8.8.3 MRF Models for Segmentation

Hansen, F.R., and Elliott, H.,
Image Segmentation Using Simple Markov Field Models,
CGIP(20), No. 2, October 1982, pp. 101-132.
WWW Version. BibRef 8210

Derin, H., Cole, W.S.,
Segmentation of Textured Images Using Gibbs Random Fields,
CVGIP(35), No. 1, July 1986, pp. 72-98. BibRef 8607

Derin, H.[Haluk], and Elliott, H.,
Modelling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields,
PAMI(9), No. 1, January 1987, pp. 39-55. See also Unsupervised Segmentation of Noisy and Textured Images Using Markov Random Fields. For a later view: See also On the Estimation of Markov Random Field Parameters. BibRef 8701

Won, C.S.[Chee Sun], Derin, H.[Haluk],
Unsupervised Segmentation of Noisy and Textured Images Using Markov Random Fields,
GMIP(54), No. 4, July 1992, pp. 308-328. See also Modelling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields. BibRef 9207

Derin, H., Elliott, H., Cristi, R., and Geman, D.,
Bayes Smoothing Algorithms for Segmentation of Binary Images Modeled by Markov Random Fields,
PAMI(6), No. 6, November 1984, pp. 707-720. Neighborhoods. BibRef 8411

Bouman, C., and Liu, B.,
Multiple Resolution Segmentation of Textured Images,
PAMI(13), No. 2, February 1991, pp. 99-113.
IEEE Abstract. IEEE Top Reference.
WWW Version. Wavelets. Markov random field based analysis using wavelets. BibRef 9102

Bouman, C., and Shapiro, M.,
A Multiscale Random Field Model for Bayesian Image Segmentation,
IP(3), No. 2, March 1994, pp. 162-177.
WWW Version. BibRef 9403

Manjunath, B.S., and Chellappa, R.,
Unsupervised Texture Segmentation Using Markov Random Field Models,
PAMI(13), No. 5, May 1991, pp. 478-482.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9105
And:
A Computational Approach to Boundary Detection,
CVPR91(358-363).
IEEE Abstract. IEEE Top Reference. Segmentation, MRF. Divide into non-overlapping regions and merge according to the texture measure. See also Classification of Textures Using Gaussian Markov Random Fields. BibRef

Manjunath, B.S., Simchony, T., and Chellappa, R.,
Stochastic and Deterministic Networks for Texture Segmentation,
ASSP(38), June 1990, pp. 1039-1049.
PDF Version. BibRef 9006

Manjunath, B.S., Shekhar, C., Chellappa, R.,
A New Approach to Image Feature Detection with Applications,
PR(29), No. 4, April 1996, pp. 627-640.
WWW Version. BibRef 9604

Krishnamachari, S., Chellappa, R.,
Multiresolution Gauss-Markov Random-Field Models for Texture Segmentation,
IP(6), No. 2, February 1997, pp. 251-267.
WWW Version. 9703 BibRef
Earlier:
GMRF models and wavelet decomposition for texture segmentation,
ICIP95(III: 568-571).
WWW Version. 9510 BibRef

Chellappa, R., and Krishnamachari, S.[Santhana],
Multiresolution GMRF Models for Image Segmentation,
AIU96(13-27). BibRef 9600

Bhagavathy, S., Manjunath, B.S.,
Modeling and Detection of Geospatial Objects Using Texture Motifs,
GeoRS(44), No. 12, December 2006, pp. 3706-3715.
WWW Version. 0701 See also texture descriptor for browsing and similarity retrieval, A. BibRef

Bhagavathy, S., Newsam, S.D., Manjunath, B.S.,
Modeling object classes in aerial images using texture motifs,
ICPR02(II: 981-984).
WWW Version. 0211 See also texture descriptor for browsing and similarity retrieval, A. BibRef

Newsam, S.D., Bhagavathy, S., Manjunath, B.S.,
Object localization using texture motifs and markov random fields,
ICIP03(II: 1049-1052).
IEEE Abstract. IEEE Top Reference. 0312 BibRef
Earlier:
Modeling object classes in aerial images using hidden Markov models,
ICIP02(I: 860-863).
IEEE Abstract. IEEE Top Reference. 0210 BibRef

