preserving
approximate layers from the multi-modal medical images.
Further, PPSIF is used to generate the feature fusion
with base layer-based weight maps. Then, the SR Fusion
Net is used to generate the spatial and texture
feature-based weight maps. Finally, optimal fusion rule
is applied on the detail layers generated from the base
layer and approximate layer, which resulted in the
fused outcome. The proposed method is capable of
performing the fusion operation between various
modalities of images, such as MRI-CT, MRI-PET, and
MRI-SPECT combinations by using two different
architectures. The simulation results show that the
proposed method resulted in better subjective and
objective performance as compared to state of art
approaches.",
acknowledgement = ack-nhfb,
articleno = "2240013",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
remark = "Special Issue on Advances in Deep Learning Algorithms
for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}
@Article{Suresh:2023:DLC,
author = "Gulivindala Suresh and Chanamallu Srinivasa Rao",
title = "Detection and Localization of Copy--Move Forgery in
Digital Images: Review and Challenges",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467823500250",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500250",
abstract = "Copy move forgery in digital images became a common
problem due to the wide accessibility of image
processing algorithms and open-source editing software.
The human visual system cannot identify the traces of
forgery in the tampered image. The proliferation of
such digital images through the internet and social
media is possible with a finger touch. These tampered
images have been used in news reports, judicial
forensics, medical records, and financial statements.
In this paper, a detailed review has been carried on
various copy-move forgery detection (CMFD) and
localization techniques. Further, challenges in the
research are identified along with possible
solutions.",
acknowledgement = ack-nhfb,
articleno = "2350025",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Islam:2023:CSD,
author = "Md. Shafiqul Islam and Rafiqul Islam",
title = "A Critical Survey on Developed Reconstruction
Algorithms for Computed Tomography Imaging from a
Limited Number of Projections",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500262",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500262",
abstract = "Rapid system and hardware development of X-ray
computed tomography (CT) technologies has been
accompanied by equally exciting advances in image
reconstruction algorithms. Of the two reconstruction
algorithms, analytical and iterative, iterative
reconstruction (IR) algorithms have become a clinically
viable option in CT imaging. The first CT scanners in
the early 1970s used IR algorithms, but lack of
computation power prevented their clinical use. In
2009, the first IR algorithms became commercially
available and replaced conventionally established
analytical algorithms as filtered back projection.
Since then, IR has played a vital role in the field of
radiology. Although all available IR algorithms share
the common mechanism of artifact reduction and/or
potential for radiation dose reduction, the magnitude
of these effects depends upon specific IR algorithms.
IR reconstructs images by iteratively optimizing an
objective function. The objective function typically
consists of a data integrity term and a regularization
term. Therefore, different regularization priors are
used in IR algorithms. This paper will briefly look at
the overall evolution of CT image reconstruction and
the regularization priors used in IR algorithms.
Finally, a discussion is presented based on the reality
of various reconstruction methodologies at a glance to
find the preferred one. Consequently, we will present
anticipation towards future advancements in this
domain.",
acknowledgement = ack-nhfb,
articleno = "2350026",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Zhiyu:2023:IIA,
author = "Wang Zhiyu and Ding Weili and And Wang Mingkui",
title = "Illumination Invariance Adaptive Sidewalk Detection
Based on Unsupervised Feature Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500274",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500274",
abstract = "To solve the problem of road recognition when the
robot is driving on the sidewalk, a novel sidewalk
detection algorithm from the first-person perspective
is proposed, which is crucial for robot navigation. The
algorithm starts from the illumination invariance graph
of the sidewalk image, and the sidewalk ``seeds'' are
selected dynamically to get the sidewalk features for
unsupervised feature learning. The final sidewalk
region will be extracted by multi-threshold adaptive
segmentation and connectivity processing. The key
innovations of the proposed algorithm are the method of
illumination invariance based on PCA and the
unsupervised feature learning for sidewalk detection.
With the PCA-based illumination invariance, it can
calculate the lighting invariance angle dynamically to
remove the impact of illumination and different brick
colors' influence on sidewalk detection. Then the
sidewalk features are selected dynamically using the
parallel geometric structure of the sidewalk, and the
confidence region of the sidewalk is obtained through
unsupervised feature learning. The proposed method can
effectively suppress the effects of shadows and
different colored bricks in the sidewalk area. The
experimental result proves that the F-measure of the
proposed algorithm can reach 93.11\% and is at least
7.7\% higher than the existing algorithm.",
acknowledgement = ack-nhfb,
articleno = "2350027",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chole:2023:LMO,
author = "Vikrant Chole and Vijay Gadicha",
title = "Locust Mayfly Optimization-Tuned Neural Network for
{AI}-Based Pruning in Chess Game",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500286",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500286",
abstract = "The art of mimicking a human's responses and behavior
in a programming machine is called Artificial
intelligence (AI). AI has been incorporated in games in
such a way to make them interesting, especially in
chess games. This paper proposes a hybrid optimization
tuned neural network (NN) to establish a winning
strategy in the chess game by generating the possible
next moves in the game. Initially, the images from
Portable Game Notation (PGN) file are used to train the
NN classifier. The proposed Locust Mayfly algorithm is
utilized to optimally tune the weights of the NN
classifier. The proposed Locust Mayfly algorithm
inherits the characteristic features of hybrid survival
and social interacting search agents. The NN classifier
involves in finding all the possible moves in the
board, among which the best move is obtained using the
mini-max algorithm. At last, the performance of the
proposed Locust mayfly-based NN method is evaluated
with help of the performance metrics, such as
specificity, accuracy, and sensitivity. The proposed
Locust mayfly-based NN method attained a specificity of
98\%, accuracy of 98\%, and a sensitivity of 98\%,
which demonstrates the productiveness of the proposed
mayfly-based NN method in pruning.",
acknowledgement = ack-nhfb,
articleno = "2350028",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pankaj:2023:NDO,
author = "Pankaj and P. K. Bharti and And Brajesh Kumar",
title = "A New Design of Occlusion-Invariant Face Recognition
Using Optimal Pattern Extraction and {CNN} with
{GRU}-Based Architecture",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500298",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500298",
abstract = "Face detection is a computer technology being used in
a variety of applications that identify human faces in
digital images. In many face recognition challenges,
Convolutional Neural Networks (CNNs) are regarded as a
problem solver. Occlusion is determined as the most
common challenge of face recognition in realistic
applications. Several studies are undergoing to obtain
face recognition without any challenges. However, the
occurrence of noise and occlusion in the image reduces
the achievement of face recognition. Hence, various
researches and studies are carried out to solve the
challenges involved with the occurrence of occlusion
and noise in the image, and more clarification is
needed to acquire high accuracy. Hence, a deep learning
model is intended to be developed in this paper using
the meta-heuristic approach. The proposed model covers
four main steps: (a) data acquisition, (b)
pre-processing, (c) pattern extraction and (d)
classification. The benchmark datasets regarding the
face image with occlusion are gathered from a public
source. Further, the pre-processing of the images is
performed by contrast enhancement and Gabor filtering.
With these pre-processed images, pattern extraction is
done by the optimal local mesh ternary pattern. Here,
the hybrid Whale--Galactic Swarm Optimization (WGSO)
algorithm is used for developing the optimal local mesh
ternary pattern extraction. By inputting the
pattern-extracted image, the new deep learning model
namely ``CNN with Gated Recurrent Unit (GRU)'' network
performs the recognition process to maximize the
accuracy, and also it is used to enhance the face
recognition model. From the results, in terms of
accuracy, the proposed WGSO- CNN+GRU model is better by
4.02\%, 3.76\% and 2.17\% than the CNN, SVM and SRC,
respectively. The experimental results are presented by
performing their comparative analysis on a standard
dataset, and they assure the efficiency of the proposed
model. However, many challenging problems related to
face recognition still exist, which offer excellent
opportunities to face recognition researchers in the
future.",
acknowledgement = ack-nhfb,
articleno = "2350029",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rahul:2023:ESP,
author = "Vaddadi Sai Rahul and M. Tejas and N. Narayanan
Prasanth and And S. P. Raja",
title = "Early Success Prediction of {Indian} Movies Using
Subtitles: a Document Vector Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500304",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500304",
abstract = "Scientific studies of the elements that influence the
box office performance of Indian films have generally
concentrated on post-production elements, such as those
discovered after a film has been completed or released,
and notably for Bollywood films. Only fewer studies
have looked at regional film industries and
pre-production factors, which are elements that are
known before a decision to greenlight a film is made.
This study looked at Indian films using natural
language processing and machine learning approaches to
see if they would be profitable in the pre-production
stage. We extract movie data and English subtitles (as
an approximation to the screenplay) for the top five
Indian regional film industries: Bollywood, Kollywood,
Tollywood, Mollywood, and Sandalwood, as they make up a
major portion of the Indian film industry's revenue.
Subtitle Vector (Sub2Vec), a Paragraph Vector model
trained on English subtitles, was used to embed
subtitle text into 50 and 100 dimensions. The proposed
approach followed a two-stage pipeline. In the first
stage, Return on Investment (ROI) was calculated using
aggregated subtitle embeddings and associated movie
data. Classification models used the ROI calculated in
the first step to predicting a film's verdict in the
second step. The optimal regressor--classifier pair was
determined by evaluating classification models using $
F_1$-score and Cohen's Kappa scores on various
hyperparameters. When compared to benchmark methods,
our proposed methodology forecasts box office success
more accurately.",
acknowledgement = ack-nhfb,
articleno = "2350030",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Honnutagi:2023:UVE,
author = "Pooja Honnutagi and Y. S. Laitha and And V. D. Mytri",
title = "Underwater Video Enhancement Using Manta Ray Foraging
Lion Optimization-Based Fusion Convolutional Neural
Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500316",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500316",
abstract = "Due to the significance of aquatic robotics and marine
engineering, the underwater video enhancement has
gained huge attention. Thus, a video enhancement
method, namely Manta Ray Foraging Lion
Optimization-based fusion Convolutional Neural Network
(MRFLO-based fusion CNN) algorithm is developed in this
research for enhancing the quality of the underwater
videos. The MRFLO is developed by merging the Lion
Optimization Algorithm (LOA) and Manta Ray Foraging
Optimization (MRFO). The blur in the input video frame
is detected and estimated through the Laplacian's
variance method. The fusion CNN classifier is used for
deblurring the frame by combining both the input frame
and blur matrix. The fusion CNN classifier is tuned by
the developed MRFLO algorithm. The pixel of the
deblurred frame is enhanced using the Type II Fuzzy
system and Cuckoo Search optimization algorithm filter
(T2FCS filter). The developed MRFLO-based fusion CNN
algorithm uses the metrics, Underwater Image Quality
Measure (UIQM), Underwater Color Image Quality
Evaluation (UCIQE), Structural Similarity Index Measure
(SSIM), Mean Square Error (MSE), and Peak
Signal-to-Noise Ratio (PSNR) for the evaluation by
varying the blur intensity. The proposed MRFLO-based
fusion CNN algorithm acquired a PSNR of 38.9118, SSIM
of 0.9593, MSE of 3.2214, UIQM of 3.0041 and UCIQE of
0.7881.",
acknowledgement = ack-nhfb,
articleno = "2350031",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sasikaladevi:2023:CBD,
author = "N. Sasikaladevi and A. Revathi",
title = "Certainty-Based Deep Fused Neural Network Using
Transfer Learning and Adaptive Movement Estimation for
the Diagnosis of Cardiomegaly",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S021946782350033X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782350033X",
abstract = "Cardiomegaly is a radiographic abnormality, and it has
significant prognosis importance in the population.
Chest X-ray images can identify it. Early detection of
cardiomegaly reduces the risk of congestive heart
failure and systolic dysfunction. Due to the lack of
radiologists, there is a demand for the artificial
intelligence tool for the early detection of
cardiomegaly. The cardiomegaly X-ray dataset is
extracted from the cheXpert database. Totally, 46195
X-ray records with a different view such as AP view, PA
views, and lateral views are used to train and validate
the proposed model. The artificial intelligence app
named CardioXpert is constructed based on deep neural
network. The transfer learning approach is adopted to
increase the prediction metrics, and an optimized
training method called adaptive movement estimation is
used. Three different transfer learning-based deep
neural networks named APNET, PANET, and LateralNET are
constructed for each view of X-ray images. Finally,
certainty-based fusion is performed to enrich the
prediction accuracy, and it is named CardioXpert. As
the proposed method is based on the largest
cardiomegaly dataset, hold-out validation is performed
to verify the prediction accuracy of the proposed
model. An unseen dataset validates the model. These
deep neural networks, APNET, PANET, and LateralNET, are
individually validated, and then the fused network
CardioXpert is validated. The proposed model
CardioXpert provides an accuracy of 93.6\%, which is
the highest at this time for this dataset. It also
yields the highest sensitivity of 94.7\% and a
precision of 97.7\%. These prediction metrics prove
that the proposed model outperforms all the
state-of-the-art deep transfer learning methods for
diagnosing cardiomegaly thoracic disorder. The proposed
deep learning neural network model is deployed as the
web app. The cardiologist can use this prognostic app
to predict cardiomegaly disease faster and more robust
in the early state by using low-cost and chest X-ray
images.",
acknowledgement = ack-nhfb,
articleno = "2350033",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ainapure:2023:DEM,
author = "Bharati S. Ainapure and Mythili Boopathi and Chandra
Sekhar Kolli and And C. Jackulin",
title = "Deep Ensemble Model for Spam Classification in
{Twitter} via Sentiment Extraction:
Bio-Inspiration-Based Classification Model",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500341",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500341",
abstract = "Twitter Spam has turned out to be a significant
predicament of these days. Current works concern on
exploiting the machine learning models to detect the
spams in Twitter by determining the statistic features
of the tweets. Even though these models result in
better success, it is hard to sustain the performances
attained by the supervised approaches. This paper
intends to introduce a deep learning-assisted spam
classification model on twitter. This classification is
based on sentiments and topics modeled in it. The
initial step is data collection. Subsequently, the
collected data are preprocessed with ``stop word
removal, stemming and tokenization''. The next step is
feature extraction, wherein, the post tagging,
headwords, rule-based lexicon, word length, and
weighted holoentropy features are extracted. Then, the
proposed sentiment score extraction is carried out to
analyze their variations in nonspam and spam
information. At last, the diffusions of spam data on
Twitter are classified into spam and nonspams. For
this, an Optimized Deep Ensemble technique is
introduced that encloses ``neural network (NN), support
vector machine (SVM), random forest (RF) and
convolutional neural network (DNN)''. Particularly, the
weights of DNN are optimally tuned by an arithmetic
crossover-based cat swarm optimization (AC-CS) model.
At last, the supremacy of the developed approach is
examined via evaluation over extant techniques.
Accordingly, the proposed AC-CS + ensemble model
attained better accuracy value when the learning
percentage is 80, which is 18.1\%, 14.89\%, 11.7\%,
12.77\%, 10.64\%, 6.38\%, 6.38\%, and 6.38\% higher
than SVM, DNN, RNN, DBN, MFO + ensemble model, WOA +
ensemble model, EHO + ensemble model and CSO + ensemble
model models.",
acknowledgement = ack-nhfb,
articleno = "2350034",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lata:2023:DDL,
author = "Navdeep Lata and Raman Kumar",
title = "{DSIT}: a Dynamic Lightweight Cryptography Algorithm
for Securing Image in {IoT} Communication",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500353",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500353",
abstract = "One of the most significant challenges appears to be
securing the Internet of Things (IoT) communication
network. As a corollary, information security has
become the basis for establishing trustworthiness in
IoT network communication. Cryptography is one of the
ways for securing information in this case. However,
the majority of current approaches are static, making
them subject to security threats. As a consequence, a
new concept, dynamic encryption, is growing rapidly in
IoT communication. In this paper, a dynamic encryption
algorithm (DSIT) has been proposed to secure IoT
communication. This algorithm is based on Feistel and
Substitution--Permutation Network. DSIT is a block
cipher that takes the 64-bit block of plaintext, 64-bit
secret key, and a secret dynamic box (D-box) as input.
It produces a 64-bit ciphertext by performing eight
rounds of the DSIT algorithm. For each round, the key
and D-box are updated. This dynamic effect provides
high security to a dynamic IoT network. The proposed
algorithm has been executed in IoT environment using
Raspberry Pi 3 Model B + and 50\% average Avalanche
effect has been achieved. The proposed algorithm
efficiently encrypts the image data to secure the
communication and high resistant to cryptanalysis
attacks.",
acknowledgement = ack-nhfb,
articleno = "2350035",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dahiya:2023:RDL,
author = "Neelam Dahiya and Sartajvir Singh and And Sheifali
Gupta",
title = "A Review on Deep Learning Classifier for Hyperspectral
Imaging",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500365",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500365",
abstract = "Nowadays, hyperspectral imaging (HSI) attracts the
interest of many researchers in solving the remote
sensing problems especially in various specific domains
such as agriculture, snow/ice, object detection and
environmental monitoring. In the previous literature,
various attempts have been made to extract the critical
information through hyperspectral imaging which is not
possible through multispectral imaging (MSI). The
classification in image processing is one of the
important steps to categorize and label the pixels
based on some specific rules. There are various
supervised and unsupervised approaches which can be
used for classification. Since the past decades,
various classifiers have been developed and improved to
meet the requirement of remote sensing researchers.
However, each method has its own merits and demerits
and is not applicable in all scenarios. Past literature
also concluded that deep learning classifiers are more
preferable as compared to machine learning classifiers
due to various advantages such as lesser training time
for model generation, handle complex data and lesser
user intervention requirements. This paper aims to
perform the review on various machine learning and deep
learning-based classifiers for HSI classification along
with challenges and remedial solution of deep learning
with hyperspectral imaging. This work also highlights
the various limitations of the classifiers which can be
resolved with developments and incorporation of
well-defined techniques.",
acknowledgement = ack-nhfb,
articleno = "2350036",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Birajdar:2023:SSP,
author = "Gajanan K. Birajdar and Mukesh D. Patil",
title = "A Systematic Survey on Photorealistic Computer Graphic
and Photographic Image Discrimination",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "04",
pages = "??--??",
month = jul,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500377",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Aug 5 16:18:20 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500377",
abstract = "The advent in graphic rendering software and
technological progress in hardware can generate or
modify photorealistic computer graphic (CG) images that
are difficult to identify by human observers.
Computer-generated images are used in magazines, film
and advertisement industry, medical and insurance
agencies, social media, and law agencies as an
information carrier. The forged computer-generated
image created by the malicious user may distort social
stability and impacts on public opinion. Hence, the
precise identification of computer graphic and
photographic image (PG) is a significant and
challenging task. In the last two decades, several
researchers have proposed different algorithms with
impressive accuracy rate, including a recent addition
of deep learning methods. This comprehensive survey
presents techniques dealing with CG and PG image
classification using machine learning and deep
learning. In the beginning, broad classification of all
the methods in to five categories is discussed in
addition to generalized framework of CG detection.
