8.8.3 MRF Models for Segmentation

Chapter Contents (Back)
Markov Random Field. MRF. Segmentation, Texture. Segmentation, MRF. See also Markov Random Field Models.

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CGIP(20), No. 2, October 1982, pp. 101-132.
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Derin, H., Cole, W.S.,
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Derin, H.[Haluk], and Elliott, H.,
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Won, C.S.[Chee Sun], Derin, H.[Haluk],
Unsupervised Segmentation of Noisy and Textured Images Using Markov Random Fields,
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Derin, H., Elliott, H., Cristi, R., and Geman, D.,
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PAMI(6), No. 6, November 1984, pp. 707-720. Neighborhoods. BibRef 8411

Bouman, C., and Liu, B.,
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PAMI(13), No. 2, February 1991, pp. 99-113.
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Bouman, C., and Shapiro, M.,
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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.
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Manjunath, B.S., Shekhar, C., Chellappa, R.,
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PR(29), No. 4, April 1996, pp. 627-640.
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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., 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 Link. 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 Link. 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., 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.
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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 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
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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
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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., Tarroux, P.,
Unsupervised Image Segmentation Using A Distributed Genetic Algorithm,
PR(27), No. 5, May 1994, pp. 659-673.
WWW Link. 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 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
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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
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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
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Dellepiane, S.G., Fontana, F., Vernazza, G.,
A robust non-iterative method for image labelling using context,
ICIP94(II: 207-211).
IEEE DOI 9411
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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
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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
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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
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And:
Sequential Bayesian segmentation of remote sensing images,
ICIP03(III: 985-988).
IEEE DOI 0312
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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
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Earlier:
Segmentation of remote-sensing images by supervised TS-MRF,
ICIP04(III: 1867-1870).
IEEE DOI 0505
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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
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Earlier: A1, A3, A2:
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IEEE DOI 0610
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Gaetano, R., Masi, G., Poggi, G., Verdoliva, L., Scarpa, G.,
Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images,
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feature extraction BibRef

Scarpa, G.[Giuseppe], Masi, G.[Giuseppe],
Dynamic Hierarchical Segmentation of Remote Sensing Images,
CIAP13(I:371-380).
Springer DOI 1311
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Gaetano, R.[Raffaele], Scarpa, G.[Giuseppe], Sziranyi, T.[Tamas],
Graph-based Analysis of Textured Images for Hierarchical Segmentation,
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Scarpa, G.[Giuseppe], Gaetano, R., Haindl, M.[Michal], Zerubia, J.B.[Josiane B.],
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IP(18), No. 8, August 2009, pp. 1830-1843.
IEEE DOI 0907
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Scarpa, G.[Giuseppe], Haindl, M.[Michal], Zerubia, J.B.[Josiane B.],
A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images,
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Scarpa, G.[Giuseppe], Haindl, M.[Michal],
Unsupervised Texture Segmentation by Spectral-Spatial-Independent Clustering,
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IEEE DOI 0609
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Haindl, M.[Michal], Mikeš, S.[Stanislav],
A competition in unsupervised color image segmentation,
PR(57), No. 1, 2016, pp. 136-151.
Elsevier DOI 1605
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Earlier:
Unsupervised Dynamic Textures Segmentation,
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Springer DOI 1308
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Earlier:
Unsupervised Texture Segmentation Using Multispectral Modelling Approach,
ICPR06(II: 203-206).
IEEE DOI 0609
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ICIAR04(II: 306-313).
Springer DOI 0409
Unsupervised image segmentation BibRef

