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.
Elsevier DOI Extend to multispectral, multi region. BibRef 8210

Derin, H.[Haluk], Cole, W.S.[William S.],
Segmentation of Textured Images Using Gibbs Random Fields,
CVGIP(35), No. 1, July 1986, pp. 72-98.
Elsevier DOI 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 DOI 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.
IEEE DOI 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 DOI BibRef 9105
And:
A Computational Approach to Boundary Detection,
CVPR91(358-363).
IEEE DOI 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 File. 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.
Elsevier DOI BibRef 9604

Krishnamachari, S., Chellappa, R.,
Multiresolution Gauss-Markov Random-Field Models for Texture Segmentation,
IP(6), No. 2, February 1997, pp. 251-267.
IEEE DOI 9703
BibRef
Earlier:
GMRF models and wavelet decomposition for texture segmentation,
ICIP95(III: 568-571).
IEEE DOI 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.
IEEE DOI 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).
IEEE DOI 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 DOI 0312
BibRef
Earlier:
Modeling object classes in aerial images using hidden Markov models,
ICIP02(I: 860-863).
IEEE DOI 0210
BibRef

Geiger, D., and Yuille, A.L.,
A Common Framework for Image Segmentation,
IJCV(6), No. 3, August 1991, pp. 227-243.
Springer DOI BibRef 9108
Earlier: ICPR90(I: 502-507).
IEEE DOI 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 DOI 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.[Chung-Lin], Cheng, T.Y.[Tai-Yuen], Chen, C.C.[Chaur-Chin],
Color Images' Segmentation Using Scale Space Filter and Markov Random Field,
PR(25), No. 10, October 1992, pp. 1217-1229.
Elsevier DOI BibRef 9210

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

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

Kim, I.Y.[Il Y.], Yang, H.S.[Hyun 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 DOI 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.
IEEE DOI 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).
IEEE DOI 9411
BibRef

Hussain, I., Reed, T.R.,
A Bond Percolation Based Model for Image Segmentation,
IP(6), No. 12, December 1997, pp. 1698-1704.
IEEE DOI 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 DOI 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.[Philippe], Tarroux, P.[Philippe],
Unsupervised Image Segmentation Using A Distributed Genetic Algorithm,
PR(27), No. 5, May 1994, pp. 659-673.
Elsevier DOI Segmentation, Learning. Genetic Algorithms. BibRef 9405

Andrey, P.[Philippe], Tarroux, P.[Philippe],
Unsupervised Segmentation of Markov Random-Field Modeled Textured Images Using Selectionist Relaxation,
PAMI(20), No. 3, March 1998, pp. 252-262.
IEEE DOI 9805
BibRef
Earlier:
Unsupervised Texture Segmentation Using Selectionist Relaxation,
ECCV96(I:482-491).
Springer DOI 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).
IEEE DOI BibRef
Earlier:
Information Fusion in a Markov Random Field Based Image Segmentation Approach Using Adaptive Neighbourhoods,
ICPR96(II: 570-575).
IEEE DOI 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).
Springer DOI 9709
BibRef

Dellepiane, S.G., Fontana, F., Vernazza, G.,
A robust non-iterative method for image labelling using context,
ICIP94(II: 207-211).
IEEE DOI 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.
IEEE DOI 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 DOI 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 DOI 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.
IEEE DOI 0310
BibRef
And:
Sequential Bayesian segmentation of remote sensing images,
ICIP03(III: 985-988).
IEEE DOI 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.
IEEE DOI 0508
BibRef
Earlier:
Segmentation of remote-sensing images by supervised TS-MRF,
ICIP04(III: 1867-1870).
IEEE DOI 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., Scarpa, G.[Giuseppe], Poggi, G.[Giovanni],
Hierarchical Texture-Based Segmentation of Multiresolution Remote-Sensing Images,
GeoRS(47), No. 7, July 2009, pp. 2129-2141.
IEEE DOI 0906
BibRef
Earlier: A1, A3, A2:
Hierarchical Mrf-Based Segmentation of Remote-Sensing Images,
ICIP06(1121-1124).
IEEE DOI 0610
BibRef