Geiger, D., and Yuille, A.L.,
A Common Framework for Image Segmentation,
IJCV(6), No. 3, August 1991, pp. 227-243. BibRef 9108
Earlier: ICPR90(I: 502-507).
WWW Version. MRF models, but where does it lead? BibRef

Dubes, R.C., Jain, A.K.,
Random Field Models in Image Analysis,
AppStat(16), No. 2, 1989, pp. 131-164. See also Segmentation and Classification of Range Images. BibRef 8900

Cohen, F.S., and Fan, Z.,
Maximum Likelihood Unsupervised Textured Image Segmentation,
GMIP(54), No. 3, 1992, pp. 239-251. BibRef 9200

Cohen, F.S., and Cooper, D.B.,
Real Time Textured Image Segmentation Based on Noncausal Markovian Random Field Models,
BrownLEMS-3, Providence, RI 02912, 1986. BibRef 8600

Cohen, F.S., and Cooper, D.B.,
Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields,
PAMI(9), No. 2, March 1987, pp. 195-219. BibRef 8703
Earlier: BrownLEMS-7, Providence RI 02912. Relaxation. BibRef

Silverman, J.F., Cooper, D.B.,
Bayesian Clustering for Unsupervised Estimation of Surface and Texture Models,
PAMI(10), No. 4, July 1988, pp. 482-495.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 8807
Earlier:
Unsupervised Bayesian Model-Learning with Application to Textured and Polynomial Image Segmentation,
ICCV87(672-676). BibRef

Cohen, F.S., Cooper, D.B., Silverman, J.F., and Hinkle, E.B.,
Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Textured Images Based on Noncausal Markovian Random Field Models,
ICPR84(1104-1107). BibRef 8400

Huang, C.L., Cheng, T.Y., Chen, C.C.,
Color Images' Segmentation Using Scale Space Filter and Markov Random Field,
PR(25), No. 10, October 1992, pp. 1217-1229.
WWW Version. BibRef 9210

Kim, I.Y., Yang, H.S.,
Efficient Image Labeling Based on Markov Random Field and Error Backpropagation Network,
PR(26), No. 11, November 1993, pp. 1695-1707.
WWW Version. BibRef 9311
Earlier:
Efficient Image Understanding Based on the Markov Random Field Model and Error Backpropagation Network,
ICPR92(I:441-444).
WWW Version. BibRef

Kim, I.Y., Yang, H.S.,
A Systematic Way for Region-Based Image Segmentation Based on Markov Random-Field Model,
PRL(15), No. 10, October 1994, pp. 969-976. BibRef 9410

Kim, I.Y., Yang, H.S.,
An Integrated Approach for Scene Understanding Based on Markov Random-Field Model,
PR(28), No. 12, December 1995, pp. 1887-1897.
WWW Version. BibRef 9512

Kim, I.Y., Yang, H.S.,
An Integration Scheme for Image Segmentation and Labeling Based on Markov Random-Field Model,
PAMI(18), No. 1, January 1996, pp. 69-73.
IEEE Abstract. IEEE Top Reference.
WWW Version. Combine interpretation and segmentation. Segmentation, Knowledge. BibRef 9601

Kervrann, C., Heitz, F.,
A Markov Random-Field Model-Based Approach to Unsupervised Texture Segmentation Using Local and Global Spatial Statistics,
IP(4), No. 6, June 1995, pp. 856-862.
WWW Version. BibRef 9506

Hussain, I., Reed, T.R.,
Bond Percolation Based Gibbs-Markov Random Fields for Image Segmentation,
SPLetters(2), 1995, pp. 145. BibRef 9500
And: Addition: SPLetters(3), No. 4, April 1996, pp. 127. 9605 BibRef
Earlier:
Segmentation-based nonlinear image smoothing,
ICIP94(II: 507-511).
WWW Version. 9411 BibRef

Hussain, I., Reed, T.R.,
A Bond Percolation Based Model for Image Segmentation,
IP(6), No. 12, December 1997, pp. 1698-1704.
WWW Version. 9712 BibRef