Haindl, M.,
Recursive Square-root Filters,
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IEEE DOI 0009
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Haindl, M.,
Texture Segmentation Using Recursive Markov Random Field Parameter Estimation,
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Aas, K.[Kjersti], Eikvil, L.[Line], Huseby, R.B.[Ragnar Bang],
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Dong, Y., Forester, B.C., Milne, A.K.,
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Kim, H.J., Kim, E.Y., Kim, J.W., Park, S.H.,
MRF Model Based Image Segmentation Using Hierarchical Distributed Genetic Algorithm,
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Kim, E.Y., Park, S.H., Kim, H.J.,
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IEEE Top Reference. 0010
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Mignotte, M., Collet, C., Pérez, P., Bouthemy, P.,
Three-Class Markovian Segmentation of High-Resolution Sonar Images,
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Mignotte, M., Collet, C., Perez, P., Bouthemy, P.,
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Lemoyne, J., Collet, C.,
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Mignotte, M., Collet, C., Pérez, P., Bouthemy, P.,
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Yao, K.C., Mignotte, M., Collet, C., Galerne, P., Burel, G.,
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Collet, C.[Christophe], Thourel, P., Perez, P., Bouthemy, P.,
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Wang, L.[Lei], Liu, J.[Jun],
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Sarkar, A., Biswas, M.K., Sharma, K.M.S.,
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Sarkar, A., Biswas, M.K., Kartikeyan, B., Kumar, V., Majumder, K.L., Pal, D.K.,
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Hazel, G.G.,
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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,
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Czúni, L.[László], Szirányi, T.[Tamáss], Zerubia, J.B.[Josiane B.],
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Sziranyi, T., Czuni, L.,
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Kato, Z., Pong, T.C.[Ting-Chuen], Qiang, S.G.[Song Guo],
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Kato, Z.[Zoltan], Pong, T.C.[Ting-Chuen],
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Segmentation; Color; Texture; Markov random fields; Parameter estimation BibRef

Kato, Z.[Zoltan], Pong, T.C.[Ting-Chuen],
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Kato, Z.[Zoltan],
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Kato, Z., Pong, T.C.[Ting-Chuen], Qiang, S.G.[Song Guo],
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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,
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Yang, X.Y.[Xiang-Yu], Liu, J.[Jun],
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Mukherjee, J.[Jayanta],
MRF clustering for segmentation of color images,
PRL(23), No. 8, June 2002, pp. 917-929.
Elsevier DOI 0204
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Feng, X.J.[Xiao-Juan], Williams, C.K.I.[Christopher K.I.], Felderhof, S.N.[Stephen N.],
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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.
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An MRF Model-Based Method for Unsupervised Textured Image Segmentation,
ICPR96(II: 765-769).
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Noda, H.,
Textured Image Segmentation Using MRF in Wavelet Domain,
ICIP00(Vol III: 572-575).
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Shirazi, M.N., Noda, H., Takao, N.,
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Image Segmentation by Data-Driven Markov Chain Monte Carlo,
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IEEE DOI 0205
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IEEE DOI 0106
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Zhu, S.C.[Song-Chun], Zhang, R.[Rong], Tu, Z.[Zhuown],
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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],
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Hidden markov random field model selection criteria based on mean field-like approximations,
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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
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Wilson, R., Li, C.T.[Chang-Tsun],
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Hidden multiresolution random fields and their application to image segmentation,
CIAP99(346-351).
IEEE DOI 9909
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Li, C.T.[Chang-Tsun], Wilson, R.,
Image segmentation based on a multiresolution Bayesian framework,
ICIP98(III: 761-765).
IEEE DOI 9810
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Chen, G.H.[Guo-Huei], Wilson, R.G.[Roland G.],
A Multiresolution Random Field Based Model for Image Segmentation,
SCIA01(O-Th3B). 0206
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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
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Ouadfel, S.[Salima], Batouche, M.[Mohamed],
MRF-based image segmentation using Ant Colony System,
ELCVIA(2), No. 1, August 2003, pp. 12-24.
WWW 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
Segmentation via a colony of ants. BibRef

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Springer DOI 0812
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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
BibRef

Nowak, R.D., Figueiredo, M.A.T.[Mário A.T.],
Unsupervised progressive parsing of Poisson fields using minimum description length criteria,
ICIP99(II:26-30).
IEEE DOI 0411
BibRef

Nowak, R.D.[Robert D.],
Multiscale Hidden Markov Models for Bayesian Image Analysis,
ICIP99(26AS1). Not in proceedings. BibRef 9900

Pok, G.C.[Gou-Chol], Liu, J.C.[Jyh-Charn],
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
BibRef

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 .


Last update:May 25, 2017 at 22:18:08