Subsequently, all the significant works are surveyed
and are grouped into five types: image statistics
methods, acquisition device properties-based
techniques, color, texture, and geometry-based methods,
hybrid methods, and deep learning methods. The
advantages and limitations of CG detection methods are
also presented. Finally, major challenges and future
trends in the CG and PG image identification field are
discussed.",
acknowledgement = ack-nhfb,
articleno = "2350037",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Ravikumar:2023:AMS,
author = "S. Ravikumar and E. Kannan",
title = "Analysis on Mental Stress of Professionals and
Pregnant Women Using Machine Learning Techniques",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467823500389",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500389",
abstract = "Stress is the way that everyone can respond actually,
intellectually and sincerely to different conditions,
changes and requests in our lives. Stress problems are
a typical issue among working experts in the business
today. With changing way of life and work societies,
there is an expansion in the stress among the
representatives. However, numerous ventures and
corporate give emotional wellness-related plans and
attempt to facilitate the work environment climate, the
issue is a long way from control. When it comes to
Pregnant Women, the uterus climate assumes a
fundamental part in future development and improvement
of hatchling. Stress during pregnancy will influence
the sensitive climate of the hatchling. These can
remember impacts for your unborn child's development
and the length of incubation period. They can likewise
expand the danger of issues in your child's future
physical and mental turn of events, just as social
issues in youth. By using various machine learning
techniques, the proposed model can analyze the stress
in a working professional and also in a pregnant woman.
We can predict the best way of yoga to reduce their
stress and get good work results from working employees
and a good growth in fetus of a pregnant women. Yoga
can positively affect the parasympathetic sensory
system and helps in bringing down heartbeat and
circulatory strain. This decreases the interest of the
body for oxygen and furthermore increment lung limit.
Compelling utilization of yoga can likewise decrease
the odds of stress, nervousness and despondency.",
acknowledgement = ack-nhfb,
articleno = "2350038",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sadaghiani:2023:IIB,
author = "Abdol Vahab Khalili Sadaghiani and Samad Sheikhaei and
And Behjat Forouzandeh",
title = "Image Interpolation Based on {$2$D-DWT} with Novel
Regularity-Preserving Algorithm Using {RLS} Adaptive
Filters",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500390",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500390",
abstract = "This paper proposes a novel method for the image
interpolation problem based on two-dimensional discrete
wavelet transform (DWT) with the edge preserving
approach. The purpose of this method is to consider two
contrasting issues of over-smoothing and creation of
spurious edges at the same time, and offer a novel
solution based on statistical dependencies of image
sub-bands, and noise behavior. The offered method has a
multi-faceted approach for the problem; by sub-band
coding, it handles each 2D-DWT image sub-band with a
different solution. For LH and HL sub-bands, two
algorithms work together in order to preserve
regularity. Area\_Check algorithm is a four-phase
edge-preserving algorithm that aims to recognize and
interpolate separating lines of environments and edgy
regions in the best possible way. On the other hand,
RLS\_AVG algorithm interpolates smooth surfaces of the
image by keeping the regularity of the image without
over-smoothing. In this regard, the offered algorithm
has a great power to counter jaggies and annoying
artifacts. In the end, in order to demonstrate the
capability, and performance of the proposed method, the
final results in various metrics are compared with the
results of the most famous and the newest image
interpolation methods.",
acknowledgement = ack-nhfb,
articleno = "2350039",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gill:2023:NIG,
author = "Jasmeen Gill and Ravinder Pal Singh",
title = "Non-Invasive Grading and Sorting of Mango
(\bioname{Mangiferad indica} {L.}) Using Antlion
Optimizer-Based Artificial Neural Networks",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500407",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500407",
abstract = "Mango is an imperative commercial fruit in terms of
market value and volume of production. In addition, it
is grown in more than ninety nations around the globe.
Consequently, the demand for effective grading and
sorting has increased, ever since. This communication
describes a non-invasive mango fruit grading and
sorting model that utilizes hybrid soft computing
approach. Artificial neural networks (ANN), optimized
with Antlion optimizer (ALO), are used as a
classification tool. The quality of mangoes is
evaluated according to four grading parameters: size
(volume and morphology), maturity (ripe/unripe), defect
(defective/healthy) and variety (cultivar). Besides, a
comparison of proposed grading system with
state-of-the-art models is performed. The system showed
an overall classification rate of 95.8\% and
outperformed the other models. Results demonstrate the
effectiveness of proposed model in fruit grading and
sorting applications.",
acknowledgement = ack-nhfb,
articleno = "2350040",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Padmavathi:2023:WCE,
author = "P. Padmavathi and J. Harikiran",
title = "Wireless Capsule Endoscopy Infected Images Detection
and Classification Using {MobileNetV2-BiLSTM} Model",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500419",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500419",
abstract = "An efficient tool to execute painless imaging and
examine gastrointestinal tract illnesses of the
intestine is also known as wireless capsule endoscopy
(WCE). Performance, safety, tolerance, and efficacy are
the several concerns that make adaptation challenging
and wide applicability. In addition, to detect
abnormalities, the great importance is the automatic
analysis of the WCE dataset. These issues are resolved
by numerous vision-based and computer-aided solutions.
But, they want further enhancements and do not give the
accuracy at the desired level. In order to solve these
issues, this paper presents the detection and
classification of WCE infected images by a deep neural
network and utilizes a bleed image recognizer (BIR)
that associates the MobileNetV2 design to classify the
images of WCE infected. For the opening-level
evaluation, the BIR uses the MobileNetV2 model for its
minimum computation power necessity, and then the
outcome is sent to the CNN for more processing. Then,
Bi-LSTM with an attention mechanism is used to improve
the performance level of the model. Hybrid attention
Bi-LSTM design yields more accurate classification
outcomes. The proposed scheme is implemented in the
Python platform and the performance is evaluated by
Cohen's kappa, F1-score, recall, accuracy, and
precision. The implementation outcomes show that the
introduced scheme achieved maximum accuracy of 0.996
with data augmentation with the dataset of WCE images
which provided higher outcomes than the others.",
acknowledgement = ack-nhfb,
articleno = "2350041",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rajkumar:2023:DLF,
author = "Rajeev Rajkumar",
title = "Deep Learning Feature Extraction Using Attention-Based
{DenseNet 121} for Copy Move Forgery Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500420",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500420",
abstract = "In modern society, digital images can be far-reaching,
and the images are manipulated by various software and
hardware technologies. The image forgery activities are
undertaken by the attackers mainly for damaging the
reputation of people or receiving fiscal gain, etc.
Taking this into consideration, many techniques are
developed to detect the forged images. In this paper, a
new deep learning-based approach is introduced for
copy-move forgery detection. The input images are
segmented into non-overlapping patches using
superpixel-based modified dense peak clustering and the
features are extracted from the segmented patches by
applying deep learning structure of attention-based
DenseNet 121 model. Besides, to compare every block,
the depth of each pixel is reconstructed, and
eventually matching process is carried out using the
adaptive chimp patch matching approach, which detects
the suspicious forged regions in an image. Finally, the
matched keypoints are merged with the segmented patches
using the merged keypoint matching algorithm. As a
result, the new deep learning approach has detected the
forged regions efficiently from the tampered image with
less time even the image is compressed, rotated, or
scaled. The performance is evaluated in terms of
recall, precision, accuracy, F1-score, computational
time, and False Positive Rate (FPR). Moreover, the
performance is compared with the other existing
approaches, and the outcomes showed that the proposed
method has achieved higher accuracy of 97\%, recall of
99\%, precision of 97.84\%, F1-score of 98.81\%, FPR of
0.022 and less computational time of 2.5 s.",
acknowledgement = ack-nhfb,
articleno = "2350042",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kaur:2023:FIB,
author = "Rajdeep Kaur and Rakesh Kumar and And Meenu Gupta",
title = "Food Image-based Nutritional Management System to
Overcome Polycystic Ovary Syndrome using
{DeepLearning}: A Systematic Review",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500432",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500432",
abstract = "Polycystic Ovary Syndrome (PCOS) is one of the growing
non-communicable diseases in those women who do not
take proper nutrients in their meals. Medically, it is
not proven that an unhealthy diet is the only cause of
PCOS, but it is one of the major causes behind this
disease. PCOS is an endocrine disorder that influences
8--10\% of women at their reproductive age and may
cause infertility or other health problems. Deep
Learning (DL) is a popular technique to classify the
food images for identifying the nutrients in the food.
This work considers food image datasets (FOOD-101,
UEC-256, UEC-100, etc.) to analyze the food image using
pre-trained Convolutional Neural Network (CNN) and a
nutritional information dataset for identifying the
nutrients in food. The proposed study aims to find the
solution to overcome the PCOS problem in women by
tracking nutrient intake using food images and
recommending the diet. Further, this study will also
provide comprehensive review of image classification
and recommendation techniques that may help the
dieticians to track the nutrient intake using food
images provided by PCOS patients to overcome the
disease.",
acknowledgement = ack-nhfb,
articleno = "2350043",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Nnolim:2023:FOP,
author = "Uche A. Nnolim",
title = "Fourth-Order Partial Differential Equation Framelet
Fusion-Based Colour Correction and Contrast Enhancement
for Underwater Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500444",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500444",
abstract = "A framelet augmented fourth-order forward-reverse
partial differential equation (PDE)-fusion-based
algorithm is proposed for underwater image enhancement.
The algorithm combines framelet domain transform-based
fusion of modified base, detail and amplified detail
layers in a PDE-based formulation. The extracted layers
via framelet decomposition with adaptive threshold
computation comprise the detail and approximation
components of the images, which are amplified,
attenuated and aggregated. Additions include a modified
global contrast enhancement/color correction function
and a suitable color space transformation to enhance
difficult underwater images with flat non-overlapping
color channel histograms. Also, gradient domain fusion
of several color corrected image layers and fuzzy
rule-based enhancement is combined in the proposed
PDE-based fusion framework. Furthermore, variational
illumination correction was also employed for better
enhancement of dark underwater images. Experimental
comparisons indicate that the proposed approaches yield
better overall visual and numerical results in most
cases when compared with state-of-the-art methods.",
acknowledgement = ack-nhfb,
articleno = "2350044",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gavade:2023:HFD,
author = "Priyanka A. Gavade and Vandana S. Bhat and And
Jagadeesh Pujari",
title = "Hybrid Features and Deep Learning Model for Facial
Expression Recognition From Videos",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500456",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500456",
abstract = "Facial expression recognition plays a crucial function
in the advancement of technologies that can be used in
detecting mental illness, sensors, and a wide variety
of applications. Facial expression recognition is an
interesting as well as strenuous task in digital field
due to the complexity of the varying individuals. The
intention of this work is to establish a face
recognition model relying upon the modified GWO-based
ensemble deep convolutional neural network (DCNN),
which effectively recognizes the expressions. The
substance of the research anticipates on the proposed
modified GWO optimization which helps in maintaining
the storage capacity with simple structures and
provides high convergence. Enabling the optimization in
the ensemble DCNN helps in tuning the internal
parameters present in the classifier as well as helps
in attaining best solution. The accomplishment of the
proposed expression recognition model is evaluated
utilizing the parameter metrics accuracy, precision,
and recall that attained the values of 94.114\%,
92.003\%, and 95.734\% which is more efficient.",
acknowledgement = ack-nhfb,
articleno = "2350045",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Vishnuvardhan:2023:MIF,
author = "Veruva Vishnuvardhan and T. Jaya",
title = "Medical Image Fusion using {ECNN}- and {OMBO}-based
Adaptive Weighted Fusion Rule",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500468",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500468",
abstract = "Medical imaging and information processing
technologies are constantly evolving, resulting in a
wide range of multimodality therapeutic pictures for
clinical illness investigation. Physicians often
require medical images produced using various
modalities such as computed tomography (CT), magnetic
resonance (MR), and positron emission computed
tomography (PET) for clinical diagnosis. Many deep
learning-based fusion methods have recently been
proposed. In Convolutional Neural Network (CNN)-based
fusion methods, only the last layer results are used as
the image features, which result in the loss of useful
information at middle layers. The fusion rule, based on
the weighted averaging, causes noises in the source
images and suppresses salient features of the image. In
order to solve these issues, this paper proposes
medical image fusion using Enhanced CNN (ECNN)- and
Opposition-based Monarch Butterfly Optimization
(OMBO)-based adaptive weighted fusion rule (AWFR). The
ECNN contains feature extraction and reconstruction
components. Both these components are trained in order
to minimize the pixel loss and structural similarity
loss. A pair of multimodal medical image is passed as
input to the ECNN model to extract the low level and
high level features. For the extracted features from
ECNN, weighted fusion rule is applied in which OMBO
algorithm is applied to adaptively optimize the weights
of the fusion rule.",
acknowledgement = ack-nhfb,
articleno = "2350046",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jameel:2023:BAI,
author = "Samer Kais Jameel and Jafar Majidpour",
title = "{BCS-AE}: Integrated Image Compression-Encryption
Model Based on {AE} and {Block-CS}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S021946782350047X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782350047X",
abstract = "For Compressive Sensing problems, a number of
techniques have been introduced, including traditional
compressed-sensing (CS) image reconstruction and Deep
Neural Network (DNN) models. Unfortunately, due to low
sampling rates, the quality of image reconstruction is
still poor. This paper proposes a lossy image
compression model (i.e. BCS-AE), which combines two
different types to produce a model that uses more
high-quality low-bitrate CS reconstruction. Initially,
block-based compressed sensing (BCS) was utilized, and
it was done one block at a time by the same operator.
It can correctly extract images with complex geometric
configurations. Second, we create an AutoEncoder
architecture to replace traditional transforms, and we
train it with a rate-distortion loss function. The
proposed model is trained and then tested on the CelebA
and Kodak databases. According to the results, advanced
deep learning-based and iterative optimization-based
algorithms perform better in terms of compression ratio
and reconstruction quality.",
acknowledgement = ack-nhfb,
articleno = "2350047",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Preetham:2023:SMR,
author = "Anusha Preetham and Vishnu Vardhan Battu",
title = "Soil Moisture Retrieval Using Sail Squirrel Search
Optimization-based Deep Convolutional Neural Network
with {Sentinel-1} Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500481",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500481",
abstract = "Soil Moisture (SM) is an environmental descriptor,
which acts as the affiliation between the atmosphere
and the earth's surface. Various SM retrieval methods
are developed to abolish the influence of vegetation
cover attenuation, surface roughness, and scattering to
find an association among SM and backscatter
coefficient. To understand the relationship between
various vegetation parameters and backscatter
coefficient poses a great challenge in SM retrieval.
Hence, an efficacious SM retrieval method is afforded
using the proposed Sail Squirrel Search
Optimization-based Deep Convolutional Neural Network
(SSSO-based Deep CNN). Here, the proposed SSSO is
derived by concatenating the Sail Fish Optimization
(SFO) with Squirrel Search Algorithm (SSA). The Deep
CNN performs the process of SM retrieval using
vegetation indices. The fitness measure of the proposed
optimization enables to find the best solution to
update the weights of the classifier for increasing the
efficiency of the retrieval mechanism. By training Deep
CNN with the proposed optimization, the soil moisture
of an area is effectively retrieved. However, the
proposed SSSO-based Deep CNN obtained minimal
estimation error and minimal RMSE of 0.550 and 0.726
using sentinel-1 data, respectively.",
acknowledgement = ack-nhfb,
articleno = "2350048",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gantenapalli:2023:SMF,
author = "Srinivasa Rao Gantenapalli and Praveen Babu Choppala
and And James Stephen Meka",
title = "Selective Mean Filtering for Reducing Impulse Noise in
Digital Color Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500493",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500493",
abstract = "The interest of this paper is in reduction of impulse
noise in digital color images. The two main methods
used for noise reduction in images are the mean and
median filters. These techniques operate by replacing
the test pixel in a chosen window by a new filtered
pixel value. The window is made to iteratively slide
across the entire image to reconstruct a new noise
reduced image. The mean filters suffer from the effect
of smoothing out color contrast and edges due to
leveraging the unrepresentative pixels in the filtering
process. The vector median filter and its variants
overcome this problem by considering only the most
representative pixel in the chosen window. The most
representative pixel, i.e. the pixel that is of highest
conformity to take the place of the test pixel, is
determined by minimizing the aggregate distance from
one pixel to every other pixel in the window. The
problem in these median filtering approaches is that
only one pixel is treated as representative of all the
pixels in the chosen window. This conjecture could lead
to information loss due to marginalizing other pixels
that also are representative of the center pixel. In
this paper, we propose a selective mean filtering
process to overcome the said problem. The key idea here
is to determine the most representative pixels in the
window using the method of aggregate distances and then
compute the mean of these pixels. This approach will
perform better than the vector median filters as now a
set of representative pixels are leveraged into the
filtering process. Simulation results show that the
proposed method performs better than the conventional
vector median filtering methods in terms of noise
reduction and structural similarity and thus validates
the proposed approach. Moreover, the method is tested
on real MRI scan images in successfully reducing
impulse noise for improved medical diagnosis.",
acknowledgement = ack-nhfb,
articleno = "2350049",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wang:2023:TIN,
author = "Xin Wang and Xiaogang Dong",
title = "Time Image De-Noising Method Based on Sparse
Regularization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "05",
pages = "??--??",
month = sep,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467825500093",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Fri Oct 13 07:20:29 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500093",
abstract = "The blurring of texture edges often occurs during
image data transmission and acquisition. To ensure the
detailed clarity of the drag-time images, we propose a
time image de-noising method based on sparse
regularization. First, the image pixel sparsity index
is set, and then an image de-noising model is
established based on sparse regularization processing
to obtain the neighborhood weights of similar image
blocks. Second, a time image de-noising algorithm is
designed to determine whether the coding coefficient
reaches the standard value, and a new image de-noising
method is obtained. Finally, the images of electronic
clocks and mechanical clocks are used as two kinds of
time images to compare different image de-noising
methods, respectively. The results show that the
sparsity regularization method has the highest peak
signal-to-noise ratio among the six compared methods
for different noise standard deviations and two time
images. The image structure similarity is always above
which shows that the proposed method is better than the
other five image de-noising methods.",
acknowledgement = ack-nhfb,
articleno = "2550009",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Salman:2023:FCI,
author = "Khalid A. Salman and Khalid Shaker and And Sufyan
Al-janabi",
title = "Fake Colorized Image Detection Approaches: a Review",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946782350050X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782350050X",
abstract = "Colorization is a process used in image editing in
which grayscale images are colored with realistic
colors. Modern techniques of colorization could produce
artfully colored images in such a way that it is
difficult for human eyes to differentiate between
actual and fake colorized images. As a result,
identifying fraudulent colored pictures has captured
the scientific community's attention in digital
forensics. This paper provides an overview of the
strategies used for detecting fake colorized images.
