7.10.3 Markov Random Field Models

Chellappa, R., Jain, A.K., (Eds.)
Markov Random Fields: Theory and Applications,
Academic Press1993. BibRef 9300

Bunke, H., Marti, U.V.,
Hidden Markov models: Applications in Computer Vision,
ACM Press2001, ISBN: 981-02-4564-5.
WWW Version. BibRef 0100

Woods, J.W.,
Two dimensional Discrete Markov Random Fields,
IT(18), 1972, pp. 232-240. BibRef 7200

Woods, J.W.,
Markov Image Modeling,
AC(23), October 1978, pp. 846-850. BibRef 7810

Cross, G.R., and Jain, A.K.,
Markov Random Field Texture Models,
PAMI(5), No. 1, January 1983, pp. 25-39. BibRef 8301
And:
Measures of Homogeneity in Texture,
CVPR83(211-216). BibRef

Hassner, M., and Sklansky, J.,
The Use of Markov Random Fields as Models of Texture,
CGIP(12), 1980, pp. 357-370. BibRef 8000
Earlier:
Markov Random Field Models of Digitized Image Texture,
ICPR78(538-540). BibRef
Earlier:
Markov Random Fields as Models of Digitized Image Texture,
BibRef

Kanal, L.N.,
Markov Mesh Models,
CGIP(12), 1980, pp. 371-375. BibRef 8000

Kashyap, R.L.,
Random Field Models of Images,
CGIP(12), No. 3, March 1980, pp. 257-270. BibRef 8003

Chellappa, R., Kashyap, R.L.,
Digital Image Restoration Using Spatial Interaction Models,
ASSP(30), June 1982, pp. 461-472. BibRef 8206

Kashyap, R.L., and Chellappa, R.,
Estimation and Choice of Neighbors in Spatial Interaction Models of Images,
IT(29), No. 1, January 1983, pp. 60-72. BibRef 8301

Kashyap, R.L., and Chellappa, R.,
Stochastic Models for Closed Boundary Analysis, Representation, and Construction,
IT(27), September 1981, pp. 627-637. BibRef 8109
Earlier:
Stochastic Models for Closed Boundary Analysis: Part I, Representation, and Construction,
ICPR80(1354-1359). BibRef

Chellappa, R., and Kashyap, R.L.,
On the Correlation Structure of Random Field Models of Images and Textures,
PRIP81(574-576). BibRef 8100

Chellappa, R., and Kashyap, R.L.,
Synthetic Generation and Estimation in Random Field Models of Images,
PRIP81(577-582). BibRef 8100

Kashyap, R.L., Chellappa, R., and Ahuja, N.,
Decision Rules for the Choice of Neighbors in Random Field Models of Images,
CGIP(15), No. 4, April 1981, pp. 301-318. BibRef 8104

Kashyap, R.L.,
Two Dimensional Autoregressive Models for Images: Parameter Estimation and Choice of Neighbors,
PRAI-78(152-154). BibRef 7800

Chellappa, R., Chatterjee, S.,
Classification of Textures Using Gaussian Markov Random Fields,
ASSP(33), August 1985, pp. 959-963. See also Unsupervised Texture Segmentation Using Markov Random Field Models. BibRef 8508

Chellappa, R., Chatterjee, S., Bagdazian, R.,
Texture Synthsis and Compression Using Gaussian-Markov Random Field Models,
SMC(15), No. 2, March/April 1985, pp. 298-303. BibRef 8503

Chellappa, R., Hu, Y.H., Kung, S.Y.,
On Two-Dimensional Markov Spectral Estimation,
ASSP(31), No. 4, August 1983, pp. 836-841. BibRef 8308

Kashyap, R.L., and Khotanzad, A.,
A Model-Based Method for Rotation Invariant Texture Classification,
PAMI(8), No. 4, July 1986, pp. 472-481. BibRef 8607
Earlier:
Rotation Invariant Texture Classification Using Circular Random Field Models,
CVPR83(194-200). BibRef

Khotanzad, A., Kashyap, R.L.,
Feature Selection for Texture Recognition Based on Image Synthesis,
SMC(17), No. 6, November 1987, pp. 1087-1095. BibRef 8711

Kashyap, R.L., Khotanzad, A.,
A Stochastic Model Based Technique for Texture Segmentation,
ICPR84(1202-1205). BibRef 8400