Gaetano, R., Masi, G., Poggi, G., Verdoliva, L., Scarpa, G.,
Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images,
GeoRS(53), No. 6, June 2015, pp. 2987-3004.
IEEE DOI 1503
feature extraction BibRef

Scarpa, G.[Giuseppe], Masi, G.[Giuseppe],
Dynamic Hierarchical Segmentation of Remote Sensing Images,
CIAP13(I:371-380).
Springer DOI 1311
BibRef

Gaetano, R.[Raffaele], Scarpa, G.[Giuseppe], Sziranyi, T.[Tamas],
Graph-based Analysis of Textured Images for Hierarchical Segmentation,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Scarpa, G.[Giuseppe], Gaetano, R., Haindl, M.[Michal], Zerubia, J.B.[Josiane B.],
Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation,
IP(18), No. 8, August 2009, pp. 1830-1843.
IEEE DOI 0907
BibRef

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

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

Haindl, M.[Michal], Mikeš, S.[Stanislav],
A competition in unsupervised color image segmentation,
PR(57), No. 1, 2016, pp. 136-151.
Elsevier DOI 1605
BibRef
Earlier:
Unsupervised Dynamic Textures Segmentation,
CAIP13(433-440).
Springer DOI 1308
BibRef
Earlier:
Unsupervised Texture Segmentation Using Multispectral Modelling Approach,
ICPR06(II: 203-206).
IEEE DOI 0609
BibRef
Earlier:
Model-Based Texture Segmentation,
ICIAR04(II: 306-313).
Springer DOI 0409
Unsupervised image segmentation BibRef

Haindl, M.,
Recursive Square-root Filters,
ICPR00(Vol II: 1014-1017).
IEEE DOI 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.
Elsevier DOI 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.
DOI Link 0001
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.
IEEE DOI 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

DOI Link
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.
Elsevier DOI 0005
BibRef

Collet, C.[Christophe], Thourel, P., Perez, P., Bouthemy, P.,
Hierarchical MRF Modeling for Sonar Picture Segmentation,
ICIP96(III: 979-982).
IEEE DOI 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.
Elsevier DOI 0002
MCMC. 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 DOI 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.
IEEE DOI 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).
Springer DOI 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).
IEEE DOI 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).
IEEE DOI 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.
Elsevier DOI 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).
IEEE DOI 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.
Elsevier DOI 0609
BibRef
Earlier:
A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features,
CAIP01(547 ff.).
Springer DOI 0210
Segmentation; Color; Texture; Markov random fields; Parameter estimation BibRef

Kato, Z.[Zoltan], Pong, T.C.[Ting-Chuen],
A Multi-Layer MRF Model for Video Object Segmentation,
ACCV06(II:953-962).
Springer DOI 0601

See also Detection of Object Motion Regions in Aerial Image Pairs With a Multilayer Markovian Model. BibRef

Kato, Z.[Zoltan],
Segmentation of color images via reversible jump MCMC sampling,
IVC(26), No. 3, 3 March 2008, pp. 361-371.
Elsevier DOI 0801
BibRef
Earlier:
Reversible Jump Markov Chain Monte Carlo for Unsupervised MRF Color Image Segmentation,
BMVC04(xx-yy).
HTML Version. 0508
Unsupervised image segmentation; Color; Parameter estimation; Normal mixture identification; Markov random fields; Reversible jump Markov chain Monte Carlo; Simulated annealing BibRef

Kato, Z., Pong, T.C.[Ting-Chuen], Qiang, S.G.[Song Guo],
Unsupervised segmentation of color textured images using a multi-layer MRF model,
ICIP03(I: 961-964).
IEEE DOI 0312
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.Y.[Xiang-Yu], 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.
Elsevier DOI 0108
BibRef