Wu, C.H., Doerschuk, P.C.,
Cluster Expansions for the Deterministic Computation of Bayesian Estimators Based on Markov Random Fields,
PAMI(17), No. 3, March 1995, pp. 275-293.
IEEE Abstract. IEEE Top Reference.
WWW Version. Computation of the mean of the Markov Random Field. See also Tree Approximations to Markov Random-Fields. BibRef 9503

Wu, C.H., and Doerschuk, P.C.,
Texture-Based Segmentation Using Markov Random Field Models and Approximate Bayesian Estimators Based on Trees,
JMIV(5), No. 4, December 1995, pp. 277-286. See also Tree Approximations to Markov Random-Fields. BibRef 9512

Andrey, P., Tarroux, P.,
Unsupervised Image Segmentation Using A Distributed Genetic Algorithm,
PR(27), No. 5, May 1994, pp. 659-673.
WWW Version. Segmentation, Learning. Genetic Algorithms. BibRef 9405

Andrey, P., Tarroux, P.,
Unsupervised Segmentation of Markov Random-Field Modeled Textured Images Using Selectionist Relaxation,
PAMI(20), No. 3, March 1998, pp. 252-262.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9805 BibRef
Earlier:
Unsupervised Texture Segmentation Using Selectionist Relaxation,
ECCV96(I:482-491).
WWW Version. MRF texture and genetic algorithm for analysis. Relaxation process where labels spread. BibRef

Smits, P.C., Dellepiane, S.G.,
An Irregular MRF Region Label Model for Multichannel Image Segmentation,
PRL(18), No. 11-13, November 1997, pp. 1133-1142. 9806 BibRef
And:
Discontinuity Adaptive MRF Model for the Analysis of Synthetic Aperture Radar Images,
ICIP97(I: 837-840).
WWW Version. BibRef
Earlier:
Information Fusion in a Markov Random Field Based Image Segmentation Approach Using Adaptive Neighbourhoods,
ICPR96(II: 570-575).
WWW Version. 9608(Univ. di Genoa., I) BibRef

Smits, P.C., Dellepiane, S.G., Vernazza, G.,
Discontinuity adaptive MRF model for synthetic aperture radar image analysis,
CIAP97(I: 255-262).
WWW Version. 9709 BibRef

Dellepiane, S.G., Fontana, F., Vernazza, G.,
A robust non-iterative method for image labelling using context,
ICIP94(II: 207-211).
WWW Version. 9411 BibRef

Zhang, J., Wang, D.Y.,
Image Segmentation By Multigrid Markov Random Field Optimization and Perceptual Considerations,
JEI(7), No. 1, January 1998, pp. 52-60. 9807 BibRef

Saquib, S.S., Bouman, C.A., Sauer, K.,
ML Parameter Estimation for Markov Random Fields with Applications to Bayesian Tomography,
IP(7), No. 7, July 1998, pp. 1029-1044.
WWW Version. 9807 BibRef

Pollak, L., Siskind, J.M., Harper, M.P., Bouman, C.A.,
Parameter estimation for spatial random trees using the EM algorithm,
ICIP03(I: 257-260).
IEEE Abstract. IEEE Top Reference. 0312 BibRef

Hu, R., Fahmy, M.M.,
Texture segmentation based on a hierarchical Markov random field model,
SP(26), No. 3, 1992, pp. 285-305. BibRef 9200

Borges, C.F.[Carlos F.],
On the Estimation of Markov Random Field Parameters,
PAMI(21), No. 3, March 1999, pp. 216-224.
IEEE Abstract. IEEE Top Reference.
WWW Version. Examine the method of: See also Modelling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields. BibRef 9903

Poggi, G., Ragozini, A.R.P.,
Image Segmentation by Tree-Structured Markov Random Fields,
SPLetters(7), No. 7, July 1999, pp. 155.
IEEE Top Reference. BibRef 9907

d'Elia, C., Poggi, G., Scarpa, G.,
A Tree-Structured Markov Random Field Model for Bayesian Image Segmentation,
IP(12), No. 10, October 2003, pp. 1259-1273.
WWW Version. 0310 BibRef
And:
Sequential Bayesian segmentation of remote sensing images,
ICIP03(III: 985-988).
IEEE Abstract. IEEE Top Reference. 0312 BibRef