Kashyap, R.L., Chellappa, R., and Khotanzad, A.,
Texture Classification Using Features Derived from Random Field Models,
PRL(1), October 1982, pp. 43-50. See also Color Image Retrieval Using Multispectral Random Field Texture Model and Color Content Features. BibRef 8210

Zerubia, J.B., and Chellappa, R.,
Mean Field Annealing Using Compound Gauss-Markov Random Fields for Edge Detection and Image Estimation,
TNN(4), 1993. BibRef 9300

Berthod, M., Kato, Z., Zerubia, J.B.,
DPA: a deterministic approach to the MAP problem,
IP(4), No. 9, September 1995, pp. 1312-1314.
WWW Version. 0402 BibRef

Kato, Z.[Zoltan], Berthod, M.[Marc], Zerubia, J.B.[Josiane B.],
A Hierarchical Markov Random-Field Model and Multitemperature Annealing for Parallel Image Classification,
GMIP(58), No. 1, January 1996, pp. 18-37. BibRef 9601

Zerubia, J.B., Kato, Z., Berthod, M.,
Multi-temperature annealing: a new approach for the energy-minimization of hierarchical Markov random field models,
ICPR94(A:520-522).
WWW Version. 9410 BibRef

Kato, Z.[Zoltan], Zerubia, J.B.[Josiane B.], Berthod, M.[Marc],
Unsupervised parallel image classification using Markovian models,
PR(32), No. 4, April 1999, pp. 591-604. BibRef 9904
And:
WWW Version.
Unsupervised Parallel Image Classification Using a Hierarchical Markovian Model,
ICCV95(169-174).
WWW Version.
WWW Version. BibRef

Berthod, M., Kato, Z., Yu, S., Zerubia, J.B.,
Bayesian Image Classification Using Markov Random-Fields,
IVC(14), No. 4, May 1996, pp. 285-295.
WWW Version. 9607 BibRef

Kato, Z., Berthod, M., and Zerubia, J.B.,
Multiscale Markov Random Field Models for Parallel Image Classification,
ICCV93(253-257).
WWW Version. BibRef 9300

Volden, E.[Espen], Giraudon, G.[Gérard], Berthod, M.[Marc],
Image redundancy and classification,
CAIP95(206-213).
WWW Version. 9509 BibRef

Wu, Z., and Leahy, R.,
An Approximate Method of Evaluating the Joint Likelihood for First-Order GMRFs,
IP(2), No. 4, October 1993, pp. 520-523.
WWW Version. BibRef 9310

Fine, S., Singer, Y., and Tishby, N.,
The hierarchical hidden markov model: Analysis and applications,
MachLearn(31), 1998, pp. 32. BibRef 9800

Bennett, J.W.[Jesse W.], Khotanzad, A.[Alireza],
Multispectral Random Field Models for Synthesis and Analysis of Color Images,
PAMI(20), No. 3, March 1998, pp. 327-332.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9805 BibRef
Earlier:
Multispectral and Color Image Modeling and Synthesis Using Random Field Models,
ICIP96(III: 991-994).
WWW Version. Extend the tradional gray level models to color. And a pseudo Markov model that allows simplified estimation. See also Color Image Retrieval Using Multispectral Random Field Texture Model and Color Content Features. See also Maximum Likelihood Estimation Methods for Multispectral Random Field Image Models. BibRef

Khotanzad, A., Bennett, J.W.,
Spatial Correlation Based Method for Neighbor Set Selection in Random Field Image Models,
IP(8), No. 5, May 1999, pp. 734-740.
WWW Version. BibRef 9905
Earlier:
A correlation structure based approach to neighborhood selection in random field models of texture images,
ICIP94(III: 383-387).
WWW Version. 9411 BibRef

Bennett, J.W.[Jesse W.], Khotanzad, A.[Alireza],
Modeling Textured Images Using Generalized Long Correlation Models,
PAMI(20), No. 12, December 1998, pp. 1365-1370.
IEEE Abstract. IEEE Top Reference.
WWW Version. See also Maximum Likelihood Estimation Methods for Multispectral Random Field Image Models. BibRef 9812

Kalayeh, H.M., and Landgrebe, D.A.,
Stochastic Model Utilizing Spectral and Spatial Characteristics,
PAMI(9), No. 3, May 1987, pp. 457-461. BibRef 8705