Mukherjee, J.[Jayanta],
MRF clustering for segmentation of color images,
PRL(23), No. 8, June 2002, pp. 917-929.
Elsevier DOI 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 DOI 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.
Elsevier DOI 0201
BibRef
Earlier:
An MRF Model-Based Method for Unsupervised Textured Image Segmentation,
ICPR96(II: 765-769).
IEEE DOI 9608
(Kyushu Institute of Technology, J) BibRef

Noda, H.,
Textured Image Segmentation Using MRF in Wavelet Domain,
ICIP00(Vol III: 572-575).
IEEE DOI 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.
Elsevier DOI 0008
Wavelets for different scales. BibRef

Shirazi, M.N.[M. Nouri],
Texture Modeling and Classification in Wavelet Feature Space,
ICIP00(Vol I: 272-275).
IEEE DOI 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 DOI 0205
BibRef
Earlier: Add A3: Shum, H.Y.[Heung-Yeung], ICCV01(II: 131-138).
IEEE DOI 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 DOI 0005
Detect 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.
Elsevier DOI 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. 0309
Mean 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.
IEEE DOI 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 DOI 0301
BibRef
Earlier:
Hidden multiresolution random fields and their application to image segmentation,
CIAP99(346-351).
IEEE DOI 9909
BibRef

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

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

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

Ouadfel, S.[Salima], Batouche, M.[Mohamed],
MRF-based image segmentation using Ant Colony System,
ELCVIA(2), No. 1, August 2003, pp. 12-24.
DOI Link BibRef 0308
Earlier:
Ant colony system with local search for Markov random field image segmentation,
ICIP03(I: 133-136).
IEEE DOI 0312
BibRef
Earlier:
Unsupervised Image Segmentation Using a Colony of Cooperating Ants,
BMCV02(109 ff.).
Springer DOI 0303
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A Segmentation Method Using Compound Markov Random Fields Based on a General Boundary Model,
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Karadag, Ö.Ö.[Özge Öztimur], Vural, F.T.Y.[Fatos T. Yarman],
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Elsevier DOI 1407
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MRF Based Image Segmentation Augmented with Domain Specific Information,
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Springer DOI 1309
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Karadag, O.O.[Ozge Oztimur], Vural, F.T.Y.[Fatos T. Yarman],
Fusion of Image Segmentations under Markov, Random Fields,
ICPR14(930-935)
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Wang, X.[Xili], Zhang, W.[Wei], Ji, Q.A.[Qi-Ang],
Image object extraction with shape and edge-driven Markov random field model,
IET-IPR(8), No. 7, July 2014, pp. 383-396.
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Markov processes BibRef

Min, C.B.[Chao-Bo], Zhang, J.J.[Jun-Ju], Chang, B.K.[Beng-Kang], Sun, B.[Bin], Li, Y.J.[Ying-Jie],
Unsupervised evaluation method using Markov random field for moving object segmentation in infrared videos,
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Wang, F.[Fan], Wu, Y.[Yan], Fan, J.W.[Jian-Wei], Zhang, X.[Xue], Zhang, Q.A.[Qi-Ang], Li, M.[Ming],
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SAR image; Multi-class segmentation; Triplet Markov random field (TMF); New energy function; Edge penalty; Multi-region merging BibRef

Song, W.Y.[Wan-Ying], Li, M.[Ming], Zhang, P.[Peng], Wu, Y.[Yan],
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IEEE DOI 1907
Scattering, Data models, Kernel, Sea surface, Analytical models, Solid modeling, Clustering algorithms, Classification, triplet discriminative random fields (TDF) BibRef

Wang, F., Wu, Y., Zhang, Q., Zhao, W., Li, M., Liao, G.,
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Gan, L., Wu, Y., Liu, M., Zhang, P., Ji, H., Wang, F.,
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SAR image; Multi-class segmentation; Fuzzy triplet Markov field (FTMF); Fuzzy clustering; Fuzzy objective function; Fuzzy iterative conditional estimation BibRef