Mainly, two approaches were used to design fake
colorized image detection systems. The first one uses
traditional machine learning (ML) techniques that rely
on hand-crafted features derived from images and used
to differentiate actual and fake images. The second
approach uses deep learning (DL) techniques as ``end to
end'' systems that don't have to be supplied with such
hand-crafted features, as they can learn the features
from the image directly. This paper focuses on the
various methods and techniques used in fake-colorized
image detection. It may aid researchers in better
understanding the benefits and drawbacks of existing
technologies to develop more efficient systems in this
field.",
acknowledgement = ack-nhfb,
articleno = "2350050",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Singh:2023:RML,
author = "Shaminder Singh and Anuj Kumar Gupta and And Tanvi
Arora",
title = "A Review of Machine Learning-Based Recognition of Sign
Language",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500511",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500511",
abstract = "Some people in society have impaired cognitive senses
like speech and hearing where they cannot behave like
normal people. It is quite a complex task for abnormal
people to understand as well as recognize the gestures
of normal people. This initiates to delve into the
study of review of Sign Language Recognition (SLR), in
specific to, machine learning techniques. In this work,
a review of machine learning techniques based on SLR
were portrayed. Several studies related to ML papers
have been collected and discussed with their merits and
demerits. Thus, the observation dictates that
recognition of hand gesture is still a challenging
task. There are two sorts of gesture recognition,
namely, static and dynamic gesture recognition. Static
gesture recognition is developed from the dynamic
gesture recognition. Almost, Convolutional Neural
Networks (CNNs), Hidden Markov Models (HMM) and
Histogram analysis were used as recognition classifiers
for sign language. Dynamic gesture recognition process
operates on tracking the centroid of hand gesture. It
changes the visual information in time basis.
Henceforth, study on dynamic gesture recognition needs
to be more focused using Machine learning techniques.
Comparative analysis is done in perspectives of
significance of segmentation models, feature extraction
and vision-based approaches.",
acknowledgement = ack-nhfb,
articleno = "2350051",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sharma:2023:VAM,
author = "Tejpal Sharma and Dhavleesh Rattan",
title = "Visualizing {Android} Malicious Applications Using
Texture Features",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500523",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500523",
abstract = "Context: Due to the change and advancement in
technology, day by day the internet service usages are
also increasing. Smartphones have become the necessity
for every person these days. It is used to perform all
basic daily activities such as calling, SMS, banking,
gaming, entertainment, education, etc. Therefore,
malware authors are developing new variants of malwares
or malicious applications especially for monetary
benefits. Objective: Objective of this research paper
is to develop a technique that can be used to detect
malwares or malicious applications on the android
devices that will work for all types of packed or
encrypted malicious applications, which usually evade
decompiling tools. Method: In the proposed approach,
visualization method is used for the detection of
malware. In the first phase, application files are
converted into images and then in second phase, texture
feature of images are extracted using Grey Level
Co-occurrence Matrix (GLCM). In the last phase, machine
learning classification algorithms are used to classify
the malicious and benign applications. Results: The
proposed approach is run on different datasets
collected from various repositories. Different
efficiency parameters are calculated and the proposed
approach is compared with the existing approaches.
Conclusion: We have proposed a static technique for
efficient detection of malwares. The proposed technique
performs better than the existing technique.",
acknowledgement = ack-nhfb,
articleno = "2350052",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wankhade:2023:ONN,
author = "Megha M. Wankhade and Suvarna S. Chorage",
title = "Optimized Neural Network with Refined Features for
Categorization of Motor Imaginary Signals",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500535",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500535",
abstract = "Motor imaginary (MI) is an attractive research field
in the brain--computer interfaces (BCIs) function, in
which the system is directed by the imaginary arm
movement of the subject. This attention is due to the
monstrous potential for its pertinence in
neurorestoration, neuroprosthetics, and gaming, where
the client's considerations of envisioned developments
should be decoded. An electroencephalography (EEG)
device is regularly utilized for monitoring frontal
cortex movements in BCI frameworks. The EEG signals are
perceived through the two fundamental processes such as
feature extraction and characterization process. This
research concentrates on developing a predominant MI
categorization model utilizing deep learning
techniques. The prominence of this research relies on
the combined features + proposed PROA-based RideNN
process known as holo-entropy-based WPD, which extracts
the most dominant feature from the EEG signals. The
extracted features enhance the performance of the
RideNN classifier. The analysis is done by utilizing
the BCI Competition-IV-2a, -2b, and GigaScience
datasets with respect to performance parameters, such
as specificity, accuracy, and sensitivity. The analysis
revealed the effective performance of the proposed
method with respect to the existing state-of-art
methodologies.",
acknowledgement = ack-nhfb,
articleno = "2350053",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yechuri:2023:GAB,
author = "Sivaramakrishna Yechuri and Sunny Dayal Vanabathina",
title = "Genetic Algorithm-Based Adaptive {Wiener} Gain for
Speech Enhancement Using an Iterative Posterior {NMF}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500547",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500547",
abstract = "In this paper, we propose a genetic algorithm-based
adaptive Wiener gain for speech enhancement using an
iterative posterior non-negative matrix factorization
(NMF). In the recent past, NMF-based Wiener filtering
methods were used to improve the performance of speech
enhancement, which has shown that they provide better
performance when compared with conventional NMF
methods. But performance degrades in non-stationary
noise environments. Template-based approaches are more
robust and perform better in non-stationary noise
environments compared to statistical model-based
approaches but are dependent on {\em a priori\/}
information. Combining the approaches avoids the
drawbacks of both. To improve the performance further,
speech and noise bases are adapted simultaneously in
the NMF approach. The usage of Super-Gaussian
constraints in iterative NMF still improves the
performance in non-stationary noise. The silence frame
is a challenging task in the case of NMF; still there
will be some amount of noise present in those frames.
For further enhancement, we have combined with a
genetic algorithm (GA)-based adaptive Wiener filter
which performs well in denoising and also the GA search
the adaptive {\alpha} `` role=''presentation``{$>$}
{\textalpha} {\textalpha} {\textalpha} allows us to
control the trade-off between fitting the observed
spectrogram of mixed speech and noise achieving high
likelihood under our prior model. The proposed method
outperforms other benchmark algorithms in terms of the
source to distortion ratio (SDR), short-time objective
intelligibility (STOI), and perceptual evaluation of
speech quality (PESQ).",
acknowledgement = ack-nhfb,
articleno = "2350054",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jia:2023:RTM,
author = "Baojian Jia and Jie Ren",
title = "Real-time Multi-person Pose Tracking Method Using Deep
Reinforcement Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500559",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500559",
abstract = "To address the problem of low tracking accuracy caused
by many recognized objects in the existing methods, we
propose a real-time multi-person pose tracking method
using deep reinforcement learning. First, the
convolutional neural network (CNN) is used to predict
the human key points and center vector in grid mode,
make the human key points point to the human center
according to the center vector, group the human key
points according to the distance from the human key
points to the human center, complete the multi-person
pose estimation, and obtain the human pose sequence
diagram. Then, the human pose sequence diagram is input
into the deep reinforcement learning network, and the
pose label and category label are output by the
supervised learning and training stage. The best pose
tracking strategy obtained in the reinforcement
learning and training stage is applied to online
tracking. Finally, CNN is used to predict the
rectangular frame position of the pose instead of the
target pose, and the tracking is completed when the
pose stops. At this time, the rectangular frame
position is the result of multi-person pose tracking.
The results show that the maximum expected average
overlap (EAO) of the proposed method is 0.53. When the
root mean square error (RMSE) of the position component
threshold reaches 8, the accuracy has been stable at
0.98\%. Therefore, the proposed method has high
tracking accuracy. In the future, it can be applied to
smart home scenarios to realize smart home human pose
tracking, effectively identify human dangerous pose and
ensure residents' life safety.",
acknowledgement = ack-nhfb,
articleno = "2350055",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sukanya:2023:DLB,
author = "S. T. Sukanya and S. Jerine",
title = "Deep Learning-Based Melanoma Detection with Optimized
Features via Hybrid Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500560",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500560",
abstract = "Recently, there had been a massive group of people,
who were being rapidly affected by melanoma. Melanoma
is a form of skin cancer that develops on the skin's
surface layer. This is primarily caused due to
excessive skin exposure to UV radiation and severe
sunburns. Thus, the early detection of melanoma can aid
us to cure it completely. This paper intends to
introduce a new melanoma detection framework with four
main phases {\em viz.\/} segmentation, feature
extraction, optimal feature selection, as well as
detection. Initially, the segmentation process takes
place to the input skin image {\em via\/} Fuzzy C-Means
Clustering (FCM) approach. From the segmented image $ I
m_{\rm seg} $ (Imseg), some of the features such as
Gray Level Run Length Matrix (GLRM), Local Vector
Pattern (LVP), Local Binary Pattern (LBP), Local
Directional Pattern (LDP) and Local Tetra Pattern
(LTrP) are extracted. As the extracted features $F$ (F)
suffered from the issue of ``curse of dimensionality'',
this paper utilizes optimization to select optimal
features, which makes the detection more precise. As a
novelty, a new hybrid algorithm Particle-Assisted Moth
Search Algorithm (PA-MSA) is introduced that hybridizes
the concept of Moth Search Algorithm (MSA) and Particle
Swarm Optimization (PSO), respectively. For the
classification process, the optimally chosen features $
F_{\rm opt}$ (Fopt) are fed as input, where Deep
Convolution Neural Network (DCNN) is used. Finally, a
performance-based comparative analysis is conducted
among the proposed PA-MSA as well as the existing
models with respect to various measures.",
acknowledgement = ack-nhfb,
articleno = "2350056",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Padate:2023:WAO,
author = "Roshni Padate and Amit Jain and Mukesh Kalla and And
Arvind Sharma",
title = "A Widespread Assessment and Open Issues on Image
Captioning Models",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500572",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500572",
abstract = "Automated generation of image captions is a demanding
AI crisis as it necessitates the exploitation of
numerous methods from diverse computer science fields.
Deep learning (DL) approaches have revealed marvelous
results in a lot of diverse appliances. On the other
hand, data augmentation in DL that imitates the
quantity and the variety of training data without the
need of gathering additional data is a hopeful area in
machine learning (ML). Producing textual descriptions
for a specified image is a demanding task using the
computer. This survey makes a critical analysis of
about 65 papers regarding image captioning. More
particularly, varied performance measures that are
contributed in diverse articles are analyzed. In
addition, a comprehensive study is made regarding the
maximal performances and varied features deployed in
each work. Moreover, chronological analysis and dataset
analysis are done and finally, the survey extends with
the determination of varied research challenges, which
might be productive for the analysts to endorse
enhanced upcoming works on image captioning.",
acknowledgement = ack-nhfb,
articleno = "2350057",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Patel:2023:DLE,
author = "Miral Jerambhai Patel and Ashish M. Kothari",
title = "Deep Learning-Enabled Road Segmentation and
Edge-Centerline Extraction from High-Resolution Remote
Sensing Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500584",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500584",
abstract = "Nowadays, precise and up-to-date maps of road are of
great significance in an extensive series of
applications. However, it automatically extracts the
road surfaces from high-resolution remote sensed images
which will remain as a demanding issue owing to the
occlusion of buildings, trees, and intricate
backgrounds. In order to address these issues, a robust
Gradient Descent Sea Lion Optimization-based U-Net
(GDSLO-based U-Net) is developed in this research work
for road outward extraction from High Resolution (HR)
sensing images. The developed GDSLO algorithm is newly
devised by the incorporation of Stochastic Gradient
Descent (SGD) and Sea Lion Optimization Algorithm
(SLnO) algorithm. Input image is pre-processed and
U-Net is employed in road segmentation phase for
extracting the road surfaces. Meanwhile, training data
of U-Net has to be done by using the GDSLO optimization
algorithm. Once road segmentation is done, road edge
detection and road centerline detection is performed
using Fully Convolutional Network (FCN). However, the
developed GDSLO-based U-Net method achieved superior
performance by containing the estimation criteria,
including precision, recall, and F1-measure through
highest rate of 0.887, 0.930, and 0.809,
respectively.",
acknowledgement = ack-nhfb,
articleno = "2350058",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sajitha:2023:AVV,
author = "A. S. Sajitha and S. Sridevi Sathya Priya",
title = "Analysis of Various Visual Cryptographic Techniques
and their Issues Based on Optimization Algorithms",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500596",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500596",
abstract = "Visual Cryptography (VC) is a process employed for the
maintenance of secret information by hiding the secret
messages that are embedded within the images.
Typically, an image is partitioned into a number of
shares that are stacked over one another in order to
reconstruct back the original image accurately. The
major limitation that existed in the traditional VC
techniques is pixel expansion, in which pixel expansion
is replaced with a number of sub-pixels in individual
share, which causes a considerable impact on the
contrast and resolution of the image that further
gradually decreases the quality of the image. VC is
named for its essential characteristics, such as
transmitting the images with two or more shares with an
equal number of black pixels and color pixel
distribution. The secret message can be decrypted using
Human Visual System (HVS). In this paper, 50 research
papers are reviewed based on various classification
algorithms, which are effectively used for the VC
technique. The classification algorithms are
categorized into three types, namely, meta-heuristic,
heuristic, and evolutionary, and the research issues
and challenges confronted by the existing techniques
are reported in this survey. Moreover, the analysis is
done based on the existing research works by
considering the classification algorithms, tools, and
evaluation metrics.",
acknowledgement = ack-nhfb,
articleno = "2350059",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mustafa:2023:QDD,
author = "Adnan A. Mustafa",
title = "Quick Dissimilarity Detection for Center-Based Binary
Images Via Smart Mapping",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500602",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500602",
abstract = "In this paper, we present three different smart
mapping schemes that improve on the quickness of
dissimilarity detection between images. We call the
mapping schemes {\em smart\/} because the mapping order
is setup intelligently to detect dissimilarity quickly
by concentrating its search near the center of the
images, which is usually the region of interest in a
given scene. Thus, smart mapping is well suited for
images when the differences between them are expected
to be concentrated near the center of the image. We
construct a mapping vector (MV) that contains an
ordered list of point mappings which is employed to map
points between images in an efficient manner. The focus
in this paper is on applying the three different smart
mapping schemes to binary images. Furthermore, we test
three different mapping densities with each smart
mapping scheme and analyze the results. Tests are
conducted on two image sets and dissimilarity detection
results are compared to results obtained via random
mapping, which had been shown to be extremely fast, as
predicted by the probabilistic matching model for
binary images (PMMBI). We show that by employing smart
mapping a great improvement in dissimilarity detection
quickness is possible when dissimilarity between images
is concentrated near the center of the scene.",
acknowledgement = ack-nhfb,
articleno = "2350060",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sahu:2023:SDR,
author = "Geeta Abakash Sahu and Manoj Hudnurkar",
title = "Sarcasm Detection: a Review, Synthesis and Future
Research Agenda",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823500614",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823500614",
abstract = "A literature review on sarcasm detection has been
undergone in this research work. To have a meaningful
study about the existing works on sarcasm detection, a
total of 65 research papers have been analyzed in
diverse aspects like the datasets utilized, language,
pre-processing technique, type of features, feature
extraction technique, machine learning/deep
learning-based sarcasm classification. All these papers
belong to diverse international as well as national
journals. Moreover, the performance of each work in
terms of accuracy, {\em F\/} -score and recall will
also be manifested. To show the superiority of the
works, a comparative evaluation has been undergone in
terms of analyzed performances of each of the works.
Finally, the works that hold the superior or improved
values are furnished. In addition, the current
challenges faced by the sarcasm detection system are
portrayed, and this will be a milestone for future
researchers.",
acknowledgement = ack-nhfb,
articleno = "2350061",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2023:PIR,
author = "Yuan Liu",
title = "Product Image Recommendation with Transformer Model
Using Deep Reinforcement Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467825500202",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500202",
abstract = "A product image recommendation algorithm with
transformer model using deep reinforcement learning is
proposed. First, the product image recommendation
architecture is designed to collect users' historical
product image clicking behaviors through the log
information layer. The recommendation strategy layer
uses collaborative filtering algorithm to calculate
users' long-term shopping interest and gated recurrent
unit to calculate users' short-term shopping interest,
and predicts users' long-term and short-term interest
output based on users' positive and negative feedback
sequences. Second, the prediction results are fed into
the transformer model for content planning to make the
data format more suitable for subsequent content
recommendation. Finally, the planning results of the
transformer model are input to Deep Q-Leaning Network
to obtain product image recommendation sequences under
the learning of this network, and the results are
transmitted to the data result layer, and finally
presented to users through the presentation layer. The
results show that the recommendation results of the
proposed algorithm are consistent with the user's
browsing records. The average accuracy of product image
recommendation is 97.1\%, the maximum recommended time
is 1.0$s$ the coverage and satisfaction are high, and
the practical application effect is good. It can
recommend more suitable products for users and promote
the further development of e-commerce.",
acknowledgement = ack-nhfb,
articleno = "2550020",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2023:AIV,
author = "Anonymous",
title = "Author Index (Volume 23)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "23",
number = "06",
pages = "??--??",
month = nov,
year = "2023",
DOI = "https://doi.org/10.1142/S0219467823990012",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823990012",
acknowledgement = ack-nhfb,
articleno = "2399001",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kamble:2024:VUM,
author = "Tanaji Umaji Kamble and Shrinivas Padmakar Mahajan",
title = "{$3$D} Vision Using Multiple Structured Light-Based
{Kinect} Depth Cameras",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467824500013",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500013",
abstract = "Real-time 3D scanning of a scene or object using
multiple depth cameras is often required in many
applications but is still a challenging task for the
computer vision community, especially when the object
or scene is partially occluded and dynamic. If active
depth sensors are used in this case, their resulting
depth map quality gets degraded due to interference
between active radiations from each depth sensor.
Passive 3D sensors like stereo cameras can avoid the
issue of interference as they do not emit any active
radiation, but they face correspondence problems. Since
releasing the commodity depth sensor Microsoft Kinect,
researchers are getting more interested in active
depth-sensing. However, Kinect sensors have some easily
noticeable limitations concerning 3D reconstruction
such as: they can provide depth maps for a limited
range, their field of view is restricted and holes are
observed in the depth map due to occlusion. The
above-mentioned limitations can be overcome if multiple
Kinect sensors are used simultaneously instead of a
single Kinect sensor. Still, the challenge here is to
avoid interference between these sensors. We present a
comprehensive review of possible solutions to avoid
interference between multiple Kinect sensors.