Veijanen, A.[Ari],
A Simulation-Based Estimator for Hidden Markov Random Fields,
PAMI(13), No. 8, August 1991, pp. 825-830.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9108

Veijanen, A.,
Contextual estimators of mixing probabilities for Markov chain random fields,
PR(26), No. 5, May 1993, pp. 763-769.
WWW Version. 0401 BibRef

Zhang, J.[Jun],
Parameter reduction for the compound Gauss-Markov model,
IP(4), No. 3, March 1995, pp. 382-386.
WWW Version. 0402 BibRef

Povlow, B.R., Dunn, S.M.,
Texture Classification Using Noncausal Hidden Markov-Models,
PAMI(17), No. 10, October 1995, pp. 1010-1014.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9510
Earlier: CVPR93(642-643).
IEEE Abstract. IEEE Top Reference. Noncausal: depends on neighbors in all directions. BibRef

Solberg, A.H.S., Taxt, T., Jain, A.K.,
A Markov Random-Field Model for Classification of Multisource Satellite Imagery,
GeoRS(34), No. 1, January 1996, pp. 100-113.
IEEE Top Reference. BibRef 9601

Wu, C.H.[Chi-Hsin], Doerschuk, P.C.,
Tree Approximations to Markov Random-Fields,
PAMI(17), No. 4, April 1995, pp. 391-402.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9504
Earlier:
Bayesian spatial classifiers based on tree approximations to Markov random fields,
ICIP94(II: 202-206).
WWW Version. 9411Applied to segmentation: See also Texture-Based Segmentation Using Markov Random Field Models and Approximate Bayesian Estimators Based on Trees. BibRef

Speis, A.[Athanasios], Healey, G.[Glenn],
An Analytical and Experimental Study of the Performance of Markov Random-Fields Applied to Textured Images Using Small Samples,
IP(5), No. 3, March 1996, pp. 447-458.
WWW Version. BibRef 9603
Earlier: ICCV95(115-120).
WWW Version.
WWW Version. The Least Square estimator is the only reasonable choice. Abstract:
HTML Version. See also Markov Random-Field Models for Unsupervised Segmentation of Textured Color Images. BibRef

Speis, A.[Athanasios], Healey, G.[Glenn],
Feature-Extraction for Texture-Discrimination via Random-Field Models with Random Spatial Interaction,
IP(5), No. 4, April 1996, pp. 635-645.
WWW Version. 9605 BibRef
Earlier:
New Directions in Texture Modeling Using Random Fields with Random Spatial Interaction,
PBMCV95(SESSION 6) BibRef

Zhang, J.,
The Mean Field Theory in EM Procedures for Blind Markov Random Field Image Restoration,
IP(2), No. 1, January 1993, pp. 27-40.
WWW Version. BibRef 9301

Zhang, J.,
The Application of the Gibbs-Bogoliubov-Feynman Inequality in Mean-Field Calculations for Markov Random-Fields,
IP(5), No. 7, July 1996, pp. 1208-1214.
WWW Version. 9607 BibRef

Zhang, J.,
The Convergence of Mean-Field Procedures for MRFs,
IP(5), No. 12, December 1996, pp. 1662-1665.
WWW Version. 9701 BibRef

Gurelli, M.I., Onural, L.,
On a parameter estimation method for Gibbs-Markov random fields,
PAMI(16), No. 4, April 1994, pp. 424-430.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

della Pietra, S., della Pietra, V., Lafferty, J.,
Inducing features of random fields,
PAMI(19), No. 4, April 1997, pp. 380-393.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

Wu, W.R., Wei, S.C.,
Rotation and Gray-Scale Transform-Invariant Texture Classification Using Spiral Resampling, Subband Decomposition, and Hidden Markov Model,
IP(5), No. 10, October 1996, pp. 1423-1434.
WWW Version. 9610 BibRef
And: Correction: IP(7), No. 2, February 1998, pp. 253-253.
WWW Version. 9802 BibRef

Jeng, F.C.,
Subsampling of Markov Random Fields,
JVCIR(3), 1992, pp. 225-229. BibRef 9200

Gray, A.J., Kay, J.W., Titterington, D.M.,
On the Estimation of Noisy Binary Markov Random Fields,
PR(25), No. 7, July 1992, pp. 749-768.
WWW Version. BibRef 9207