Wang, F.[Fan], Huang, Q.X.[Qi-Xing], Ovsjanikov, M.[Maks], Guibas, L.J.[Leonidas J.],
Unsupervised Multi-class Joint Image Segmentation,
CVPR14(3142-3149)
IEEE DOI 1409
Functional Maps;Image Segmentation;Multi-Class BibRef

Wang, F.[Fan], Huang, Q.X.[Qi-Xing], Guibas, L.J.[Leonidas J.],
Image Co-segmentation via Consistent Functional Maps,
ICCV13(849-856)
IEEE DOI 1403
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A Robust Scaling Approach for Implementation of HsMMs,
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Scene Parsing From an MAP Perspective,
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Golipour, M., Ghassemian, H., Mirzapour, F.,
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Springer DOI 1605
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Incorporating higher-order point distribution model priors into MRFs using convex quadratic programming,
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Liu, K.W.[Kang-Wei], Zhang, J.[Junge], Yang, P.P.[Pei-Pei], Maybank, S.J.[Stephen J.], Huang, K.Q.[Kai-Qi],
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Springer DOI 1702
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Earlier: A1, A2, A3, A5, Only:
GRSA: Generalized range swap algorithm for the efficient optimization of MRFs,
CVPR15(1761-1769)
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IET-CV(11), No. 8, December 2017, pp. 717-724.
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Elsevier DOI 1812
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MCMC based sampling technique for robust multi-model fitting and visual data segmentation,
IPTA16(1-6)
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A Spatially Constrained Probabilistic Model for Robust Image Segmentation,
IP(29), 2020, pp. 4898-4910.
IEEE DOI 2003
Image segmentation, Hidden Markov models, Brain modeling, Probabilistic logic, Estimation, Labeling, Robustness, Segmentation, class label distribution BibRef

Berman, M.[Maxim], Blaschko, M.B.[Matthew B.],
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Springer DOI 2006
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Zheng, C.[Chen], Zhang, Y.[Yun], Wang, L.G.[Lei-Guang],
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IEEE DOI 2112
Semantics, Image segmentation, Remote sensing, Feature extraction, Biological system modeling, Spatial resolution, semantic BibRef

Kazantzidis, I., Florez-Revuelta, F., Nebel, J.,
Profile Hidden Markov Models for Foreground Object Modelling,
ICIP18(1628-1632)
IEEE DOI 1809
Hidden Markov models, Videos, Cameras, Biological system modeling, Image segmentation, Visualization, Labeling, Vide-omics BibRef

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Markovian Segmentation of Textured Color Images,
ISCV20(1-5)
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ISCV18(1-8)
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Truncated Max-of-Convex Models,
CVPR17(664-672)
IEEE DOI 1711
A special case of pair-wise random fields. Approximation algorithms, Computational modeling, Labeling, Optimization, Random variables, Robustness BibRef

Toya, Y., Kudo, H.,
An MRF-based image segmentation with unsupervised model parameter estimation,
MVA17(432-435)
DOI Link 1708
Density measurement, Image segmentation, Minimization, Optimization, Organizations, Smoothing methods, TV BibRef

Zhang, Z.Y.[Zi-Yu], Fidler, S.[Sanja], Urtasun, R.[Raquel],
Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs,
CVPR16(669-677)
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MRF model for consistent labelling BibRef

Su, X., Rizkallah, M., Maugey, T., Guillemot, C.,
Graph-based light fields representation and coding using geometry information,
ICIP17(4023-4027)
IEEE DOI 1803
Cameras, Geometry, Image coding, Image color analysis, Image edge detection, Image segmentation, Light fields (LF) BibRef