Poggi, G.[Giovanni], Scarpa, G.[Giuseppe], Zerubia, J.B.[Josiane B.],
Supervised segmentation of remote sensing images based on a tree-structured MRF model,
GeoRS(43), No. 8, August 2005, pp. 1901-1911.
WWW Version. 0508 BibRef
Earlier:
Segmentation of remote-sensing images by supervised TS-MRF,
ICIP04(III: 1867-1870).
WWW Version. 0505 BibRef
Earlier: A2, A1, A3:
A binary tree-structured MRF model for multispectral satellite image segmentation,
INRIARR-5062, 2003.
HTML Version. BibRef

Gaetano, R., Poggi, G.[Giovanni], Scarpa, G.[Giuseppe],
Hierarchical Mrf-Based Segmentation of Remote-Sensing Images,
ICIP06(1121-1124). 0610
WWW Version. BibRef

Scarpa, G.[Giuseppe], Haindl, M.[Michal], Zerubia, J.[Josiane],
A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images,
SCIA07(303-312).
WWW Version. 0706 BibRef

Scarpa, G.[Giuseppe], Haindl, M.[Michal],
Unsupervised Texture Segmentation by Spectral-Spatial-Independent Clustering,
ICPR06(II: 151-154).
WWW Version. 0609 BibRef

Haindl, M.[Michal], Mikeš, S.[Stanislav],
Unsupervised Texture Segmentation Using Multispectral Modelling Approach,
ICPR06(II: 203-206).
WWW Version. 0609 BibRef
Earlier:
Model-Based Texture Segmentation,
ICIAR04(II: 306-313).
WWW Version. 0409 BibRef

Haindl, M.,
Recursive Square-root Filters,
ICPR00(Vol II: 1014-1017).
WWW Version.
HTML Version. 0009 BibRef

Haindl, M.,
Texture Segmentation Using Recursive Markov Random Field Parameter Estimation,
SCIA99(Statistical Methods). BibRef 9900

Aas, K.[Kjersti], Eikvil, L.[Line], Huseby, R.B.[Ragnar Bang],
Applications of hidden Markov chains in image analysis,
PR(32), No. 4, April 1999, pp. 703-713.
WWW Version. BibRef 9904

Dong, Y., Forester, B.C., Milne, A.K.,
Segmentation of radar imagery using the Gaussian Markov random field model,
JRS(20), No. 8, May 1999, pp. 1617. BibRef 9905

Kim, H.J., Kim, E.Y., Kim, J.W., Park, S.H.,
MRF Model Based Image Segmentation Using Hierarchical Distributed Genetic Algorithm,
IEE Electronic Letters(35), No. 25, 1998, pp. xx-yy. Genetic algorithm for segmentation. BibRef 9800

Kim, E.Y., Park, S.H., Kim, H.J.,
A Genetic Algorithm-Based Segmentation of Markov Random Field Modeled Images,
SPLetters(7), No. 11, November 2000, pp. 301-303.
IEEE Top Reference. 0010 BibRef

Mignotte, M., Collet, C., Pérez, P., Bouthemy, P.,
Three-Class Markovian Segmentation of High-Resolution Sonar Images,
CVIU(76), No. 3, December 1999, pp. 191-204. 0001
WWW Version. BibRef

Mignotte, M., Collet, C., Perez, P., Bouthemy, P.,
Sonar Image Segmentation Using an Unsupervised Hierarchical MRF Model,
IP(9), No. 7, July 2000, pp. 1216-1231.
WWW Version. 0006 BibRef

Lemoyne, J., Collet, C.,
Seafloor Texture Classification with a Multiscale Discriminant Analysis on High Resolution Sonar Images,
MVA98(xx-yy). BibRef 9800

Mignotte, M., Collet, C., Pérez, P., Bouthemy, P.,
Markov Random Field and Fuzzy Logic Modeling in Sonar Imagery: Application to the Classification of Underwater Floor,
CVIU(79), No. 1, July 2000, pp. 4-24. 0006
WWW Version. See also Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification of Shadow Shapes in Sonar Imagery. BibRef

Yao, K.C., Mignotte, M., Collet, C., Galerne, P., Burel, G.,
Unsupervised segmentation using a self-organizing map and a noise model estimation in sonar imagery,
PR(33), No. 9, September 2000, pp. 1575-1584.
WWW Version. 0005 BibRef