Qian, W., Titterington, D.M.,
On the Use of Gibbs Markov Chain Models in the Analysis of Images Based on Second-Order Pairwise Interactive Distributions,
AppStat(6), No. 2, 1989, pp. 267-282. BibRef 8900

Qian, W., Titterington, D.M.,
Pixel labelling for 3-D scenes based on Markov mesh models,
SP(22), No. 3, 1991, pp. 313-328. BibRef 9100

Dunmur, A.P., Titterington, D.M.,
Computational Bayesian Analysis of Hidden Markov Mesh Models,
PAMI(19), No. 11, November 1997, pp. 1296-1300.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9712 BibRef

Dunmur, A.P., Titterington, D.M.,
Mean Fields and Two Dimensional Markov Random Fields,
PAA(1), No. 4, 1998, pp. 248-260. BibRef 9800

Aykroyd, R.G., Haigh, J.G.B., Zimeras, S.,
Unexpected Spatial Patterns in Exponential Family Auto Models,
GMIP(58), No. 5, September 1996, pp. 452-463. 9611 BibRef

Milun, D., Sher, D.,
Improving Sampled Probability Distributions for Markov Random Fields,
PRL(14), 1993, pp. 781-788. BibRef 9300
Earlier:
Learning structural and corruption information from samples for Markov random field binary image reconstruction,
ICPR92(III:513-516).
WWW Version. 9208 BibRef

Gimel'Farb, G.L., Zalesny, A.V.,
Probabilistic Models of Digital Region Maps Based on Markov Random Fields with Short- and Long-Range Interaction,
PRL(14), 1993, pp. 789-797. BibRef 9300

Gimel'Farb, G.L., Van Gool, L.J., Zalesny, A.V.,
To FRAME or not to FRAME in probabilistic texture modelling?,
ICPR04(II: 707-711).
WWW Version. 0409 BibRef

Chen, C.C., Huang, C.L.,
Markov Random Fields for Texture Classification,
PRL(14), 1993, pp. 907-914. BibRef 9300

Sher, D.B.,
Minimizing the Cost of Errors with a Markov Random Field,
PRL(12), 1991, pp. 85-89. BibRef 9100

Chen, C.C.,
A Nonparametric Test for Comparing Estimators in Markov Random Fields,
PRL(11), 1990, pp. 765-770. BibRef 9000

Jeng, F.C., Woods, J.W.,
On the Relationship of the Markov Mesh to the NSHP Markov Chain,
PRL(5), 1987, pp. 273-279. BibRef 8700

Bello, M.G.,
A Combined Markov Random Field and Wave-Packet Transform-Based Approach for Image Segmentation,
IP(3), No. 6, November 1994, pp. 834-846.
WWW Version. BibRef 9411

Li, S.Z., Wang, H., Chan, K.L., Petrou, M.,
Minimization of MRF Energy With Relaxation Labeling,
JMIV(7), No. 2, March 1997, pp. 149-161.
WWW Version. 9705 BibRef

Li, S.Z., Wang, H.[Han], Petrou, M.,
Relaxation labeling of Markov random fields,
ICPR94(A:488-492).
WWW Version. 9410 BibRef

Smyth, P.,
Belief Networks, Hidden Markov-Models, and Markov Random Fields: A Unifying View,
PRL(18), No. 11-13, November 1997, pp. 1261-1268. 9806 BibRef

Fessler, J.A.,
On the Convergence of Mean Field Procedures for MRFs,
IP(7), No. 6, June 1998, pp. 917.
WWW Version. 9806 BibRef

Shen, D., Ip, H.H.S.,
Markov random field regularisation models for adaptive binarisation of nonuniform images,
VISP(145), No. 5, October 1998, pp. p.322. BibRef 9810

Descombes, X., Morris, R.D., Zerubia, J.B., Berthod, M.,
Estimation of Markov Random Field Prior Parameters Using Markov Chain Monte Carlo Maximum Likelihood,
IP(8), No. 7, July 1999, pp. 954-963.
WWW Version. BibRef 9907

Rellier, G.[Guillaume], Descombes, X.[Xavier], Falzon, F.[Frederic], Zerubia, J.B.[Josiane B.],
Analyse de texture hyperspectrale par modélisation markovien,
INRIARR-4479, June 2002.
HTML Version. 0211 BibRef