Hog, M.[Matthieu], Sabater, N.[Neus], Guillemot, C.[Christine],
Light Field Segmentation Using a Ray-Based Graph Structure,
ECCV16(VII: 35-50).
Springer DOI 1611
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Wu, Z.R.[Zhi-Rong], Lin, D.[Dahua], Tang, X.[Xiaoou],
Deep Markov Random Field for Image Modeling,
ECCV16(VIII: 295-312).
Springer DOI 1611
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Perciano, T., Ushizima, D.M., Bethel, E.W., Mizrahi, Y.D., Parkinson, D., Sethian, J.A.,
Reduced-complexity image segmentation under parallel Markov Random Field formulation using graph partitioning,
ICIP16(1259-1263)
IEEE DOI 1610
Algorithm design and analysis BibRef

Ameur, M., Idrissi, N., Daoui, C.,
Markovian Segmentation of Color and Gray Level Images,
CGiV16(259-264)
IEEE DOI 1608
expectation-maximisation algorithm BibRef

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Saito, M.[Masaki], Okatani, T.[Takayuki],
Transformation of Markov Random Fields for marginal distribution estimation,
CVPR15(797-805)
IEEE DOI 1510
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Memory Efficient Max Flow for Multi-Label Submodular MRFs,
PAMI(41), No. 4, April 2019, pp. 886-900.
IEEE DOI 1903
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Earlier: CVPR16(5867-5876)
IEEE DOI 1612
Standards, Memory management, Approximation algorithms, Encoding, Image edge detection, Heuristic algorithms, Random variables, graphical models BibRef

Ajanthan, T.[Thalaiyasingam], Hartley, R.I.[Richard I.], Salzmann, M.[Mathieu], Li, H.D.[Hong-Dong],
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CVPR15(5144-5152)
IEEE DOI 1510
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Closed-Form Approximate CRF Training for Scalable Image Segmentation,
ECCV14(III: 550-565).
Springer DOI 1408
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Márquez-Neila, P.[Pablo], Kohli, P.[Pushmeet], Rother, C.[Carsten], Baumela, L.[Luis],
Non-parametric Higher-Order Random Fields for Image Segmentation,
ECCV14(VI: 269-284).
Springer DOI 1408
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Amid, E.[Ehsan],
Bayesian Non-parametric Image Segmentation with Markov Random Field Prior,
SCIA13(76-84).
Springer DOI 1311
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Perceptually Inspired Layout-Aware Losses for Image Segmentation,
ECCV14(II: 663-678).
Springer DOI 1408
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Kohli, P.[Pushmeet], Osokin, A.[Anton], Jegelka, S.[Stefanie],
A Principled Deep Random Field Model for Image Segmentation,
CVPR13(1971-1978)
IEEE DOI 1309
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Kae, A.[Andrew], Marlin, B.M.[Benjamin M>], Learned-Miller, E.G.[Erik G.],
The Shape-Time Random Field for Semantic Video Labeling,
CVPR14(272-279)
IEEE DOI 1409
CRF; RBM; deep learning; deep model; faces; image labeling BibRef

Kae, A.[Andrew], Sohn, K.[Kihyuk], Lee, H.L.[Hong-Lak], Learned-Miller, E.G.[Erik G.],
Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling,
CVPR13(2019-2026)
IEEE DOI 1309
attributes; deep learning; face processing; segmentation BibRef

Nakamura, T.[Takuma], Harada, T.[Tatsuhiro], Suzuki, T.[Tomohiko], Matsumoto, T.[Takashi],
HDP-MRF: A hierarchical Nonparametric model for image segmentation,
ICPR12(2254-2257).
WWW Link. 1302
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Zhou, L.[Lei], Qiao, Y.[Yu], Yang, J.[Jie], He, X.J.[Xiang-Jian],
Learning geodesic CRF model for image segmentation,
ICIP12(1565-1568).
IEEE DOI 1302
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Yadollahpour, P.[Payman], Batra, D.[Dhruv], Shakhnarovich, G.[Gregory],
Discriminative Re-ranking of Diverse Segmentations,
CVPR13(1923-1930)
IEEE DOI 1309
M-best BibRef