Collet, C.[Christophe], Thourel, P., Perez, P., Bouthemy, P.,
Hierarchical MRF Modeling for Sonar Picture Segmentation,
ICIP96(III: 979-982).
WWW Version. BibRef 9600

Barker, S.A., Rayner, P.J.W.,
Unsupervised image segmentation using Markov random field models,
PR(33), No. 4, April 2000, pp. 587-602.
WWW Version. 0002 BibRef

Wang, L.[Lei], Liu, J.[Jun],
Texture segmentation based on MRMRF modeling,
PRL(21), No. 2, February 2000, pp. 189-200. 0003 BibRef

Lanterman, A.D.[Aaron D.], Grenander, U.[Ulf], Miller, M.I.[Michael I.],
Bayesian Segmentation via Asymptotic Partition Functions,
PAMI(22), No. 4, April 2000, pp. 337-347.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0006 BibRef

Sarkar, A., Biswas, M.K., Sharma, K.M.S.,
A Simple Unsupervised MRF Model Based Image Segmentation Approach,
IP(9), No. 5, May 2000, pp. 801-812.
WWW Version. 0005 BibRef

Sarkar, A., Biswas, M.K., Kartikeyan, B., Kumar, V., Majumder, K.L., Pal, D.K.,
A MRF Model-Based Segmentation Approach to Classification for Multispectral Imagery,
GeoRS(40), No. 5, May 2002, pp. 1102-1113.
IEEE Top Reference. 0206 BibRef

Hazel, G.G.,
Multivariate Gaussian MRF for Multispectral Scene Segmentation and Anomaly Detection,
GeoRS(38), No. 3, May 2000, pp. 1199-1211.
IEEE Top Reference. 0006 BibRef

Szirányi, T.[Tamás], Zerubia, J.B.[Josiane B.], Czúni, L.[László], Geldreich, D.[David], Kato, Z.[Zoltán],
Image Segmentation Using Markov Random Field Model in Fully Parallel Cellular Network Architectures,
RealTimeImg(6), No. 3, June 2000, pp. 195-211. 0008 BibRef

Czúni, L.[László], Szirányi, T.[Tamáss], Zerubia, J.B.[Josiane B.],
Multigrid MRF based picture segmentation with cellular neural networks,
CAIP97(345-352).
WWW Version. 9709 BibRef

Sziranyi, T., Czuni, L.,
Picture Segmentation with Introducing an Anisotropic Preliminary Step to an MRF Model with Cellular Neural Networks,
ICPR96(IV: 366-370).
WWW Version. 9608(Hungarian Academy of Sciences, H) BibRef

Wilson, S.P.[Simon P.], Zerubia, J.B.[Josiane B.],
Segmentation of Textured Satellite and Aerial Images by Bayesian Inference,
INRIARR-4336, December 2002.
HTML Version. 0211 BibRef

Morris, R., Descombes, X., and Zerubia, J.B.,
Fully Bayesian Image Segmentation: An Engineering Perspective,
ICIP97(III: 54-57).
WWW Version. 9710 BibRef

Kato, Z.[Zoltan], Pong, T.C.[Ting-Chuen], Lee, J.C.M.[John Chung-Mong],
Color image segmentation and parameter estimation in a markovian framework,
PRL(22), No. 3-4, March 2001, pp. 309-321.
HTML Version. 0105 BibRef

Kato, Z., Pong, T.C.[Ting-Chuen], Qiang, S.G.[Song Guo],
Multicue MRF image segmentation: combining texture and color features,
ICPR02(I: 660-663).
WWW Version. 0211 BibRef

Kato, Z.[Zoltan], Pong, T.C.[Ting-Chuen],
A Markov random field image segmentation model for color textured images,
IVC(24), No. 10, 1 October 2006, pp. 1103-1114.
WWW Version. 0609 BibRef
Earlier:
A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features,
CAIP01(547 ff.).
HTML Version. 0210Segmentation; Color; Texture; Markov random fields; Parameter estimation BibRef