Furthermore, we introduce the Kinect technology in
detail along with applications where multiple Kinect
sensors are used in the literature. We expect that this
paper will be helpful to the researchers who want to
use multiple Kinect sensors in sharing the workplace in
their research.",
acknowledgement = ack-nhfb,
articleno = "2450001",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Deepak:2024:ASI,
author = "A. V. S. Deepak and Umesh Ghanekhar",
title = "Analysis of Single Image Super-Resolution Techniques:
an Evolutionary Study",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500025",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500025",
abstract = "Single image super-resolution (SR) is a technique that
reconstructs a high-resolution (HR) image from a single
low-resolution (LR) input image. The main objective of
super-resolution algorithms is to achieve a
high-resolution image that is consistent with the input
low-resolution image but has enhanced spectral
properties. In this review, several research papers and
their corresponding algorithms have been reviewed and
are classified based on their methodology. The
principal objective of this review is to understand the
evolution of SISR techniques from basic interpolation
techniques to sophisticated convolutional neural
networks. This article also presents design
considerations for future advancements.",
acknowledgement = ack-nhfb,
articleno = "2450002",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Roy:2024:SRM,
author = "Srinjoy Roy and D. Binu and B. R. Rajakumar and
Vamsidhar Talasila and And Abhishek Bhatt",
title = "Super Resolved Maize Plant Leaves Disease Detection
Using Optimal Generative Adversarial Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500037",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500037",
abstract = "Agriculture plays a vital role in the economy and crop
disease causes huge financial losses every year. The
losses can be reduced by detecting the disease
accurately. The variation in light intensity and
complex background of the agricultural field in
detecting the maize leaves disease are the biggest
challenges. An optimization algorithm, named Cat Swarm
Political Optimizer Algorithm (CSPOA) has been
developed in this research to detect the disease of a
maize plant leaf. Our proposed algorithm is an
integration of the Cat Swarm Optimization (CSO) and
Political Optimizer (PO) algorithm. Anisotropic
filtering performs pre-processing for removing noise
and the Region of Interest (ROI) extraction for
enhancing the image quality. The super resolution image
is obtained from the Low Resolution (LR) images using
kernel regression model. After obtaining the super
resolution image, the salient map extraction has been
carried out for representing the saliency. Finally, the
maize plant leaves disease classification process is
done using General Adversarial Network (GAN) for
identifying the maize leaves disease. The training of
GAN develops the CSPOA. On comparing with the existing
maize plant leaves disease detection approaches, the
developed CSPOA-based GAN performed with a maximum
accuracy 0.9056, maximum sensitivity 0.9599, and the
maximum specificity 0.9592, respectively.",
acknowledgement = ack-nhfb,
articleno = "2450003",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Naik:2024:NSE,
author = "Manoj Kumar Naik and Monorama Swain and Rutuparna
Panda and And Ajith Abraham",
title = "Novel Square Error Minimization-Based Multilevel
Thresholding Method for {COVID-19} {X}-Ray Image
Analysis Using Fast Cuckoo Search",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500049",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500049",
abstract = "Coronavirus outbreaks in 2019 (COVID-19) have been a
huge disaster in the fields of health, economics,
education, and tourism in the last two years. For
diagnosis, a quick interpretation of the COVID-19 chest
X-ray image is required. There is also a strong need to
find an efficient multiclass segmentation technique for
the analysis of COVID-19 X-ray images. Most of the
threshold selection techniques are entropy-based.
Nevertheless, these techniques suffer from their
dependencies on the spatial distribution of grey
values. To tackle these issues, a novel non-entropic
threshold selection method is proposed, which is the
primary key contribution having found a new source of
information to the biomedical image processing field.
The firsthand Square Error (SE)-based objective
function is suggested. The second key contribution is
the new optimizer called Fast Cuckoo Search (FCS),
which is useful and brings novel ideas into the
subject, used to optimize the suggested objective
functions for computing the optimal thresholds. To
ensure a faster convergence with a quality optimal
solution, we include extra exploitation together with a
chance factor. The FCS is validated using the
well-known classical and CEC 2014 benchmark test
functions, which shows a significant improvement over
its predecessors --- Adaptive Cuckoo Search (ACS) and
other state-of-the-art optimizers. Further, the SE
minimization-based optimal multilevel thresholding
method using the FCS, coined as SE-FCS, is proposed. To
experiment, images are considered from the Kaggle
Radiography database. We have compared its performances
with Tsallis, Kapur's, and Masi entropy-based
techniques using well-known segmentation metrics and
achieved a performance increase of 2.95\%, 5.51\% and
10.50\%, respectively. The proposed method shows
superiority using Friedman's mean rank statistical test
and ranked first.",
acknowledgement = ack-nhfb,
articleno = "2450004",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lyu:2024:AIO,
author = "Chengang Lyu and Mengqi Zhang and And Jie Jin",
title = "An Adaptive Illumination Optimization Method for Local
Overexposed Image",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500050",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500050",
abstract = "In order to solve the local overexposure caused by
uneven surface reflectance, this paper proposes a
fast-adaptive illumination control method with a
camera-projector system. At first, an image is captured
by the camera and the local overexposed area is
segmented using saliency detection. Then the calculated
image is projected onto the object by the projector as
corrective illumination. The calculation process
includes the inversion of the gray value in the
overexposed area and the adjustment based on the
position and depth information of the object. The
high-exposure saturated regional which affects the
target recognition is thus reduced, and the original
illumination intensity is reserved for the other
regions. This process is iterated until the optimal
illumination is achieved. The resulting image for each
iteration is evaluated using Blind/no Reference Image
Space Quality Estimator (BRISQUE). When BRISQUE value
reaches the minimum, a high-quality image is achieved.
The experiments show that the proposed approach can
significantly improve the speed of obtaining normally
exposed images, and this system provides new ideas for
industry image acquisition.",
acknowledgement = ack-nhfb,
articleno = "2450005",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Elloumi:2024:FRO,
author = "Nessrine Elloumi and Habiba Loukil and And Med Salim
Bouhlel",
title = "Full-Reference Objective Quality Metric for
Three-Dimensional Deformed Models",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500062",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500062",
abstract = "Three-dimensional data are generally represented by
triangular meshes. The 3D data are used in several
fields including remote 3D games, 3D medical
application, 3D virtual worlds and 3D augmented reality
application. These applications require displaying,
printing or exchanging the 3D models through the
network to optimize the rendering of the 3D models and
3D applications, which include different treatments,
for example, smoothing, compression, re-meshing,
simplification, watermarking, etc. However, these
processes generate distortions that affect the quality
of the rendered 3D data. Thus, subjective or objective
metrics are required for assessing the visual quality
of the deformed models to evaluate the efficiency of
the applied algorithms. In this context, we introduce a
new perceptual full-reference metric that compare two
3D meshes based on their 3D content information. The
proposed metric integrates the relativity and
selectivity properties of the Human visual system (HVS)
independent of the mesh type and connectivity (e.g.
Triangular, Quadrilateral, Tetrahedron, Hexahedron),
which represent a limit in the existing method, in
order to capture the perceptual quantity of the
distortion by the observer. The results of the proposed
approach outperform the existing metrics and have a
high correlation with the subjective measures. We use
the two correlation coefficients Spearman Rank (Rs) and
Pearson Rank (Rp) in order to assess the performance of
the proposed metric.",
acknowledgement = ack-nhfb,
articleno = "2450006",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pedalanka:2024:EDN,
author = "P. S. Subhashini Pedalanka and Manchikalapudi Satya
Sai Ram and And Duggirala Sreenivasa Rao",
title = "An Enhanced Deep Neural Network-Based Approach for
Speaker Recognition Using Triumvirate Euphemism
Strategy",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500074",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500074",
abstract = "Automatic Speech Recognition (ASR) has been an
intensive research area during the recent years in
internet to enable natural human--machine
communication. However, the existing Deep Neutral
Network (DNN) techniques need more focus on feature
extraction process and recognition accuracy. Thus, an
enhanced deep neural network (DNN)-based approach for
speaker recognition with a novel Triumvirate Euphemism
Strategy (TES) is proposed. This overcomes poor feature
extraction from Mel-Frequency Cepstral Coefficient
(MFCC) map by extracting the features based on petite,
hefty and artistry of the features. Then, the features
are trained with Silhouette Martyrs Method (SMM)
without any inter-class and intra-class separability
problems and margins are affixed between classes with
three new loss functions, namely A-Loss, AM-Loss and
AAM-Loss. Additionally, the parallelization is done by
a mini-batch-based BP algorithm in DNN. A novel
Frenzied Heap Atrophy (FHA) with a multi-GPU model is
introduced in addition with DNN to enhance the
parallelized computing that accelerates the training
procedures. Thus, the outcome of the proposed technique
is highly efficient that provides feasible extraction
features and gives incredibly precise results with
97.5\% accuracy in the recognition of speakers.
Moreover, various parameters were discussed to prove
the efficiency of the system and also the proposed
method outperformed the existing methods in all
aspects.",
acknowledgement = ack-nhfb,
articleno = "2450007",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2024:OAC,
author = "V. Rajesh Kumar and P. Aruna Jeyanthy and And R.
Kesavamoorthy",
title = "Optimization-Assisted {CNN} Model for Fault
Classification and Site Location in Transmission
Lines",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500086",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500086",
abstract = "The theme of the paper is to emphasize the detection
and classification of faults and their site location in
the transmission line using machine learning techniques
which help to indemnify the foul-up of the humans in
identifying the site and type of occurrence of fault.
Moreover, the transient stability is a supreme one in
power systems and so the disturbances like faults are
required to be separated to preserve the transient
stability. In general, the protection of the
transmission line includes the installation of relays
at both ends of the line that constantly monitor
voltages and currents and operate unless a fault occurs
on a line. Therefore, this paper intends to introduce a
novel transmission line protection model by exploiting
the hybrid optimization concept to train the
Convolutional Neural Network (CNN). Here, the fault
detection, classification and site location are
diagnosed by using CNN which is trained and tested by
making use of diverse synthetic field data derived from
the simulation models of distinct types of transmission
lines. Hence, the location and the type of faults will
be predicted by the CNN depending on the fault signal
characteristics which are optimally trained by a new
hybrid algorithm named Chicken Swarm Insisted Spotted
Hyena (CSI-SH) Algorithm that hybrids both the concept
of Spotted Hyena Optimization (SHO) and Chicken Swarm
Optimization (CSO). Finally, the proposed method based
on CNN for fault classification and site location of
transmission lines is implemented in MATLAB/Simulink
and the performances are compared with various measures
like classification accuracy, fault detection rate and
so on.",
acknowledgement = ack-nhfb,
articleno = "2450008",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bapatla:2024:DHO,
author = "Sesikala Bapatla and J. Harikiran",
title = "Deer Hunting Optimization with {$3$D}-Convolutional
Neural Network for Diabetic Retinopathy Classification
Model",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500098",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500098",
abstract = "A retina disease caused by high glucose levels in the
blood is called Diabetic Retinopathy (DR) and is the
world's leading cause of blindness. To avoid or delay
vision degradation and loss, early diagnosis and
treatment are required. As a result, the creation of an
automated method for accurate DR identification is
essential. For this, in this paper, a 3D-Convolution
Neural Network (3D-CNN) with Deer Hunting Optimization
(DHO) algorithm is proposed for detecting and
classifying DR images. The proposed 3D-CNN-DHO approach
includes four phases such as pre-processing,
segmentation, feature extraction, and classification.
The contrast of the DR image is first improved using a
Contrast-Limited Adaptive Histogram Equalization
(CLAHE) approach. Subsequently, the threshold-based
effective segmentation is carried out. Then, the
Resnet50 model is implemented to extract the features
from the image. Finally, 3D-CNN-DHO-based classifier
model is implemented to categorize the various DR
stages. The experiments are carried out in detail and
evaluated on the Messidor DR benchmark dataset. The
acquired experimental result demonstrated the
3D-CNN-DHO model's outstanding qualities by achieving
optimal specificity, sensitivity, recall, precision,
and accuracy.",
acknowledgement = ack-nhfb,
articleno = "2450009",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Gadde:2024:CMD,
author = "Swetha Gadde and J. Amutharaj and And S. Usha",
title = "Cloud Multimedia Data Security by
Optimization-Assisted Cryptographic Technique",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500104",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500104",
abstract = "Currently, the size of multimedia data is rising
gradually from gigabytes to petabytes, due to the
progression of a larger quantity of realistic data. The
majority of big data is conveyed via the internet and
they were accumulated on cloud servers. Since cloud
computing offers internet-oriented services, there were
a lot of attackers and malevolent users. They always
attempt to deploy the private data of users without any
right access. At certain times, they substitute the
real data by any counterfeit data. As a result, data
protection has turned out to be a noteworthy concern in
recent times. This paper aims to establish an
optimization-based privacy preservation model for
preserving multimedia data by selecting the optimal
secret key. Here, the encryption and decryption process
is carried out by Improved Blowfish cryptographic
technique, where the sensitive data in cloud server is
preserved using the optimal key. Optimal key generation
is the significant procedure to ensure the objectives
of integrity and confidentiality. Likewise, data
restoration is the inverse process of sanitization
(decryption). In both the cases, key generation remains
a major aspect, which is optimally chosen by a novel
hybrid algorithm termed as ``Clan based Crow Search
with Adaptive Awareness probability (CCS-AAP). Finally,
an analysis is carried out to validate the improvement
of the proposed method.",
acknowledgement = ack-nhfb,
articleno = "2450010",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Yang:2024:DDS,
author = "Hailong Yang and Yinghao Liu and And Tian Xia",
title = "Defect Detection Scheme of Pins for Aviation
Connectors Based on Image Segmentation and Improved
{RESNET-50}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500116",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500116",
abstract = "In this paper, a new detection method of pin defects
based on image segmentation and ResNe-50 is proposed,
which realizes the defect detection of faulty pins in
many aviation connectors. In this paper, a new dataset
image segmentation method is used to segment many
aviation connectors in a single image to generate a
dataset, which reduces the tedious work of manually
labeling the dataset. In the defect detection model,
based on ResNet-50, a ResNet-B residual structure is
introduced to reduce the loss of features during
information extraction; a continuously differentiable
CELU is used as the activation function to reduce the
neuron death problem of ReLU; a new deformable
convolution network (DCN v2) is introduced as the
convolution kernel structure of the model to improve
the recognition of aviation connectors with prominent
geometric deformation pin recognition. The improved
model achieved 97.2\% and 94.4\% accuracy for skewed
and missing pins, respectively, in the experiments. The
detection accuracy improved by 1.91\% to 96.62\%
compared to the conventional ResNet-50. Compared with
the traditional model, the improved model has better
generalization ability.",
acknowledgement = ack-nhfb,
articleno = "2450011",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2024:MLA,
author = "K. Antony Kumar and M. J. Carmel Mary Belinda",
title = "A Multi-Layer Acoustic Neural Network-Based
Intelligent Early Diagnosis System for Rheumatic Heart
Disease",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500128",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500128",
abstract = "Rheumatic Heart Disease (RHD) is a disorder of heart
caused by streptococcal throat infection followed by
the organ damage, irreversible valve damage and heart
failure. Acute Rheumatic Fever (ARF) is a precursor to
the disease. Sometimes, RHD can occur without any signs
or symptoms, and if there are any symptoms, they occur
with the infection in the heart valves and fever. Due
to these issues, respiratory problems occur with chest
pain and tremors. Additionally, the symptoms include
faint, heart murmurs, stroke and unexpected collapse.
The techniques available try to detect the RHD as early
as possible. Although the recent medical health care
department uses crucial techniques, they are not
accurate in terms of symptom classification, precision
and prediction. On the scope, we are developing
Multi-Layered Acoustic Neural (MLAN) Networks to detect
the RHD symptoms using heart beat sound and
Electrocardiogram (ECG) measurements. In this proposed
MLAN system, the novel techniques such as
multi-attribute acoustic data sampling model, heart
sound sampling procedures, ECG data sampling model, RHD
Recurrent Convolutional Network (RRCN) and Acoustic
Support Vector Machine (ASVM) are used for increasing
the accuracy. In the implementation section, the
proposed model has been compared to the Long Short-Term
Memory-based Cardio (LSTC) data analysis model,
Cardio-Net and Video-Based Deep Learning (VBDL)
techniques. In this comparison, the proposed system has
10\%--17\% higher accuracy in RHD detection than
existing techniques.",
acknowledgement = ack-nhfb,
articleno = "2450012",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Duan:2024:ABR,
author = "Xueying Duan",
title = "Abnormal Behavior Recognition for Human Motion Based
on Improved Deep Reinforcement Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "01",
pages = "??--??",
month = jan,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467825500299",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu May 23 07:14:57 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500299",
abstract = "Recognizing abnormal behavior recognition (ABR) is an
important part of social security work. To ensure
social harmony and stability, it is of great
significance to study the identification methods of
abnormal human motion behavior. Aiming at the low
accuracy of human motion ABR method, ABR method for
human motion based on improved deep reinforcement
learning (DRL) is proposed. First, the background image
is processed in combination with the Gaussian model;
second, the background features and human motion
trajectory features are extracted, respectively;
finally, the improved DRL model is constructed, and the
feature information is input into the improvement model
to further extract the abnormal behavior features, and
the ABR of human motion is realized through the
interaction between the agent and the environment. The
different methods were examined based on UCF101 data
set and HiEve data set. The results show that the
accuracy of human motion key point acquisition and
posture estimation accuracy is high, the proposed
method sensitivity is good, and the recognition
accuracy of human motion abnormal behavior is as high
as 95.5\%. It can realize the ABR for human motion and
lay a foundation for the further development of
follow-up social security management.",
acknowledgement = ack-nhfb,
articleno = "2550029",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Rao:2024:HSE,
author = "K. Venkateswara Rao and B. Venkata Ramana Reddy",
title = "{HM-SMF}: an Efficient Strategy Optimization using a
Hybrid Machine Learning Model for Stock Market
Prediction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1142/S021946782450013X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782450013X",
abstract = "Stock market forecasting is a significant task, and
investing in the stock marketplace is a significant
part of monetary research due to its high risk.
Therefore, accurate forecasting of stock market
analysis is still a challenge. Due to stable and
volatile data, stock market forecasting remains a major
challenge for investors. Recent machine learning (ML)
models have been able to reduce the risk of stock
market forecasting. However, diversity remains a key
challenge in developing better erudition models and
extracts more intellectually priceless qualities to
auxiliary advanced predictability. In this paper, we
propose an efficient strategy optimization using a
hybrid ML model for stock market prediction (HM-SMP).
The first contribution of the proposed HM-SMP model is
to introduce chaos-enhanced firefly bowerbird
optimization (CEFBO) algorithm for optimal feature
selection among multiple features which reduce the data
dimensionality. Second, we develop a hybrid
multi-objective capuchin with a recurrent neural
network (HC-RNN) for the prediction of the stock market
which enhances the prediction accuracy. We use
supervised RNN to predict the closing price. Finally,
to estimate the presence of the proposed HM-SMP model
through the benchmark, stock market datasets and the
performance can be compared with the existing
state-of-the-art models in terms of accuracy,
precision, recall, and $F$-measure.",
acknowledgement = ack-nhfb,
articleno = "2450013",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Moradi:2024:IIE,
author = "Saed Moradi and Jahed Moradi and Saeid Aghaziyarati
and And Hadi Shahraki",
title = "Infrared Image Enhancement Based on Optimally Weighted
Multi-Scale {Laplacian} of {Gaussian} and Local
Statistics Using Particle Swarm Optimization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500141",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500141",
abstract = "Infrared imagery is extensively used in defense,
remote sensing and medical applications. While the
infrared images have many advantages over RGB images,
the details in these images are usually blurred which
in turn leads to some difficulties for human operators.