Batra, D.[Dhruv], Yadollahpour, P.[Payman], Guzman-Rivera, A.[Abner], Shakhnarovich, G.[Gregory],
Diverse M-Best Solutions in Markov Random Fields,
ECCV12(V: 1-16).
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Majeed, T.[Tahir], Fundana, K.[Ketut], Luthi, M.[Marcel], Kiriyanthan, S.[Silja], Beinemann, J.[Jorg], Cattin, P.C.[Philippe C.],
Using a flexibility constrained 3D statistical shape model for robust MRF-based segmentation,
MMBIA12(57-64).
IEEE DOI 1203
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Feng, H.[Hao], Jiang, Z.G.[Zhi-Guo],
Image segmentation with hierarchical topic assignment,
ICIP11(2125-2128).
IEEE DOI 1201
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Sun, L.[Liye], Wu, K.Z.[Kan-Zhi],
Tree-Structured MRF Based Image Segmentation Combined with Advanced Means Shift Mode Detection,
ICIG11(228-233).
IEEE DOI 1109
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Chen, C.[Chao], Freedman, D.[Daniel], Lampert, C.H.[Christoph H.],
Enforcing topological constraints in random field image segmentation,
CVPR11(2089-2096).
IEEE DOI 1106
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The Divide and Segment Method for Parallel Image Segmentation,
ACIVS13(504-515).
Springer DOI 1311
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Korting, T.S.[Thales Sehn], Garcia Fonseca, L.M.[Leila Maria], Câmara, G.[Gilberto],
A Geographical Approach to Self-Organizing Maps Algorithm Applied to Image Segmentation,
ACIVS11(162-170).
Springer DOI 1108
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Paiva, A.R.C.[Antonio R.C.], Jurrus, E.[Elizabeth], Tasdizen, T.[Tolga],
Using Sequential Context for Image Analysis,
ICPR10(2800-2803).
IEEE DOI 1008
Fast inference for MRF image analysis. BibRef

Zhao, B.[Bin], Fei-Fei, L.[Li], Xing, E.P.[Eric P.],
Image Segmentation with Topic Random Field,
ECCV10(V: 785-798).
Springer DOI 1009
To enforce spatial constraints. BibRef

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Bayesian image segmentation with mean shift,
ICIP09(2405-2408).
IEEE DOI 0911
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Flach, B.[Boris], Sixta, T.[Tomas],
Unsupervised (parameter) learning for MRFs on bipartite graphs,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Flach, B.[Boris], Schlesinger, D.[Dmitrij],
Modelling composite shapes by Gibbs random fields,
CVPR11(2177-2182).
IEEE DOI 1106
BibRef
Earlier:
Combining Shape Priors and MRF-Segmentation,
SSPR08(177-186).
Springer DOI 0812
BibRef

Aoki, K.[Kohta], Nagahashi, H.[Hiroshi],
Bayesian Image Segmentation Using MRF's Combined with Hierarchical Prior Models,
SCIA05(65-74).
Springer DOI 0506
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Kluszczynski, R.[Rafa], Lieshout, M.C.[Marie-Colette], Schreiber, T.[Tomasz],
An Algorithm for Binary Image Segmentation Using Polygonal Markov Fields,
CIAP05(383-390).
Springer DOI 0509
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Sha, Y.H.[Yu-Heng], Cong, L.[Lin], Sun, Q.A.[Qi-Ang], Jiao, L.C.[Li-Cheng],
Unsupervised Image Segmentation Using Contourlet Domain Hidden Markov Trees Model,
ICIAR05(32-39).
Springer DOI 0509
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Sun, Q.A.[Qi-Ang], Gou, S.P.[Shui-Ping], Jiao, L.C.[Li-Cheng],
A New Approach to Unsupervised Image Segmentation Based on Wavelet-Domain Hidden Markov Tree Models,
ICIAR04(I: 41-48).
Springer DOI 0409
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Kim, D.H.[Dong Hwan], Yun, I.D.[Il Dong], Lee, S.U.[Sang Uk],
New MRF Parameter Estimation Technique for Texture Image Segmentation using Hierarchical GMRF Model Based on Random Spatial Interaction and Mean Field Theory,
ICPR06(II: 365-368).
IEEE DOI 0609
BibRef