Kato, Z.[Zoltan],
Segmentation of color images via reversible jump MCMC sampling,
IVC(26), No. 3, 3 March 2008, pp. 361-371.
WWW Version. 0801Unsupervised image segmentation; Color; Parameter estimation; Normal mixture identification; Markov random fields; Reversible jump Markov chain Monte Carlo; Simulated annealing BibRef

Bruno, O.M.[Odemir Martinez], da Fontoura Costa, L.[Luciano],
Effective Image Segmentation with Flexible ICM-Based Markov Random Fields in Distributed Systems of Personal Computers,
RealTimeImg(6), No. 4, August 2000, pp. 283-295. 0010 BibRef

Yang, X.[Xiangyu], Liu, J.[Jun],
Unsupervised texture segmentation with one-step mean shift and boundary Markov random fields,
PRL(22), No. 10, August 2001, pp. 1073-1081.
HTML Version. 0108 BibRef

Mukherjee, J.[Jayanta],
MRF clustering for segmentation of color images,
PRL(23), No. 8, June 2002, pp. 917-929.
HTML Version. 0204 BibRef

Feng, X.J.[Xiao-Juan], Williams, C.K.I.[Christopher K.I.], Felderhof, S.N.[Stephen N.],
Combining Belief Networks and Neural Networks for Scene Segmentation,
PAMI(24), No. 4, April 2002, pp. 467-483.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0204 See also Multiscale Random Field Model for Bayesian Image Segmentation, A. TSBN (Tree-Structured Belief Networks). BibRef

Noda, H.[Hideki], Shirazi, M.N.[Mahdad N.], Kawaguchi, E.[Eiji],
MRF-based texture segmentation using wavelet decomposed images,
PR(35), No. 4, April 2002, pp. 771-782.
WWW Version. 0201 BibRef
Earlier:
An MRF Model-Based Method for Unsupervised Textured Image Segmentation,
ICPR96(II: 765-769).
WWW Version. 9608(Kyushu Institute of Technology, J) BibRef

Noda, H.,
Textured Image Segmentation Using MRF in Wavelet Domain,
ICIP00(Vol III: 572-575).
IEEE Abstract. IEEE Top Reference. 0008 BibRef

Shirazi, M.N., Noda, H., Takao, N.,
Texture classification based on Markov modeling in wavelet feature space,
IVC(18), No. 12, September 2000, pp. 967-973.
WWW Version. 0008 BibRef

Shirazi, M.N.[M. Nouri],
Texture Modeling and Classification in Wavelet Feature Space,
ICIP00(Vol I: 272-275).
IEEE Abstract. IEEE Top Reference. 0008 BibRef

Tu, Z.W.[Zhuo-Wen], Zhu, S.C.[Song Chun],
Image Segmentation by Data-Driven Markov Chain Monte Carlo,
PAMI(24), No. 5, May 2002, pp. 657-673.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0205 BibRef
Earlier: Add A3: Shum, H.Y.[Heung-Yeung], ICCV01(II: 131-138).
WWW Version. 0106 BibRef

Zhu, S.C.[Song-Chun], Zhang, R.[Rong], Tu, Z.[Zhuown],
Integrating Bottom-Up/Top-Down for Object Recognition by Data Driven Markov Chain Monte Carlo,
CVPR00(I: 738-745).
IEEE Abstract. IEEE Top Reference.
WWW Version. 0005Detect specific features/objects based on saliency with specific types: Uniform, cluttered (irregular textures), textured, shading (gradient). BibRef

Celeux, G.[Gilles], Forbes, F.[Florence], Peyrard, N.[Nathalie],
EM procedures using mean field-like approximations for Markov model-based image segmentation,
PR(36), No. 1, January 2003, pp. 131-144.
WWW Version. 0210 BibRef

Forbes, F.[Florence], Peyrard, N.[Nathalie],
Hidden markov random field model selection criteria based on mean field-like approximations,
PAMI(25), No. 9, September 2003, pp. 1089-1101.
IEEE Abstract. IEEE Top Reference. 0309Mean field theory leads to tractable computations for computing clusters. Focus on choosing number of classes. Takes spatial info into account. See also Estimating the Dimension of a Model. BibRef

Forbes, F.[Florence], Fort, G.,
Combining Monte Carlo and Mean-Field-Like Methods for Inference in Hidden Markov Random Fields,
IP(16), No. 3, March 2007, pp. 824-837.
WWW Version. 0703 BibRef