In this paper, a new method based on Laplacian of
Gaussian scale-space and local variance is presented to
improve the visual quality of the infrared images. At
the first step, the Gaussian scale-space is constructed
by convolving the original image with different
Gaussian kernels. Then, the two-dimensional Laplacian
kernels are convolved with the Gaussian scale-space to
achieve details with both positive as well as negative
contrasts. The weighted details are added to the
original image to deblur the dim areas. At the final
step, to increase the dynamic range of the image and
have better visual quality, the local variance of the
image is also added to the output of the previous step.
Since finding optimum weighting coefficients is a
difficult task empirically, here, we use a
population-based meta-heuristic optimization algorithm
called particle swarm optimization (PSO) to find the
optimum values for weighting coefficient values. Beside
qualitative comparison, Structural Similarity (SSIM)
and second-derivative-like measure of enhancement
(SDME) are used to quantitatively investigate the
images quality. The proposed method outperforms the
baseline algorithms in both qualitative and
quantitative perspectives.",
acknowledgement = ack-nhfb,
articleno = "2450014",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Thakkar:2024:EBP,
author = "Priyanka Bibay Thakkar and R. H. Talwekar",
title = "An Efficient Blood Pressure Estimation and Risk
Analysis System of {PPG} Signals Using {IDA} and
{MPPIW-DLNN} Algorithms",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500153",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500153",
abstract = "The non-invasive Blood Pressure Estimation (BPE)
utilizing the technology of photoplethysmography (PPG)
gains significant interest because PPG could be
extensively employed to wearable sensors. Here, a
method for estimating Systolic Blood pressure (SBP), as
well as Diastolic Blood pressure (DBP), grounded only
on a PPG signal utilizing the Image Denoising
Algorithms (IDA) algorithms is proposed. Also, a
classification methodology to execute the risk analysis
(RA) of the BP patients utilizing Moore--Penrose
Pseudo-Inverse Matrix-Deep Learning Neural Network
(MPPIW-DLNN) is proposed. The preprocessing is then
done on the input PPG signal utilizing the
Modified--Chebyshev Filter (CF) to eradicate the
unwanted information existent in the signal. Afterward,
the BPE is done utilizing IDA, which categorizes those
components into (i) SBP and (ii) DBP. The MPPIW-DLNN
provides the results of four sorts of risk classes like
(i) stroke, (ii) heart failure (HF), (iii) heart attack
(HA), and (iv) aneurysm identified from the inputted
PPG signal.",
acknowledgement = ack-nhfb,
articleno = "2450015",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumawat:2024:TVS,
author = "Manisha Kumawat and Arti Khaparde",
title = "Time-Variant Satellite Vegetation Classification
Enabled by Hybrid Metaheuristic-Based Adaptive
Time-Weighted Dynamic Time Warping",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500165",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500165",
abstract = "Land cover data is very significant for designing the
earth system, managing the natural resources, and also
for performing conservation planning. Time-series data
are captured with their dynamic vegetation behavior
using remote sensing technology, which is broadly
utilized in land cover mapping. Most of the Vegetation
Index (VI) such as the Enhanced Vegetation Index (EVI)
and Normalized Difference Vegetation Index (NDVI)
comprises commonly employed features that are obtained
from the time-series spectral data. But, these VIs are
not validated as the optimal techniques for generating
the temporal profiles. Recent researches highly depend
on optical satellite imagery for performing these
above-mentioned tasks. Dynamic Time Warping (DTW) is
said to be an effective optimal solution for solving
the existing challenges, especially the improved
version of DTW named Time-Weighted Dynamic Time Warping
(TWDTW) is used for time-series analysis regarding the
time-series vegetation classification. Yet, the TWDTW
efficiency is not shown with other comparative machine
learning approaches owing to the classification of
vegetation type in the mountain areas. The major goal
of this paper is to research and create a novel
approach for distinguishing the kind of vegetation in a
farm region near Ujani Dam in Solapur District,
Maharashtra using time-series analysis. For time-series
analysis employing satellite images, the suggested
model offers a unique Adaptive Time-Weighted Dynamic
Time Warping (ATWDTW). The farm's satellite images are
first pre-processed before being sent to ATWDTW for
examination. The TWDTW idea is optimized for
classification performance using a new hybrid
metaheuristic technique named Adaptive Coyote Crow
Search Optimization (ACCSO). From the experimental
results, the performance of the suggested ACCSO-ATWDTW
correspondingly provides superior performance to the
traditional approaches, where the designed model using
ACCSO-ATWDTW provides 7.2\%, 5.2\%, 9.9\%, 4.55\%, and
2.33\% higher MCC than the MFO-ATWDTW, BSA-ATWDTW,
MF-BSA-ATWDTW, CSA-ATWDTW, and COA-ATWDTW at the
maximum iteration of 200. This proved the robustness
and less sensitivity to training samples of the TWDTW
method when applied to mountain vegetation-type
classifications.",
acknowledgement = ack-nhfb,
articleno = "2450016",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sowmya:2024:CNB,
author = "M. N. Sowmya and Keshava Prasanna",
title = "Convoluted Neighborhood-Based Ordered-Dither Block
Truncation Coding for Ear Image Retrieval",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500177",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500177",
abstract = "Image retrieval is a significant and hot research
topic among researchers that drives the focus of
researchers from keyword toward semantic-based image
reconstruction. Nevertheless, existing image retrieval
investigations still have a shortage of significant
semantic image definition and user behavior
consideration. Hence, there is a necessity to offer a
high level of assistance towards regulating the
semantic gap between low-level visual patterns and
high-level ideas for a better understanding between
humans and machines. Hence, this research devises an
effective medical image retrieval strategy using
convoluted neighborhood-based Ordered-dither block
truncation coding (ODBTC). The developed approach is
devised by modifying the ODBTC concept using a
convoluted neighborhood mechanism. Here, the convoluted
neighborhood-based color co-occurrence feature (CCF)
and convoluted neighborhood-based bit pattern feature
(BBF) are extracted. Finally, cross-indexing is
performed to convert the feature points into binary
codes for effective image retrieval. Meanwhile, the
proposed convoluted neighborhood-based ODBTC has
achieved maximum precision, recall, and f-measure with
values of 0.740, 0.680, and 0.709.",
acknowledgement = ack-nhfb,
articleno = "2450017",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chaurasiya:2024:RRV,
author = "Rashmi Chaurasiya and Dinesh Ganotra",
title = "Reflection Removal with Varied Field of View Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500189",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500189",
abstract = "Due to the presence of an additional glass pane
between the camera and the scene, an additional
reflection scene is captured in the image apart from
the desired object sometimes. Images are more often
captured from mobile handsets these days which have
multiple cameras. This paper gives the advantage of
multiple cameras. There exists a disparity and varied
field of view when images are captured with multiple
cameras. We use these two factors to act as a cue to
remove reflection, as reflection intensity across the
image pairs change with different field-of-view. The
proposed method is robust and convenient to implement
as it does not require an additional hardware, for
example, light field camera for stereo images. Also, it
does not make assumptions about the appearance or
intensity of reflection.",
acknowledgement = ack-nhfb,
articleno = "2450018",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shaikh:2024:RTM,
author = "Shakil A. Shaikh and Jayant J. Chopade and And Mohini
Pramod Sardey",
title = "Real-Time Multi-Object Detection Using Enhanced
{Yolov5-7S} on Multi-{GPU} for High-Resolution Video",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500190",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500190",
abstract = "Multiple objects tracking in a video sequence can be
performed by detecting and distinguishing the objects
that appear in the sequence. In the context of computer
vision, the robust multi-object tracking problem is a
difficult problem to solve. Visual tracking of multiple
objects is a vital part of an autonomous driving
vehicle's vision technology. Wide-area video
surveillance is increasingly using advanced imaging
devices with increased megapixel resolution and
increased frame rates. As a result, there is a huge
increase in demand for high-performance computation
system of video surveillance systems for real-time
processing of high-resolution videos. As a result, in
this paper, we used a single stage framework to solve
the MOT problem. We proposed a novel architecture in
this paper that allows for the efficient use of one and
multiple GPUs are used to process Full High Definition
video in real time. For high-resolution video and
images, the suggested approach is real-time
multi-object detection based on Enhanced Yolov5-7S on
Multi-GPU Vertex. We added one more layer at the top in
backbone to increase the resolution of feature
extracted image to detect small object and increase the
accuracy of model. In terms of speed and accuracy, our
proposed approach outperforms the state-of-the-art
techniques.",
acknowledgement = ack-nhfb,
articleno = "2450019",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Du:2024:SSS,
author = "Fuhe Du and Bo Peng and Zaid Al-huda and And Jing
Yao",
title = "Semi-Supervised Skin Lesion Segmentation via Iterative
Mask Optimization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500207",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500207",
abstract = "Deep learning-based skin lesion segmentation methods
have achieved promising results in the community.
However, they are usually based on fully supervised
learning and require many high-quality ground truths.
Labeling the ground truths takes a lot of labor,
material, and financial resources. We propose a novel
semi-supervised skin lesion segmentation method to
solve this problem. First, a hierarchical image
segmentation algorithm is used to generate optimal
segmentation maps. Then, fully supervised training is
performed on a small part of the images with ground
truths. The resulting pseudo masks are generated to
train the rest of the images. The optimal segmentation
maps are utilized in this process to refine the pseudo
masks. Experiments show that the proposed method can
improve the performance of semi-supervised learning for
skin lesion segmentation by reducing the gap with fully
supervised learning methods. Moreover, it can reduce
the workload of labeling the ground truths. Extensive
experiments are conducted on the open dataset to
validate the efficiency of the proposed method. The
results show that our method is competitive in
improving the quality of semi-supervised
segmentation.",
acknowledgement = ack-nhfb,
articleno = "2450020",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mulla:2024:WGE,
author = "Samina Mulla and Nuzhat F. Shaikh",
title = "Weighted Graph Embedding Feature with Bi-Directional
Long Short-Term Memory Classifier for Multi-Document
Text Summarization",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500220",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500220",
abstract = "In this digital era, there is a tremendous increase in
the volume of data, which adds difficulties to the
person who utilizes particular applications, such as
websites, email, and news. Text summarization targets
to reduce the complexity of obtaining statistics from
the websites as it compresses the textual document to a
short summary without affecting the relevant
information. The crucial step in multi-document
summarization is obtaining a relationship between the
cross-sentence. However, the conventional methods fail
to determine the inter-sentence relationship,
especially in long documents. This research develops a
graph-based neural network to attain an inter-sentence
relationship. The significant step in the proposed
multi-document text summarization model is forming the
weighted graph embedding features. Furthermore, the
weighted graph embedding features are utilized to
evaluate the relationship between the document's words
and sentences. Finally, the bidirectional long
short-term memory (BiLSTM) classifier is utilized to
summarize the multi-document text summarization. The
experimental analysis uses the three standard datasets,
the Daily Mail dataset, Document Understanding
Conference (DUC) 2002, and Document Understanding
Conference (DUC) 2004 dataset. The experimental outcome
demonstrates that the proposed weighted graph embedding
feature + BiLSTM model exceeds all the conventional
methods with Precision, Recall, and F1 score of 0.5352,
0.6296, and 0.5429, respectively.",
acknowledgement = ack-nhfb,
articleno = "2450022",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Enturi:2024:ODC,
author = "B. Krishna Manash Enturi and A. Suhasini and And
Narayana Satyala",
title = "Optimized Deep {CNN} with Deviation Relevance-based
{LBP} for Skin Cancer Detection: Hybrid Metaheuristic
Enabled Feature Selection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500232",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500232",
abstract = "Segmentation of skin lesions is a significant and
demanding task in dermoscopy images. This paper
proposes a new skin cancer recognition scheme, with:
``Pre-processing, Segmentation, Feature extraction,
Optimal Feature Selection and Classification''. Here,
pre-processing is done with certain processes. The
pre-processed images are segmented via the ``Otsu
Thresholding model''. The third phase is feature
extraction, where Deviation Relevance-based ``Local
Binary Pattern (DRLBP), Gray-Level Co-Occurrence Matrix
(GLCM) features and Gray Level Run-Length Matrix (GLRM)
features'' are extracted. From these extracted
features, the optimal features are chosen via Particle
Updated WOA (PU-WOA) model. Subsequently,
classification occurs via Optimized DCNN and NN to
classify the skin lesion. To make the classification
more precise, the DCNN is optimized by the introduced
algorithm. The result has shown a higher accuracy of
0.998737, when compared with other extant models like
IPSO, IWOA, PSO+CNN, WOA+CNN and CNN schemes.",
acknowledgement = ack-nhfb,
articleno = "2450023",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Pakhare:2024:HML,
author = "Jayamala D. Pakhare and Mahadev D. Uplane",
title = "Hybrid Mayfly {L{\'e}vy} Flight Distribution
Optimization Algorithm-Tuned Deep Convolutional Neural
Network for Indoor--Outdoor Image Classification",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500244",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500244",
abstract = "Image classification in the image is the persistent
task to be computed in robotics, automobiles, and
machine vision for sustainability. Scene categorization
remains one of the challenging parts of various
multi-media technologies implied in human--computer
communication, robotic navigation, video surveillance,
medical diagnosing, tourist guidance, and drone
targeting. In this research, a Hybrid Mayfly L{\'e}vy
flight distribution (MLFD) optimization algorithm-tuned
deep convolutional neural network is proposed to
effectively classify the image. The feature extraction
process is a significant task to be executed as it
enhances the classifier performance by reducing the
execution time and the computational complexity.
Further, the classifier is optimally trained by the
Hybrid MLFD algorithm which in turn reduces
optimization issues. The accuracy of the proposed
MLFD-based Deep-CNN using the SCID-2 dataset is
95.2683\% at 80\% of training and 97.6425\% for 10
K-fold. This manifests that the proposed MLFD-based
Deep-CNN outperforms all the conventional methods in
terms of accuracy, sensitivity, and specificity.",
acknowledgement = ack-nhfb,
articleno = "2450024",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Xin:2024:SAC,
author = "Guangnan Xin and Min Zhu and Yuze Zhou and Guanyu
Jiang and Zeyu Cai and Aoyu Pang and And Qi Zhu",
title = "A Self-Attention {CycleGAN} for Cross-Domain
Semi-Supervised Contactless Palmprint Recognition",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500256",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500256",
abstract = "Nowadays, there is a growing concern about contactless
palmprint recognition because of its high-recognition
rate, efficiency, and convenience. With the development
of image acquisition equipment, it is an often case
that the palmprint images for identification and for
registration are captured by different devices. At the
same time, a large amount of well-labeled palmprint
images are difficult to collect. Therefore, the
performance of most existing contactless palmprint
recognition methods will be poor in real-life
applications. To address these issues, we proposed a
self-attention CycleGAN for cross-domain
semi-supervised palmprint recognition. Based on
CycleGAN, the styles of contactless palmprint images in
source domain and target domain can be swapped.
Specifically, the spatial features are captured through
self-attention modules by modeling long-range
dependencies. In addition, an extra source domain
classifier is trained with the labeled source domain
images to give the unlabeled images in target domain a
pseudo-label, by which images in target domain are
efficiently utilized. The experiment results showed
that our method achieved competitive performance.",
acknowledgement = ack-nhfb,
articleno = "2450025",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2024:FSP,
author = "Yun Liu",
title = "Fault Signal Perception of Nanofiber Sensor for {$3$D}
Human Motion Detection Using Multi-Task Deep Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "02",
pages = "??--??",
month = mar,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467825500603",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:54 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500603",
abstract = "Once a fault occurs in the nanofiber sensor, the
scientific and reliable three-dimensional (3D) human
motion detection results will be compromised. It is
necessary to accurately and rapidly perceive the fault
signals of the nanofiber sensor and determine the type
of fault, to enable it to continue operating in a
sustained and stable manner. Therefore, we propose a
fault signal perception method for 3D human motion
detection nanofiber sensor based on multi-task deep
learning. First, through obtaining the fault
characteristic parameters of the nanofiber sensor, the
fault of the nanofiber sensor is reconstructed to
complete the fault location of the nanofiber sensor.
Second, the fault signal of the nanofiber sensor is
mapped by the penalty function, and the feature
extraction model of the fault signal of the nanofiber
sensor is constructed by combining the multi-task deep
learning. Finally, the multi-task deep learning
algorithm is used to calculate the sampling frequency
of the fault signal, and the key variable information
of the fault of the nanofiber sensor is extracted
according to the amplitude of the state change of the
nanofiber sensor, to realize the perception of the
fault signal of the nanofiber sensor. The results show
that the proposed method can accurately perceive the
fault signal of a nanofiber sensor in 3D human motion
detection, the maximum sensor fault location accuracy
is 97\%, and the maximum noise content of the fault
signal is only 5 dB, which shows that the method can be
widely used in fault signal perception.",
acknowledgement = ack-nhfb,
articleno = "2550060",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Chukka:2024:BSM,
author = "Demudu Naidu Chukka and James Stephen Meka and S.
Pallam Setty and And Praveen Babu Choppala",
title = "{Bayesian} Selective Median Filtering for Reduction of
Impulse Noise in Digital Color Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467824500268",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500268",
abstract = "The focus of this paper is impulse noise reduction in
digital color images. The most popular noise reduction
schemes are the vector median filter and its many
variants that operate by minimizing the aggregate
distance from one pixel to every other pixel in a
chosen window. This minimizing operation determines the
most confirmative pixel based on its similarity to the
chosen window and replaces the central pixel of the
window with the determined one. The peer group filters,
unlike the vector median filters, determine a set of
pixels that are most confirmative to the window and
then perform filtering over the determined set. Using a
set of pixels in the filtering process rather than one
pixel is more helpful as it takes into account the full
information of all the pixels that seemingly contribute
to the signal. Hence, the peer group filters are found
to be more robust to noise. However, the peer group for
each pixel is computed deterministically using
thresholding schemes. A wrong choice of the threshold
will easily impair the filtering performance. In this
paper, we propose a peer group filtering approach using
principles of Bayesian probability theory and
clustering. Here, we present a method to compute the
probability that a pixel value is clean (not corrupted
by impulse noise) and then apply clustering on the
probability measure to determine the peer group. The
key benefit of this proposal is that the need for
thresholding in peer group filtering is completely
avoided. Simulation results show that the proposed
method performs better than the conventional vector
median and peer group filtering methods in terms of
noise reduction and structural similarity, thus
validating the proposed approach.",
acknowledgement = ack-nhfb,
articleno = "2450026",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{He:2024:LBH,
author = "Shuhan He and Xueming Li and And Qiang Fu",
title = "{Laplace}-Based {$3$D} Human Mesh Sequence
Compression",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S021946782450027X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782450027X",
abstract = "Three-dimensional (3D) human mesh sequences obtained
by 3D scanning equipment are often used in film and
television, games, the internet, and other industries.