Kim, J.H.[Jeong Hee], Yun, I.D.[Ii Dong], Lee, S.U.[Sang Uk],
Unsupervised segmentation of textured image using Markov random field in random spatial interaction,
ICIP98(III: 756-760).
IEEE DOI 9810
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Mohammad-Djafari, A.[Ali], Bali, N.[Nadia], Mohammadpour, A.[Adel],
Hierarchical Markovian Models for Hyperspectral Image Segmentation,
IWICPAS06(416-424).
Springer DOI 0608
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A Unified Model of GMRF and MOG for Image Segmentation,
ICIP05(III: 1140-1143).
IEEE DOI 0512
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Image segmentation by convex quadratic programming,
ICPR08(1-5).
IEEE DOI 0812
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Rivera, M.[Mariano], Gee, J.C.[James C.],
Two-level MRF Models for Image Restoration and Segmentation,
BMVC04(xx-yy).
HTML Version. 0508
BibRef
Earlier:
Image Segmentation by Flexible Models Based on Robust Regularized Networks,
ECCV02(III: 621 ff.).
Springer DOI 0205
BibRef

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Context-dependent tree-structured image classification using the QDA distortion measure and the hidden markov model,
ICIP04(III: 1887-1890).
IEEE DOI 0505
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Kostiainen, T., Lampinen, J.,
Efficient proposal distributions for MCMC image segmentation,
ICIP04(II: 933-936).
IEEE DOI 0505
Bayesian reversible jump Markov chain Monte Carlo. Segmentation BibRef

Wilson, S., Stefanou, G.,
Image Segmentation Using the Double Markov Random Field, with Application to Land Use Estimation,
ICIP01(I: 742-745).
IEEE DOI 0108
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Unsupervised progressive parsing of Poisson fields using minimum description length criteria,
ICIP99(II:26-30).
IEEE DOI 0411
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Nowak, R.D.[Robert D.],
Multiscale Hidden Markov Models for Bayesian Image Analysis,
ICIP99(26AS1). Not in proceedings. BibRef 9900

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Unsupervised Texture Segmentation Based on Histogram of Encoded Gabor Features and MRF Model,
ICIP99(III:208-211).
IEEE DOI BibRef 9900

Yalabik, N.[Nese], Yalabik, C.[Cemal], Goktepe, M.[Mesut], Atalay, V.[Volkan],
Unsupervised Texture Based Image Segmentation by Simulated Annealing Using Markov Random Field and Potts Models,
ICPR98(Vol I: 820-822).
IEEE DOI 9808
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Goktepe, M., Yalabik, N., Atalay, V.,
Unsupervised Segmentation of Gray Level Markov Model Textures with Hierarchical Self Organizing Maps,
ICPR96(IV: 90-94).
IEEE DOI 9608
(Middle East Technical Univ., TR) BibRef

Meier, T., Ngan, K.N., and Crebbin, G.,
A Robust Markovian Segmentation Based on Highest Confidence First (HCF),
ICIP97(I: 216-219).
IEEE DOI BibRef 9700

Wilinski, P.[Piotr], Solaiman, B., Hillion, A., Czarnecki, W.,
A Multiresolution Hybrid Neuro-Markovian Image Modeling and Segmentation,
ICIP96(III: 951-954).
IEEE DOI BibRef 9600

Gunsel, B., Panayirci, E.,
Segmentation of range and intensity images using multiscale Markov random field representations,
ICIP94(II: 187-191).
IEEE DOI 9411
BibRef

Azencott, R., Graffigne, C.,
Non-supervised segmentation using multi-level Markov random fields,
ICPR92(III:201-204).
IEEE DOI 9208
BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Fractal Texture Segmentation .