Wilson, R., Li, C.T.[Chang-Tsun],
A class of discrete multiresolution random fields and its application to image segmentation,
PAMI(25), No. 1, January 2003, pp. 42-56.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0301 BibRef
Earlier:
Hidden multiresolution random fields and their application to image segmentation,
CIAP99(346-351).
WWW Version. 9909 BibRef

Li, C.T.[Chang-Tsun], Wilson, R.,
Image segmentation based on a multiresolution Bayesian framework,
ICIP98(III: 761-765).
WWW Version. 9810 BibRef

Chen, G.H.[Guo-Huei], Wilson, R.[Roland],
A Multiresolution Random Field Based Model for Image Segmentation,
SCIA01(O-Th3B). 0206 BibRef

Li, C.T.[Chang-Tsun], and Wilson, R.[Roland],
Textured Image Segmentation Using Multiresolution Markov Fields and a Two-Component Texture Model,
SCIA97(xx-yy) 9705
HTML Version. BibRef

Ouadfel, S.[Salima], Batouche, M.[Mohamed],
MRF-based image segmentation using Ant Colony System,
ELCVIA(2), No. 1, August 2003, pp. 12-24.
WWW Version. BibRef 0308
Earlier:
Ant colony system with local search for Markov random field image segmentation,
ICIP03(I: 133-136).
IEEE Abstract. IEEE Top Reference. 0312 BibRef
Earlier:
Unsupervised Image Segmentation Using a Colony of Cooperating Ants,
BMCV02(109 ff.).
HTML Version. 0303Segmentation via a colony of ants. BibRef

Melkemi, K.E.[Kamal E.], Batouche, M.[Mohamed], Foufou, S.[Sebti],
A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics,
PRL(27), No. 11, August 2006, pp. 1230-1238.
WWW Version. Markov random fields; Multiagent systems; Genetic algorithms; Extremal optimization 0606 BibRef

Farag, A.A., Mohamed, R.M., El-Baz, A.S.,
A Unified Framework for MAP Estimation in Remote Sensing Image Segmentation,
GeoRS(43), No. 7, July 2005, pp. 1617-1634.
WWW Version. 0508 BibRef

El-Baz, A.S., Farag, A.A.,
Image Segmentation Using GMRF Models: Parameters Estimation and Applications,
ICIP03(II: 177-180).
IEEE Abstract. IEEE Top Reference. 0312 BibRef

El-Baz, A.S.[Ayman S.], Farag, A.A.[Aly A.], Gimel'farb, G.[Georgy],
Iterative Approximation of Empirical Grey-Level Distributions for Precise Segmentation of Multimodal Images,
JASP(2005), No. 13, 2005, pp. 1969-1983.
WWW Version. 0603 BibRef
Earlier: A2, A1, A3:
Precise Image Segmentation by Iterative EM-Based Approximation of Empirical Grey Level Distributions with Linear Combinations of Gaussians,
LCV04(109).
WWW Version. 0406 BibRef

Farag, A.A.[Aly A.], El-Baz, A.S.[Ayman S.], Gimel'farb, G.[Georgy],
Precise segmentation of multimodal images,
IP(15), No. 4, April 2006, pp. 952-968.
WWW Version. 0604 BibRef

El-Baz, A.S.[Ayman S.], Gimel'farb, G.[Georgy],
Image segmentation with a parametric deformable model using shape and appearance priors,
CVPR08(1-8).
WWW Version. 0806 BibRef

El-Baz, A.S.[Ayman S.], Gimel'farb, G.[Georgy],
EM Based Approximation of Empirical Distributions with Linear Combinations of Discrete Gaussians,
ICIP07(IV: 373-376).
WWW Version. 0709 BibRef

El-Baz, A.S., Farag, A.A., Gimel'farb, G.,
Stochastic Deformable Model,
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Meier, T., Ngan, K.N., and Crebbin, G.,
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ICIP97(I: 216-219).
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Wilinski, P.[Piotr], Solaiman, B., Hillion, A., Czarnecki, W.,
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Gunsel, B., Panayirci, E.,
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Azencott, R., Graffigne, C.,
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Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Fractal Texture Segmentation .