However, due to the dense point cloud data obtained by
3D scanning equipment, the data of a single frame of a
3D human model is always large. Considering the
different topologies of models between different
frames, and even the interaction between the human body
and other objects, the content of 3D models between
different frames is also complex. Therefore, the
traditional 3D model compression method always cannot
handle the compression of the 3D human mesh sequence.
To address this problem, we propose a sequence
compression method of 3D human mesh sequence based on
the Laplace operator, and test it on the complex
interactive behavior of a soccer player bouncing the
ball. This method first detects the mesh separation
degree of the interactive object and human body, and
then divides the sequence into a series of fragments
based on the consistency of separation degrees. In each
fragment, we employ a deformation algorithm to map
keyframe topology to other frames, to improve the
compression ratio of the sequence. Our work can be used
for the storage of mesh sequences and mobile
applications by providing an approach for data
compression.",
acknowledgement = ack-nhfb,
articleno = "2450027",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bai:2024:ODN,
author = "G. Mercy Bai and P. Venkadesh",
title = "Optimized Deep Neuro-Fuzzy Network with {MapReduce}
Architecture for Acute Lymphoblastic Leukemia
Classification and Severity Analysis",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500281",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500281",
abstract = "The most common life-threatening disease, acute
lymphoblastic leukemia (ALL), can be lethal within a
few weeks if untreated. The early detection and
analysis of leukemia is a key dilemma in the field of
disease diagnosis, and the methods available for the
classification process are time-consuming. To overcome
the issues, this paper develops a robust classification
technique named Horse Herd Whale Optimization-enabled
Deep Neuro-Fuzzy Network (HHWO-enabled DNFN method) for
ALL classification and severity analysis using the
MapReduce framework. The input image is first
preprocessed and segmented, and the useful features
necessary for improving the classification performance
are extracted during the mapper phase, known as HHWO,
which incorporates Horse Herd Optimization Algorithm
(HOA) and Whale Optimization Algorithm (WOA). Finally,
severity analysis of ALL is done to classify the levels
of leukemia to offer optimal treatment. As a result,
the developed method performed better than other
existing methods, achieving superior performance with a
greater testing accuracy of 0.959, sensitivity of
0.965, and specificity of 0.966, respectively.",
acknowledgement = ack-nhfb,
articleno = "2450028",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Joseph:2024:MFT,
author = "Jovi Joseph and S. R. Sreela",
title = "{MODCN}: Fine-Tuned Deep Convolutional Neural Network
with {GAN} Deployed to Forecast Diabetic Eye Damage in
Fundus Retinal Images",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500293",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500293",
abstract = "Diabetic Retinopathy (DR) and Glaucoma are two of the
most common causes of vision loss world-wide. However,
it can be averted if therapy is begun early enough. In
biomedical applications, the use of digital image
processing has assisted in the automated identification
of some ailments at an earlier stage. To make this
prediction generally neural network classifier models
were previously used, but these models have the
drawback of being unable to detect multiple illnesses
that occur in the eye at the same time and require a
big database for successful classifier training. As a
result, a model is needed to reliably distinguish DR
and Glaucoma in diabetic individuals more accurately
and with minimum dataset images. In this view, this
study introduced Mayfly Optimized Deep Convolutional
Network (MODCN) model for automated disease detection
in the fundus retina images. In the MODCN model, the
images are initially preprocessed, segmented at
generator in the GAN model then a discriminator readily
gives synthesis of real images of the fundus retina,
thus a wide database has been created and considered as
training images for the MODCN classifier. MODCN
classifier has a modified high-density layer as a
transition layer to avoid overfitting and the errors
are minimized by tuning the hyperparameters using
Mayfly Optimization Algorithm. After feature mapping,
the classes normal, DR and Glaucoma are labeled and
stored. At the testing stage, images are preprocessed,
feature mapped and classified in the MODCN model. Thus,
the proposed MODCN model detects multiple illness such
as Diabetic Retinopathy and Glaucoma at the same time
even with a small amount of database that performs a
successful classifier training. This model is then
evaluated and gives an accuracy of 99\% that was higher
compared to previous models.",
acknowledgement = ack-nhfb,
articleno = "2450029",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shanmugasundaram:2024:DAI,
author = "Suresh Shanmugasundaram and Natarajan Palaniappan",
title = "Detection Accuracy Improvement on One-Stage Object
Detection Using {AP}-Loss-Based Ranking Module and
{ResNet-152} Backbone",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S021946782450030X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782450030X",
abstract = "Localization-loss and classification-loss are
optimized at the same time to train the one-stage
object detectors. Because of the large number of
anchors, the severe foreground--background class
disproportion causes significant classification-loss.
This paper discusses using a ranking module instead of
the classification module to mitigate this difficulty
and also Average-Precision loss (AP-loss) is utilized
on the ranking module. An optimization algorithm is
used to make the AP-loss as effective as possible.
Optimization algorithm blends the error-driven updating
method of perceptron learning and the deep network
backpropagation technique. This optimization algorithm
handles the foreground--background class disproportion
issues. One-stage detector with AP-loss and backbone
with ResNet-152 attains improvement in the detection
performance compared to the classification-losses-based
detectors.",
acknowledgement = ack-nhfb,
articleno = "2450030",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Begum:2024:MDA,
author = "Afiya Parveen Begum and Prabha Selvaraj",
title = "Multiclass Diagnosis of {Alzheimer}'s Disease Analysis
Using Machine Learning and Deep Learning Techniques",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500311",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500311",
abstract = "Alzheimer's disease (AD) is a popular neurological
disorder affecting a critical part of the world's
population. Its early diagnosis is extremely imperative
for enhancing the quality of patients' lives. Recently,
improved technologies like image processing, artificial
intelligence involving machine learning, deep learning,
and transfer learning have been introduced for
detecting AD. This review describes the contribution of
image processing, feature extraction, optimization, and
classification approach in AD recognition. It deeply
investigates different methods adopted for multiclass
diagnosis of AD. The paper further presents a brief
comparison of existing AD studies in terms of
techniques adopted, performance measures,
classification accuracy, publication year, and
datasets. It then summarizes the important technical
barriers in reviewed works. This paper allows the
readers to gain profound knowledge regarding AD
diagnosis for promoting extensive research in this
field.",
acknowledgement = ack-nhfb,
articleno = "2450031",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2024:CAP,
author = "S. Sathish Kumar and An. Sigappi and G. Arun Sampaul
Thomas and Y. Harold Robinson and And S. P. Raja",
title = "Classification and Analysis of Pistachio Species
Through Neural Embedding-Based Feature Extraction and
Small-Scale Machine Learning Techniques",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500323",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500323",
abstract = "Pistachios are a tremendous source of fiber, protein,
antioxidants, healthy fats, and other minerals like
thiamine and vitamin B6. They may help people lose
weight, lower cholesterol, and blood sugar levels, lead
to better gut, eye, and blood vessel health. The two
main varieties farmed and exported in Turkey are
kirmizi and siirt pistachios. Understanding how to
detect the type of pistachio is essential as it plays
an important role in trade. In this study, it is aimed
to classify these two types of pistachios and analyze
the performance of the various small-scale machine
learning algorithms. 2148 sample images for these two
kinds of pistachios were considered for this study
which includes 1232 of Kirmizi type and 916 of Siirt
type. In order to evaluate the model fairly, stratified
random sampling is applied on the dataset. For feature
extraction, we used deep neural network-based
embeddings to acquire the vector representation of
images. The classification of pistachio species is then
performed using a variety of small-scale machine
learning algorithms$^{29, 31}$ that have been trained
using these feature vectors. As a result of this study,
the success rate obtained from Logistic Regression
through features extracted from the penultimate layer
of Painters network is 97.20\%. The performance of the
models was evaluated through Class Accuracy, Precision,
Recall, F1 Score, and values of Area under the curve
(AUC). The outcomes show that the method suggested in
this study may quickly and precisely identify different
varieties of pistachios while also meeting agricultural
production needs.",
acknowledgement = ack-nhfb,
articleno = "2450032",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sau:2024:ORE,
author = "Paresh Chandra Sau and Manish Gupta and And Atul
Bansal",
title = "Optimized {ResUNet++}-Enabled Blood Vessel
Segmentation for Retinal Fundus Image Based on Hybrid
Meta-Heuristic Improvement",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500335",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500335",
abstract = "In recent years, several studies have undergone
automatic blood vessel segmentation based on
unsupervised and supervised algorithms to reduce user
interruption. Deep learning networks have been used to
get highly accurate segmentation results. However, the
incorrect segmentation of pathological information and
low micro-vascular segmentation is considered the
challenges present in the existing methods for
segmenting the retinal blood vessel. It also affects
different degrees of vessel thickness, contextual
feature fusion in technique, and perception of details.
A deep learning-aided method has been presented to
address these challenges in this paper. In the first
phase, the preprocessing is performed using the retinal
fundus images employed by the black ring removal, LAB
conversion, CLAHE-based contrast enhancement, and
grayscale image. Thus, the blood vessel segmentation is
performed by a new deep learning model termed optimized
ResUNet++. As an improvement to this deep learning
architecture, the activation function is optimized by
the J-AGSO algorithm. The objective function for the
optimized ResUNet++-based blood vessel segmentation is
to minimize the binary cross-entropy loss function.
Further, the post-processing of the images is carried
out by median filtering and binary thresholding. By
verifying the standard benchmark datasets, the proposed
model outperforms and attains enhanced performance.",
acknowledgement = ack-nhfb,
articleno = "2450033",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mulchandani:2024:EIE,
author = "Mona Mulchandani and Pramod S. Nair",
title = "{EBMICQL}: Improving Efficiency of Blockchain Miner
Pools via Incremental and Continuous {$Q$}-Learning
Framework",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500347",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib;
https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500347",
abstract = "Blockchain mining pools assist in reducing
computational load on individual miner nodes via
distributing mining tasks. This distribution must be
done in a non-redundant manner, so that each miner is
able to calculate block hashes with optimum efficiency.
To perform this task, a wide variety of mining
optimization methods are proposed by researchers, and
most of them distribute mining tasks via statistical
request processing models. These models segregate
mining requests into non-redundant sets, each of which
will be processed by individual miners. But this
division of requests follows a static procedure, and
does not consider miner specific parameters for set
creation, due to which overall efficiency of the
underlying model is limited, which reduces its mining
performance under real-time scenarios. To overcome this
issue, an Incremental & Continuous Q-Learning Framework
for generation of miner-specific task groups is
proposed in this text. The model initially uses a
Genetic Algorithm (GA) method to improve individual
miner performance, and then applies Q-Learning to
individual mining requests. The Reason for selecting GA
model is that it assists in maintaining better
speed-to-power (S2P) ratio by optimization of miner
resources that are utilized during computations. While,
the reason for selecting Q-Learning Model is that it is
able to continuously identify miners performance, and
create performance-based mining pools at a per-miner
level. Due to application of Q-Learning, the model is
able to assign capability specific mining tasks to
individual miner nodes. Because of this
capability-driven approach, the model is able to
maximize efficiency of mining, while maintaining its
QoS performance. The model was tested on different
consensus methods including Practical Byzantine Fault
Tolerance Algorithm (PBFT), Proof-of-Work (PoW),
Proof-of-Stake (PoS), and Delegated PoS (DPoS), and its
performance was evaluated in terms of mining delay,
miner efficiency, number of redundant calculations per
miner, and energy efficiency for mining nodes. It was
observed that the proposed GA based Q-Learning Model
was able to reduce mining delay by 4.9\%, improve
miners efficiency by 7.4\%, reduce number of redundant
computations by 3.5\%, and reduce energy required for
mining by 7.1\% when compared with various
state-of-the-art mining optimization techniques.
Similar performance improvement was observed when the
model was applied on different blockchain deployments,
thus indicating better scalability and deployment
capability for multiple application scenarios.",
acknowledgement = ack-nhfb,
articleno = "2450034",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Muraleedharan:2024:CUN,
author = "K. M. Muraleedharan and K. T. Bibish Kumar and Sunil
John and And R. K. Sunil Kumar",
title = "Combined Use of Nonlinear Measures for Analyzing
Pathological Voices",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500359",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500359",
abstract = "Automatic voice pathology detection enables an
objective assessment of pathologies that influence the
voice production strategy. By utilizing the
conventional pipeline model as well as the modern deep
learning-centric end-to-end methodology, numerous
pathological voice analyzing techniques have been
developed. The conventional methodology is still a
valid choice owing to the lack of enormous amounts of
training data in the study region of pathological
voice. In the meantime, obtaining higher precision,
higher accuracy, and stability is still a complicated
task. Therefore, by amalgamating the nonlinear measure,
the pathological voices are analyzed to abate such
risks. The viability of six nonlinear discriminating
measures derived from the phase space realm, involving
healthy and pathological voice signals, is studied in
this work. The analyzed parameters are Singularity
spectrum coefficients ($ \alpha_{\rm min}, \alpha_{\rm
max}, \gamma_1 $ and $ \gamma_2$). Correlation entropy
at optimum embedding dimension ($ K_{2m}$) and
correlation dimension at optimum embedding dimension ($
D_{2m}$). Analyzing the pathological voices with better
accuracy rates is the major objective of the proposed
methodology. Here, the Support Vector Machine (SVM) was
utilized as the classifier. Experimentations were
performed on VOiceICarfEDerico (VOICED) databases
subsuming 208 healthy, as well as pathological voices,
amongst these 50 samples, were utilized. Here, the
model obtained 97\% of accuracy with 99\% as of the
classifier with Gaussian kernel function. Therefore, to
differentiate normal as well as pathological subjects,
the six proposed characteristics are highly beneficial;
in addition, they will be supportive in pathology
diagnosis.",
acknowledgement = ack-nhfb,
articleno = "2450035",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Liu:2024:ICL,
author = "Fuxiang Liu and Chen Zang and Junqi Shi and Weiyu He
and Yupeng Liang and And Lei Li",
title = "An Improved {COVID-19} Lung {X}-Ray Image
Classification Algorithm Based on {ConvNeXt} Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500360",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500360",
abstract = "Aiming at the new coronavirus that appeared in 2019,
which has caused a large number of infected patients
worldwide due to its high contagiousness, in order to
detect the source of infection in time and cut off the
chain of transmission, we developed a new Chest X-ray
(CXR) image classification algorithm with high
accuracy, simple operation and fast processing for
COVID-19. The algorithm is based on ConvNeXt pure
convolutional neural network, we adjusted the network
structure and loss function, added some new Data
Augmentation methods and introduced attention
mechanism. Compared with other classical convolutional
neural network classification algorithms such as
AlexNet, ResNet-34, ResNet-50, ResNet-101,
ConvNeXt-tiny, ConvNeXt-small and ConvNeXt-base, the
improved algorithm has better performance on COVID
dataset.",
acknowledgement = ack-nhfb,
articleno = "2450036",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Fauzi:2024:FBO,
author = "Nurul Izzatie Husna Fauzi and Zalili Musa and And
Fadhl Hujainah",
title = "Feature-Based Object Detection and Tracking: a
Systematic Literature Review",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500372",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500372",
abstract = "Correct object detection plays a key role in
generating an accurate object tracking result.
Feature-based methods have the capability of handling
the critical process of extracting features of an
object. This paper aims to investigate object tracking
using feature-based methods in terms of (1) identifying
and analyzing the existing methods; (2) reporting and
scrutinizing the evaluation performance matrices and
their implementation usage in measuring the
effectiveness of object tracking and detection; (3)
revealing and investigating the challenges that affect
the accuracy performance of identified tracking
methods; (4) measuring the effectiveness of identified
methods in terms of revealing to what extent the
challenges can impact the accuracy and precision
performance based on the evaluation performance
matrices reported; and (5) presenting the potential
future directions for improvement. The review process
of this research was conducted based on standard
systematic literature review (SLR) guidelines by
Kitchenam's and Charters'. Initially, 157 prospective
studies were identified. Through a rigorous study
selection strategy, 32 relevant studies were selected
to address the listed research questions. Thirty-two
methods were identified and analyzed in terms of their
aims, introduced improvements, and results achieved,
along with presenting a new outlook on the
classification of identified methods based on the
feature-based method used in detection and tracking
process.",
acknowledgement = ack-nhfb,
articleno = "2450037",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Li:2024:ATM,
author = "Zhipeng Li and Jun Wang and Lijun Hua and Honghui Liu
and And Wenli Song",
title = "Automatic Tracking Method for {$3$D} Human Motion Pose
Using Contrastive Learning",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "03",
pages = "??--??",
month = may,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467825500378",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Wed Jun 5 09:06:55 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467825500378",
abstract = "Automatic tracking of three-dimensional (3D) human
motion pose has the potential to provide corresponding
technical support in various fields. However, existing
methods for tracking human motion pose suffer from
significant errors, long tracking times and suboptimal
tracking results. To address these issues, an automatic
tracking method for 3D human motion pose using
contrastive learning is proposed. By using the feature
parameters of 3D human motion poses, threshold
variation parameters of 3D human motion poses are
computed. The golden section is introduced to transform
the threshold variation parameters and extract the
features of 3D human motion poses by comparing the
feature parameters with the threshold of parameter
variation. Under the supervision of contrastive
learning, a constraint loss is added to the
local--global deep supervision module of contrastive
learning to extract local parameters of 3D human motion
poses, combined with their local features. After
normalizing the 3D human motion pose images, frame
differences of the background image are calculated. By
constructing an automatic tracking model for 3D human
motion poses, automatic tracking of 3D human motion
poses is achieved. Experimental results demonstrate
that the highest tracking lag is 9\%, there is no
deviation in node tracking, the pixel contrast is
maintained above 90\% and only 6 sub-blocks have detail
loss. This indicates that the proposed method
effectively tracks 3D human motion poses, tracks all
the nodes, achieves high accuracy in automatic tracking
and produces good tracking results.",
acknowledgement = ack-nhfb,
articleno = "2550037",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kumar:2024:NIR,
author = "Kattela Pavan Kumar and Matcha Venu Gopala Rao and And
Moram Venkatanarayana",
title = "A Novel Image Recovery from Moving Water Surface Using
Multi-Objective Bispectrum Method",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467824500384",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500384",
abstract = "Nowadays, the image degradation field suffers from
several challenges while processing underwater color
images including color distortion and image blurring
due to the scattering media. Moreover, to get
appropriate multi-frame super-resolution images, there
is essential for recovering a better quantity of
images. Traditionally, the shift among images is
directly evaluated when considering the under-sampled
Low-Resolution (LR) images. On the other hand, the
high-frequency LR image faces unreliability owing to
the aliasing consequences of sub-sampling, but it will
also degrade the recovery accuracy. This task design
implements a novel image recovery model from the moving
water surface by adopting the multi-objective adaptive
higher-order spectral analysis. Image pre-processing,
lucky region selection, and image recovery are the
three main phases of this model. The bicoherence method
and dice coefficient method are adopted for performing
the lucky region selection. Finally, the adoption of
the multi-objective adaptive bispectra method is used
for performing the image recovery from the moving water
surface. The improved Adaptive Fitness-oriented Random
number-based Galactic Swarm Optimization (AFR-GSO)
algorithm is used for optimizing the constraints of the
bispectrum method. The experimental results verify the
enrichment of image quality by the proposed model over
the existing techniques.",
acknowledgement = ack-nhfb,
articleno = "2450038",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Veling:2024:MDC,
author = "Shripad S. Veling and T. B. Mohite-patil",
title = "Multi-Disease Classification of Mango Tree Using
Meta-Heuristic-Based Weighted Feature Selection and
{LSTM} Model",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500396",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500396",
abstract = "Global food security can be influenced by the diseases
in crop plants as several diseases straightforwardly
influence the quality of the grains, vegetables,
fruits, etc., which also results in affecting of
agricultural productivity. Like other plants, the mango
tree is also affected by several diseases, and also the
identification of multi-disease classification with a
single leaf is more complex, and also it is impossible
to detect diseases with bare eyes. Based on the other
plants, the mango tree is also affected by various
diseases, which is more difficult to detect the
disorders with bare eyes. It is error-prone,
inconsistent, and unreliable. Here, the mango trees are
affected during the production, and also affect the
plant health regarding multi-diseases. When the plants
are affected by the diseases, it may cause fewer
amounts of productivity, as a result, impacting the
economy. However, it is more critical to detect plant
diseases with the large varieties of trees and plants.
Various research tasks on deep learning approaches
focus on identifying the diseases in plants including
leaves and fruits. Thus, the main objective of this
paper is to implement an effective and appropriate
technique for diagnosing mango tree diseases and their
symptoms through fruit and leaf images, and thus, there
is a need for an appropriate system for cost-effective
and early solutions to this problem. Hence, the main
intention of this work is to implement an efficient and
suitable technique for diagnosing mango tree diseases
and also identify the symptoms through fruit and leaf
images. Intending to overcome the existing challenges,
there is a need for an appropriate system for achieving
cost-effectiveness and also creating an early solution
to resolve this problem. This paper intends to present
novel deep learning models for mango tree multi-disease
classification. Initially, the data collection is done
for gathering the diseased parts of the mango tree in
terms of leaf and fruit images. Then, the contrast
enhancement of the images is performed by the
``Contrast-Limited Adaptive Histogram Equalization
(CLAHE)''. For the deep feature extraction of leaf
images, and fruit images, Convolutional Neural Network
(CNN) is employed, and the features from both inputs
are concatenated for further processing. Further, the
weighted feature selection is adopted for selecting the
most significant features by the Adaptive Squirrel-Grey
Wolf Search Optimization (AS-GWSO). Enhanced ``Long
Short Term Memory (LSTM)'' is applied in the
classification part with parameter optimization using
the same AS-GWSO for enhancing classification accuracy.
At last, the results of the designed system on various
mango tree diseases verify that the designed approach
has yielded the highest accuracy by evaluating
conventional approaches. Therefore, it would also
alleviate and treat the affected mango leaf diseases
accurately.",
acknowledgement = ack-nhfb,
articleno = "2450039",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Hamdi:2024:CAC,
author = "Dhekra {El Hamdi} and Ines Elouedi and And Ihsen
Slim",
title = "Computer-Aided Classification of Cell Lung Cancer Via
{PET\slash CT} Images Using Convolutional Neural
Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500402",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500402",
abstract = "Lung cancer is the leading cause of cancer-related
death worldwide. Therefore, early diagnosis remains
essential to allow access to appropriate curative
treatment strategies. This paper presents a novel
approach to assess the ability of Positron Emission
Tomography/Computed Tomography (PET/CT) images for the
classification of lung cancer in association with
artificial intelligence techniques. We have built, in
this work, a multi output Convolutional Neural Network
(CNN) as a tool to assist the staging of patients with
lung cancer. The TNM staging system as well as
histologic subtypes classification were adopted as a
reference. The VGG 16 network is applied to the PET/CT
images to extract the most relevant features from
images. The obtained features are then transmitted to a
three-branch classifier to specify Nodal (N), Tumor (T)
and histologic subtypes classification. Experimental
results demonstrated that our CNN model achieves good
results in TN staging and histology classification. The
proposed architecture classified the tumor size with a
high accuracy of 0.94 and the area under the curve
(AUC) of 0.97 when tested on the Lung-PET-CT-Dx
dataset. It also has yielded high performance for N
staging with an accuracy of 0.98. Besides, our approach
has achieved better accuracy than state-of-the-art
methods in histologic classification.",
acknowledgement = ack-nhfb,
articleno = "2450040",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Prasad:2024:FSC,
author = "Rajesh S. Prasad and Jayashree Rajesh Prasad and
Bhushan S. Chaudhari and Nihar M. Ranjan and And Rajat
Srivastava",
title = "{FCM} with Spatial Constraint Multi-Kernel
Distance-Based Segmentation and Optimized Deep Learning
for Flood Detection",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500414",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500414",
abstract = "Floods are the deadly and catastrophic disasters,
causing loss of life and harm to assets, farmland, and
infrastructure. To address this, it is necessary to
devise and employ an effective flood management system
that can immediately identify flood areas to initiate
relief measures as soon as possible. Therefore, this
research work develops an effective flood detection
method, named Anti- Corona-Shuffled Shepherd
Optimization Algorithm-based Deep Quantum Neural
Network (ACSSOA-based Deep QNN) for identifying the
flooded areas. Here, the segmentation process is
performed using Fuzzy C-Means with Spatial Constraint
Multi-Kernel Distance (MKFCM\_S) wherein the Fuzzy
C-Means (FCM) is modified with Spatial Constraints
Based on Kernel-Induced Distance (KFCM\_S). For flood
detection, Deep QNN has been used wherein the training
progression of Deep QNN is done using designed
optimization algorithm, called ACSSOA. Besides, the
designed ACSSOA is newly formed by the hybridization of
Anti Corona Virus Optimization (ACVO) and Shuffled
Shepherd Optimization Algorithm (SSOA). The devised
method was evaluated using the Kerala Floods database,
and it acquires the segmentation accuracy, testing
accuracy, sensitivity, and specificity with highest
values of 0.904, 0.914, 0.927, and 0.920,
respectively.",
acknowledgement = ack-nhfb,
articleno = "2450041",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Khaki:2024:RCN,
author = "Ali Khaki",
title = "Robust Convolutional Neural Network Based on {UNet}
for Iris Segmentation",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500426",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500426",
abstract = "Nowadays, the iris recognition system is one of the
most widely used and most accurate biometric systems.
The iris segmentation is the most crucial stage of iris
recognition system. The accurate iris segmentation can
improve the efficiency of iris recognition. The main
objective of iris segmentation is to obtain the iris
area. Recently, the iris segmentation methods based on
convolutional neural networks (CNNs) have been grown,
and they have improved the accuracy greatly.
Nevertheless, their accuracy is decreased by
low-quality images captured in uncontrolled conditions.
Therefore, the existing methods cannot segment
low-quality images precisely. To overcome the
challenge, this paper proposes a robust convolutional
neural network (R-Net) inspired by UNet for iris
segmentation. R-Net is divided into two parts: encoder
and decoder. In this network, several layers are added
to ResNet-34, and used in the encoder path. In the
decoder path, four convolutions are applied at each
level. Both help to obtain suitable feature maps and
increase the network accuracy. The proposed network has
been tested on four datasets: UBIRIS v2 (UBIRIS), CASIA
iris v4.0 (CASIA) distance, CASIA interval, and IIT
Delhi v1.0 (IITD). UBIRIS is a dataset that is used for
low-quality images. The error rate (NICE1) of proposed
network is 0.0055 on UBIRIS, 0.0105 on CASIA interval,
0.0043 on CASIA distance, and 0.0154 on IITD. Results
show better performance of the proposed network
compared to other methods.",
acknowledgement = ack-nhfb,
articleno = "2450042",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jannu:2024:SAN,
author = "Chaitanya Jannu and Sunny Dayal Vanambathina",
title = "Shuffle Attention {$U$}-Net for Speech Enhancement in
Time Domain",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500438",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500438",
abstract = "Over the past 10 years, deep learning has enabled
significant advancements in the improvement of noisy
speech. In an end-to-end speech enhancement, the deep
neural networks transform a noisy speech signal to a
clean speech signal in the time domain directly without
any conversion or estimation of mask. Recently, the
U-Net-based models achieved good enhancement
performance. Despite this, some of them may neglect
context-related information and detailed features of
input speech in case of ordinary convolution. To
address the above issues, recent studies have upgraded
the performance of the model by adding various network
modules such as attention mechanisms, long and
short-term memory (LSTM). In this work, we propose a
new U-Net-based speech enhancement model using a novel
lightweight and efficient Shuffle Attention (SA), Gated
Recurrent Unit (GRU), residual blocks with dilated
convolutions. Residual block will be followed by a
multi-scale convolution block (MSCB). The proposed
hybrid structure enables the temporal context
aggregation in time domain. The advantage of shuffle
attention mechanism is that the channel and spatial
attention are carried out simultaneously for each
sub-feature in order to prevent potential noises while
also highlighting the proper semantic feature areas by
combining the same features from all locations. MSCB is
employed for extracting rich temporal features. To
represent the correlation between neighboring noisy
speech frames, a two Layer GRU is added in the
bottleneck of U-Net. The experimental findings
demonstrate that the proposed model outperformed the
other existing models in terms of short-time objective
intelligibility (STOI), and perceptual evaluation of
the speech quality (PESQ).",
acknowledgement = ack-nhfb,
articleno = "2450043",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Visalini:2024:DES,
author = "K. Visalini and Saravanan Alagarsamy and And S. P.
Raja",
title = "Detecting Epileptic Seizures Using Symplectic Geometry
Decomposition-Based Features and {Gaussian} Deep
{Boltzmann} Machines",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S021946782450044X",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S021946782450044X",
abstract = "Studies deem that about 1 percent of the human
population is affected by epileptic seizures on a
global scale. It is characterized as an undue neuronal
discharge in the brain and degrades the quality of life
of the patients to a large extent. Children being
unaware of a sudden onset of seizures could be affected
by severe injury or even mortality.
Machine-learning-based epileptic seizure detection from
EEG (Electro-Encephalogram) signals have always been a
hot area of research. However, the majority of the
research works rely on correlated non-linear features
extracted from the EEG signals, causing a
high-computational overhead, and challenging their
application in real-time clinical diagnosis. This study
proposes a robust seizure detection framework using
Gaussian Deep Boltzmann Machine-based classifier and
Symplectic Geometric Decomposition (SGD)-based
features. The simplified eigenvalues derived through
Symplectic Similarity Transform (SST) are employed as
feature vectors for the classifier, eliminating the
need for a deliberate feature extraction procedure. The
study examines the transferability capability of the
suggested framework in discriminating seizures in both
neonates and pediatric subjects in unison,
experimenting with classical annotated datasets. The
model yielded a mean accuracy of about 97.91\% and an
F1 Score of 0.935 in pediatric seizure detection, and
mean sensitivity and specificity of 99.05\% and
98.28\%, in neonatal seizure detection tasks,
respectively. Thus, the model can be deemed comparable
to the available state-of-the-art seizure detection
frameworks.",
acknowledgement = ack-nhfb,
articleno = "2450044",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Prashanthi:2024:HOB,
author = "M. Prashanthi and M. Chandra Mohan",
title = "Hybrid Optimization-Based Neural Network Classifier
for Software Defect Prediction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500451",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500451",
abstract = "The software is applied in various areas so the
quality of the software is very important. The software
defect prediction (SDP) is used to solve the software
issues and enhance the quality. The robustness and
reliability are the major concerns in the existing SDP
approaches. Hence, in this paper, the hybrid
optimization-based neural network (Optimized NN) is
developed for the effective detection of the defects in
the software. The two main steps involved in the
Optimized NN-based SDP are feature selection and SDP
utilizing Optimized NN. The data is fed forwarded to
the feature selection module, where relief algorithm
selects the significant features relating to the defect
and no-defects. The features are fed to the SDP module,
and the optimal tuning of NN classifier is obtained by
the hybrid optimization developed by the integration of
the social spider algorithm (SSA) and gray wolf
optimizer (GWO). The comparative analysis of the
developed prediction model reveals the effectiveness of
the proposed method that attained the maximum accuracy
of 93.64\%, maximum sensitivity of 95.14\%, maximum
specificity of 99\%, maximum $ F_1$-score of 93.53\%,
and maximum precision of 99\% by considering the
$K$-fold.",
acknowledgement = ack-nhfb,
articleno = "2450045",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Kulkarni:2024:HBE,
author = "Girish Kulkarni and Chiranjeevi Manike",
title = "Heuristic-Based Ensemble Model Selection Strategy with
Parameter Tuning for Optimal {{\em Diabetes
mellitus\/}} Prediction",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500463",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500463",
abstract = "Diabetes is a terrible health situation characterized
by high-rise blood glucose levels. If it is not
predicted at an early stage, then it generates the
problems in the human body like kidney failure or
premature death, and stroke. Controlling blood glucose
levels provides patients with helpful dietary
recommendations, which are critical components of
diabetes management. In the past decades, diverse
conventional approaches have been executed to predict
the beginning stages of diabetes mellitus depending on
physical and substance tests. Still, developing a new
framework that can effectively diagnose diabetes
mellitus-affected patients is required. To this end,
the major target of this task is to predict diabetes
mellitus with an advanced accuracy rate with the help
of the Heuristic-based Ensemble Model Selection
Strategy (H-EMSS). In the data collection phase, the
Pima Indian Diabetes dataset (PID) is taken from the
storage area of UCI. The data cleaning is performed in
the pre-processing stage, which is the technique of
removing or fixing, corrupted, incorrect, duplicate,
incomplete data, or incorrectly formatted, inside a
dataset. Then, the diabetes prediction is accomplished
by the H-EMSS. Here, 10 base learners like Naive Bayes
(NB), Convolutional Neural Network (CNN), Linear
Regression (LR), Deep Neural Network (DNN), Support
Vector Machine (SVM), Artificial Neural Network (ANN),
Decision Tree (DT), Random Forest (RF), Auto Encoder
(AE) and Recurrent Neural Network (RNN) are considered.
From these, three classifiers are optimally selected by
the Modified Scalar Factor-based Elephant Herding
Optimization (MSF-EHO), so that the prediction rate
will be high. The suggested methodology's efficacy is
also compared and analyzed, with the findings
demonstrating the recommended model's superiority. The
overall evaluation is that the Root Mean Square Error
(RMSE) of the designed Modified Scalar Factor-based
Elephant Herding Optimization-Heuristic-based Ensemble
Model Selection Strategy (MSF-EHO-H-EMSS) attains
4.601\% and also the Mean Absolute Error (MAE) on the
designed method achieves 0.99\%. Thus, the given
outcomes of the designed method revealed that it
achieves elevated performance than the other existing
techniques regarding diverse error metrics.",
acknowledgement = ack-nhfb,
articleno = "2450046",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Dharwadkar:2024:HEC,
author = "Nagaraj V. Dharwadkar and Ashutosh A. Lonikar and And
Mufti Mahmud",
title = "High Embedding Capacity Color Image Steganography
Scheme Using Pixel Value Differencing and Addressing
the Falling-Off Boundary Problem",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500475",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500475",
abstract = "In this paper, we changed the methodology for pixel
value differencing. The proposed method work on RGB
color images improves the existing PVD technique in
terms of embedding capacity and overcomes the issue of
falling off boundaries in the traditional PVD
technique, and provides security to the secret message
from histogram quantization attack. Color images are
composed of three different color channels (red, green,
and blue), so we cannot apply the traditional pixel
value differencing algorithm to them. Due to that, the
proposed technique divides the RGB photograph in red,
blue, and green channels. Following that the modified
pixel value differencing algorithm is employed to all
successive pixels of color channels. We get the total
embedding capacity by adding the embedding capacities
of each color component. After embedding the data, we
concatenate the color channels to get the stegoimage.
On a series of color images, we tested our pixel value
differencing approach and found that the
stego-picture's visual excellence and payload capacity
were reasonable. The variation in histogram between the
stego and cover photographs was minor, making it
resistant to histogram quantization attacks, and the
suggested approach also solves the issue of falling off
the boundary.",
acknowledgement = ack-nhfb,
articleno = "2450047",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Lavanya:2024:ECM,
author = "V. Lavanya and P. Chandra Sekhar",
title = "Efficient Cybersecurity Model Using Wavelet Deep {CNN}
and Enhanced Rain Optimization Algorithm",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500487",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500487",
abstract = "Cybersecurity has received greater attention in modern
times due to the emergence of IoT (Internet-of-Things)
and CNs (Computer Networks). Because of the massive
increase in Internet access, various malicious malware
have emerged and pose significant computer security
threats. The numerous computing processes across the
network have a high risk of being tampered with or
exploited, which necessitates developing effective
intrusion detection systems. Therefore, it is essential
to build an effective cybersecurity model to detect the
different anomalies or cyber-attacks in the network.
This work introduces a new method known as {\em Wavelet
Deep Convolutional Neural Network (WDCNN)\/} to
classify cyber-attacks. The presented network combines
WDCNN with Enhanced Rain Optimization Algorithm (EROA)
to minimize the loss in the network. This proposed
algorithm is designed to detect attacks in large-scale
data and reduces the complexities of detection with
maximum detection accuracy. The proposed method is
implemented in PYTHON. The classification process is
completed with the help of the two most famous
datasets, KDD cup 1999 and CICMalDroid 2020. The
performance of WDCNN\_EROA can be assessed using
parameters like specificity, accuracy, precision
F-measure and recall. The results showed that the
proposed method is about 98.72\% accurate for the first
dataset and 98.64\% for the second dataset.",
acknowledgement = ack-nhfb,
articleno = "2450048",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Wei:2024:AIS,
author = "Yanxi Wei",
title = "Artistic Image Style Transfer Based on {CycleGAN}
Network Model",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "04",
pages = "??--??",
month = jul,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500499",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Sat Oct 19 15:24:03 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500499",
abstract = "With the development of computer technology, image
stylization has become one of the hottest technologies
in image processing. To optimize the effect of artistic
image style conversion, a method of artistic image
style conversion optimized by attention mechanism is
proposed. The CycleGAN network model is introduced, and
then the generator is optimized by the attention
mechanism. Finally, the application effect of the
improved model is tested and analyzed. The results show
that the improved model tends to be stable after 40
iterations, the loss value remains at 0.3, and the PSNR
value can reach up to 15. From the perspective of the
generated image effect, the model has a better visual
effect than the CycleGAN model. In the subjective
evaluation, 63 people expressed satisfaction with the
converted artistic image. As a result, the cyclic
generative adversarial network model optimized by the
attention mechanism improves the clarity of the
generated image, enhances the effect of blurring the
target boundary contour, retains the detailed
information of the image, optimizes the image
stylization effect, and improves the image quality of
the method and application value of the processing
field.",
acknowledgement = ack-nhfb,
articleno = "2450049",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Sasikala:2024:ECD,
author = "P. Sasikala and L. Mary Immaculate Sheela",
title = "An Efficient {COVID-19} Disease Outbreak Prediction
Using {BI-SSOA-TMLPNN} and {ARIMA}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1142/S0219467823400119",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823400119",
abstract = "Globally, people's health and wealth are affected by
the outbreak of the corona virus. It is a virus, which
infects from common fever to severe acute respiratory
syndrome. It has the potency to transmit from one
person to another. It is established that this virus
spread is augmenting speedily devoid of any symptoms.
Therefore, the prediction of this outbreak situation
with mathematical modelling is highly significant along
with necessary. To produce informed decisions along
with to adopt pertinent control measures, a number of
outbreak prediction methodologies for COVID-19 are
being utilized by officials worldwide. An effectual
COVID-19 outbreaks' prediction by employing Squirrel
Search Optimization Algorithm centric Tanh Multi-Layer
Perceptron Neural Network (MLPNN) (SSOA-TMLPNN) along
with Auto-Regressive Integrated Moving Average (ARIMA)
methodologies is proposed here. Initially, from the
openly accessible sources, the input time series
COVID-19 data are amassed. Then, pre-processing is
performed for better classification outcomes after
collecting the data. Next, by utilizing Sine-centered
Empirical Mode Decomposition (S-EMD) methodology, the
data decomposition is executed. Subsequently, the data
are input to the Brownian motion Intense (BI) -
SSOA-TMLPNN classifier. In this, the diseased,
recovered, and death cases in the country are
classified. After that, regarding the time-series data,
the corona-virus's future outbreak is predicted by
employing ARIMA. Afterwards, data visualization is
conducted. Lastly, to evaluate the proposed model's
efficacy, its outcomes are analogized with certain
prevailing methodologies. The obtained outcomes
revealed that the proposed methodology surpassed the
other existing methodologies.",
acknowledgement = ack-nhfb,
articleno = "2340011",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shalini:2024:EJF,
author = "L Shalini and K Vijayakumar",
title = "An Efficient {JSH-FCM}-Based Thyroid Disease Detection
Using {ASH-ANN} with Stage Classification via a Fuzzy
Rule-Based Approach",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467823400120",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823400120",
abstract = "One of the most misunderstood and undiagnosed diseases
is termed Thyroid Disease (TD), which is a subset of
endocrinology. It emerges at the edge of the thyroid
gland due to the abnormal development of thyroid
tissue. Owing to the lack of awareness and early
diagnosis, TD is a critical problem in underdeveloped
countries. For TD diagnosis, various theoretical works
have been introduced; still, in the early diagnosis of
TD, accurate prediction of the thyroid data is a
significant problem. Thus, by utilizing Altered
SigHyper activation-centric Artificial Neural Network
(ANN) (ASH-ANN) with various stage classifications, an
effectual Jaccard Similarity and He-initialization
induced Fuzzy C-Means (FCM) (JSH-FCM)
clustering-centric TD detection system is proposed by
means of a fuzzy rule-centric methodology. Initially,
for accurate detection, the thyroid dataset is gathered
and the data is pre-processed. Next, by JSH-FCM
clustering, the age-centric clustering is carried out.
After that, by utilizing Pearson
Correlation-amalgamated Principal Component Analysis
((PC)$^2$ A), Feature Extraction (FE) and feature
selection is conducted. Moreover, to detect the TD
kind, an ASH-ANN classifier is wielded. Finally, for
differentiating the stages of TD, the fuzzy rule is
employed. The experimental outcomes depict that the
proposed system achieved superior performance with an
accuracy of 97.32% when weighed against the prevailing
system; in addition, the stages of TD are
differentiated precisely.",
acknowledgement = ack-nhfb,
articleno = "2340012",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Praveen:2024:CKD,
author = "S Phani Praveen and Veerapaneni Esther Jyothi and
Chokka Anuradha and K Venugopal and Vahiduddin Shariff
and And S Sindhura",
title = "Chronic Kidney Disease Prediction Using {ML}-Based
Neuro-Fuzzy Model",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467823400132",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823400132",
abstract = "Nowadays, in most countries, the most dangerous and
life threatening infection is Chronic Kidney Disease
(CKD). A progressive malfunctioning of the kidneys and
less effectiveness of the kidney are considered CKD.
CKD can be a life threatening disease if it continues
for longer period of time. Prediction of chronic
disease in early stage is very crucial so that
sustainable care of the patient is taken to prevent
menacing situations. Most of the developing countries
are being affected by this deadly disease and treatment
applied for this disease is also very expensive, here
in this paper, a Machine Learning (ML)-positioned
approach called Neuro-Fuzzy model is used for
prediction belonging to CKD. Based on the image
processing technique, fibrosis proportions are detected
in the kidney tissues. It also builds a system for
identifying and detection of CKD at an early stage.
Neuro-Fuzzy model is based on ML which can detect risk
of CKD patients. Compared with other conventional
methods such as Support Vector Machine (SVM) and
K-Nearest Neighbor (KNN), the proposed method of this
paper --- ML-based Neuro-Fuzzy logic method ---
obtained 97% accuracy in CKD prediction. This method
can be evaluated based on various parameters such as
Precision, Accuracy, Recall and F1-Score in CKD
prediction. From the results, the patients having high
risk of chronic disease can be predicted.",
acknowledgement = ack-nhfb,
articleno = "2340013",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Bhan:2024:CMS,
author = "Anupama Bhan and Partha Sarathi Mangipudi and And
Ayush Goyal",
title = "Cardiac {MRI} Segmentation Using Efficient
{ResNeXT-50}-Based {IEI} Level Set and Anisotropic
Sigmoid Diffusion Algorithms",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467823400144",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467823400144",
abstract = "Endocardial and epicardial border identification has
been of extensive interest in cardiac Magnetic
Resonance Images (MRIs). It is a difficult job to
segment the epicardium and endocardium accurately and
automatically from cardiac MRI owing to the cardiac
tissues' complexity even though the prevailing Deep
Learning (DL) methodologies had attained significant
success in medical imaging segmentation. Hence, by
employing effectual ResNeXT-50-centric Inverse Edge
Indicator Level Set (IEILS) and anisotropic sigmoid
diffusion algorithms, this system has proposed cardiac
MRI segmentation. The work has endured some function
for an effectual partition of epicardium and
endocardium. Initially, by employing the Truncated
Kernel Function (TK)-Trilateral Filter, the noise
removal function is executed on the input cardiac MRI.
Next, by wielding the ResNeXT-50 IEILS, the Left and
Right Ventricular (LV/RV) regions are segmented. The
epicardium and endocardium are segmented by the ASD
algorithm once the LV/RV is separated from the Left
Ventricle (LV) region. Here, the openly accessible
Sunnybrook and the Right Ventricle (RV) datasets are
wielded. Then, the prevailing state-of-art algorithms
are analogized to the outcomes achieved by the proposed
framework. Regarding accuracy, sensitivity, and
specificity, the proposed methodology executed the
cardiac MRI segmentation process precisely along with
the other surpassed state-of-the-art methodologies.",
acknowledgement = ack-nhfb,
articleno = "2340014",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Iqbal:2024:RND,
author = "Md. Asim Iqbal and K. Devarajan and And Syed Musthak
Ahmed",
title = "{RDN-NET}: A Deep Learning Framework for Asthma
Prediction and Classification Using Recurrent Deep
Neural Network",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500505",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500505",
abstract = "Asthma is the one of the crucial types of disease,
which causes the huge deaths of all age groups around
the world. So, early detection and prevention of asthma
disease can save numerous lives and are also helpful to
the medical field. But the conventional machine
learning methods have failed to detect the asthma from
the speech signals and resulted in low accuracy. Thus,
this paper presented the advanced deep learning-based
asthma prediction and classification using recurrent
deep neural network (RDN-Net). Initially, speech
signals are preprocessed by using minimum
mean-square-error short-time spectral amplitude
(MMSE-STSA) method, which is used to remove the noises
and enhances the speech properties. Then, improved
Ripplet-II Transform (IR2T) is used to extract
disease-dependent and disease-specific features. Then,
modified gray wolf optimization (MGWO)-based
bio-optimization approach is used to select the optimal
features by hunting process. Finally, RDN-Net is used
to predict the asthma disease present from speech
signal and classifies the type as either wheeze,
crackle or normal. The simulations are carried out on
real-time COSWARA dataset and the proposed method
resulted in better performance for all metrics as
compared to the state-of-the-art approaches.",
acknowledgement = ack-nhfb,
articleno = "2450050",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mundada:2024:MIB,
author = "Kapil Mundada and Jayant Kulkarni",
title = "{MRI} Image-Based Automatic Segmentation and
Classification of Brain Tumor and Swelling Using Novel
Methodologies",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500517",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500517",
abstract = "In the medical image analysis field, brain tumors
(BTs) classification is a complicated process. For
effortlessly detecting the tumor devoid of any surgical
interference, the radiologists are aided with automated
along with computerized technology. Currently, in the
field of medical image processing along with analysis,
admirable progress has been made by deep learning (DL)
methodologies. In medical fields, for resolving several
issues, huge attention was paid to DL techniques. For
automation of several performed by radiologists like
(1) lesion detection, (2) segmentation, (3)
classification, (4) monitoring, along with (5) also
prediction of treatment response that is not achievable
without software, DL might be wielded. Nevertheless,
classifying BTs by utilizing magnetic resonance imaging
(MRI) has various complications like the difficulty of
brain structure along with the intertwining of tissues
in it; additionally, the brain's higher density nature
also makes the BT Classification (BTC) process quite
complex. Therefore, by utilizing novel systems,
MRI-centric Automatic segmentation together with
classifications of BT and swelling have been proposed
to overcome the aforementioned issues. The proposed
methodology underwent various operations to detect BTs
effectively. Initially, by utilizing the Range-centric
Otsu's Thresholding (ROT) algorithm, the skull
stripping (SS) is conducted. After that, by performing
contrast enhancement (CE) along with noise removal, the
skull-stripped images are pre-processed. Next, by
employing the Rectilinear Watershed Segmentation (RWS)
algorithm, the tumor or swelling areas are segmented.
Afterward, to obtain the precise tumor or swelling
region, the morphological operations are executed on
the segmented areas; subsequently, the desired along
with relevant features are extracted. Lastly, the
features being extracted are inputted to the classifier
termed Uniform Convolution neural network (UCNN). The
tumor tissues along with the swelling tissues are
classified precisely in the classification phase. Here,
the openly accessible BT Image Segmentation Benchmark
(BRATS) datasets are utilized. Then, the outcomes
obtained are analogized with prevailing methodologies.
The experiential outcomes displayed that the BTC is
performed by the proposed model with a higher accuracy
rate; thus, outshined the other prevailing models.",
acknowledgement = ack-nhfb,
articleno = "2450051",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Mangai:2024:TSS,
author = "P. Mangai and M. Kalaiselvi Geetha and And G.
Kumaravelan",
title = "Two-Stream Spatial--Temporal Feature Extraction and
Classification Model for Anomaly Event Detection Using
Hybrid Deep Learning Architectures",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500529",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500529",
abstract = "Identifying events using surveillance videos is a
major source that reduces crimes and illegal
activities. Specifically, abnormal event detection
gains more attention so that immediate responses can be
provided. Video processing using conventional
techniques identifies the events but fails to
categorize them. Recently deep learning-based video
processing applications provide excellent performances
however the architecture considers either spatial or
temporal features for event detection. To enhance the
detection rate and classification accuracy in abnormal
event detection from video keyframes, it is essential
to consider both spatial and temporal features. Earlier
approaches consider any one of the features from
keyframes to detect the anomalies from video frames.
However, the results are not accurate and prone to
errors sometimes due to video environmental and other
factors. Thus, two-stream hybrid deep learning
architecture is presented to handle spatial and
temporal features in the video anomaly detection
process to attain enhanced detection performances. The
proposed hybrid models extract spatial features using
YOLO-V4 with VGG-16, and temporal features using
optical FlowNet with VGG-16. The extracted features are
fused and classified using hybrid CNN-LSTM model.
Experimentation using benchmark UCF crime dataset
validates the proposed model performances over existing
anomaly detection methods. The proposed model attains
maximum accuracy of 95.6% which indicates better
performance compared to state-of-the-art techniques.",
acknowledgement = ack-nhfb,
articleno = "2450052",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Jannu:2024:SAB,
author = "Chaitanya Jannu and Sunny Dayal Vanambathina",
title = "Self-Attention-Based Convolutional {GRU} for
Enhancement of Adversarial Speech Examples",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500530",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500530",
abstract = "Recent research has identified adversarial examples
which are the challenges to DNN-based ASR systems. In
this paper, we propose a new model based on
Convolutional GRU and Self-attention U-Net called
GRU-U-Net_{AT} to improve adversarial speech signals.
To represent the correlation between neighboring noisy
speech frames, a two-Layer GRU is added in the
bottleneck of U-Net and an attention gate is inserted
in up-sampling units to increase the adversarial
stability. The goal of using GRU is to combine the
weights sharing technique with the use of gates to
control the flow of data across multiple feature maps.
As a result, it outperforms the original 1D convolution
used in U-Net_{AT} . Especially, the performance of the
model is evaluated by explainable speech recognition
metrics and its performance is analyzed by the improved
adversarial training. We used adversarial audio attacks
to perform experiments on automatic speech recognition
(ASR). We saw (i) the robustness of ASR models which
are based on DNN can be improved using the temporal
features grasped by the attention-based GRU network;
(ii) through adversarial training, including some
additive adversarial data augmentation, we could
improve the generalization power of Automatic Speech
Recognition models which are based on DNN. The
word-error-rate (WER) metric confirmed that the
enhancement capabilities are better than the
state-of-the-art U-Net_{AT} . The reason for this
enhancement is the ability of GRU units to extract
global information within the feature maps. Based on
the conducted experiments, the proposed GRU-U-Net_{AT}
increases the score of Speech Transmission Index (STI),
Perceptual Evaluation of Speech Quality (PESQ), and the
Short-term Objective Intelligibility (STOI) with
adversarial speech examples in speech enhancement.",
acknowledgement = ack-nhfb,
articleno = "2450053",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Shanmugasundaram:2024:IBB,
author = "Suresh Shanmugasundaram and Natarajan Palaniappan",
title = "Improvement of Bounding Box and Instance Segmentation
Accuracy Using {ResNet-152 FPN} with Modulated
Deformable {ConvNets v2} Backbone-based Mask Scoring
{R-CNN}",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500542",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500542",
abstract = "A challenging task is to make sure that the deep
learning network learns prediction accuracy by itself.
Intersection-over-Union (IoU) amidst ground truth and
instance mask determines mask quality. There is no
relationship between classification score and mask
quality. The mission is to investigate this problem and
learn the predicted instance mask's accuracy. The
proposed network regresses the MaskIoU by comparing the
predicted mask and the respective instance feature. The
mask scoring strategy determines the disorder among
mask score and mask quality, then adjusts the
parameters accordingly. Adaptation ability to the
object's geometric variations decides deformable
convolutional network's performance. Using increased
modeling power and stronger training, focusing ability
on pertinent image regions is improved by a
reformulated Deformable ConvNets. The introduction of
modulation technique, which broadens the deformation
modeling scope, and the integration of deformable
convolution comprehensively within the network enhance
the modeling power. The features which resemble
region-based convolutional neural network (R-CNN)
feature's classification capability and its object
focus are learned by the network with the help of
feature mimicking scheme of DCNv2. Feature mimicking
scheme of DCNv2 guides the network training to
efficiently control this enhanced modeling capability.
The backbone of the proposed Mask Scoring R-CNN network
is designed with ResNet-152 FPN and DCNv2 network. The
proposed Mask Scoring R-CNN network with DCNv2 network
is also tested with other backbones ResNet-50 and
ResNet-101. Instance segmentation and object detection
on COCO benchmark and Cityscapes dataset are achieved
with top accuracy and improved performance using the
proposed network.",
acknowledgement = ack-nhfb,
articleno = "2450054",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Palanimeera:2024:YPR,
author = "J. Palanimeera and K. Ponmozhi",
title = "Yoga Posture Recognition by Learning Spatial-Temporal
Feature with Deep Learning Techniques",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824500554",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824500554",
abstract = "Yoga posture recognition remains a difficult issue
because of crowded backgrounds, varied settings,
occlusions, viewpoint alterations, and camera motions,
despite recent promising advances in deep learning. In
this paper, the method for accurately detecting various
yoga poses using DL (Deep Learning) algorithms is
provided. Using a standard RGB camera, six yoga poses
--- Sukhasana, Kakasana, Naukasana, Dhanurasana,
Tadasana, and Vrikshasana --- were captured on ten
people, five men and five women. In this study, a
brand-new DL model is presented for representing the
spatio-temporal (ST) variation of skeleton-based yoga
poses in movies. It is advised to use a variety of
representation learners to pry video-level temporal
recordings, which combine spatio-temporal sampling with
long-range time mastering to produce a successful and
effective training approach. A novel feature extraction
method using Open Pose is described, together with a
DenceBi-directional LSTM network to represent
spatial-temporal links in both the forward and backward
directions. This will increase the efficacy and
consistency of modeling long-range action detection. To
improve temporal pattern modeling capability, they are
stacked and combined with dense skip connections. To
improve performance, two modalities from look and
motion are fused with a fusion module and compared to
other deep learning models are LSTMs including LSTM,
Bi-LSTM, Res-LSTM, and Res-BiLSTM. Studies on real-time
datasets of yoga poses show that the suggested
DenseBi-LSTM model performs better and yields better
results than state-of-the-art techniques for yoga pose
detection.",
acknowledgement = ack-nhfb,
articleno = "2450055",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
@Article{Anonymous:2024:AIV,
author = "Anonymous",
title = "Author Index (Volume 24)",
journal = j-INT-J-IMAGE-GRAPHICS,
volume = "24",
number = "06",
pages = "??--??",
month = nov,
year = "2024",
DOI = "https://doi.org/10.1142/S0219467824990018",
ISSN = "0219-4678",
ISSN-L = "0219-4678",
bibdate = "Thu Nov 21 07:12:35 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
URL = "https://www.worldscientific.com/doi/10.1142/S0219467824990018",
acknowledgement = ack-nhfb,
articleno = "2499001",
fjournal = "International Journal of Image and Graphics (IJIG)",
journal-URL = "http://www.worldscientific.com/worldscinet/ijig",
}