Descombes, X.[Xavier],
A Dense Class of Markov Random Fields and Associated Parameter Estimation,
JVCIR(8), 1997, pp. 299-316. BibRef 9700

Lorette, A., Descombes, X., Zerubia, J.B.,
Texture Analysis through a Markovian Modelling and Fuzzy Classification: Application to Urban Area Extraction from Satellite Images,
IJCV(36), No. 3, February-March 2000, pp. 221-236.
WWW Version. 0003 BibRef
Earlier:
Texture Analysis through Markov Random Fields: Urban Areas Extraction,
ICIP99(IV:430-434).
IEEE Abstract. IEEE Top Reference. Urban Area. BibRef

Rellier, G., Descombes, X., Zerubia, J.B., Falzon, F.,
A gauss-markov model for hyperspectral texture analysis of urban areas,
ICPR02(I: 692-695).
WWW Version. 0211 BibRef

Viveros-Cancino, O.[Oscar], Descombes, X.[Xavier], Zerubia, J.B.[Josiane B.],
Analyse intra-urbaine à partir d'images satellitaires par une approche de fusion de données sur la ville de Mexico,
INRIARR-4578, October 2002.
HTML Version. 0211Urban texture extraction. Split/merge application. BibRef

Descombes, X., Sigelle, M., Preteux, F.,
Estimating Gaussian Markov Random Field Parameters in a Nonstationary Framework: Application to Remote Sensing Imaging,
IP(8), No. 4, April 1999, pp. 490-503.
WWW Version. BibRef 9904

Tso, B.C.K., Mather, P.M.,
Classification of Multisource Remote Sensing Imagery Using a Genetic Algorithm and Markov Random Fields,
GeoRS(37), No. 3, May 1999, pp. 1255.
IEEE Top Reference. BibRef 9905

Shahtalebi, K., Gazor, S., Pasupathy, S., Gulak, P.G.,
Second order H-infinity optimal LMS and NLMS algorithms based on a second-order Markov model,
VISP(147), No. 3, 2000, pp. 231-237. 0008 BibRef

Zhu, S.C.[Song Chun], Liu, X.W.[Xie Wen], Wu, Y.N.[Ying Nian],
Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo-Toward a 'Trichromacy' Theory of Texture,
PAMI(22), No. 6, June 2000, pp. 554-569.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0008 BibRef

Wang, L.[Lei], Liu, J.[Jun], Li, S.Z.[Stan Z.],
MRF parameter estimation by MCMC method,
PR(33), No. 11, November 2000, pp. 1919-1925.
WWW Version. 0011 BibRef

Huang, K.C.[Kuo-Chang], Tung, S.L.[Shin-Lun], Juang, Y.T.[Yau-Tarng],
Application of the variance compensation likelihood measure for robust hidden Markov model in noise,
PRL(22), No. 3-4, March 2001, pp. 353-358.
HTML Version. 0105 BibRef

Cai, J.H.[Jin-Hai], Liu, Z.Q.[Zhi-Qiang],
Pattern recognition using Markov random field models,
PR(35), No. 3, March 2002, pp. 725-733.
WWW Version. 0201 BibRef

Bui, H., Venkatesh, S., and West, G.A.W.,
Policy recognition in the abstract hidden markov model,
JAIR(17), 2002, pp. 451-499. BibRef 0200

Stan, S., Palubinskas, G., Datcu, M.,
Bayesian selection of the neighbourhood order for Gauss-Markov texture models,
PRL(23), No. 10, August 2002, pp. 1229-1238.
HTML Version. 0205 BibRef

Yu, Y.[Yihua], Cheng, Q.S.[Qian-Sheng],
MRF parameter estimation by an accelerated method,
PRL(24), No. 9-10, June 2003, pp. 1251-1259.
WWW Version. 0304 BibRef

Ferraiuolo, G., Pascazio, V.,
The effect of modified markov random fields on the local minima occurrence in microwave imaging,
GeoRS(41), No. 5, May 2003, pp. 1043-1055.
IEEE Abstract. IEEE Top Reference. 0307 BibRef

Ibáñez, M.V., Simó, A.,
Parameter estimation in Markov random field image modeling with imperfect observations. A comparative study,
PRL(24), No. 14, October 2003, pp. 2377-2389.
WWW Version. 0307 BibRef

Marroquín, J.L.[Jose L.], Santana, E.A.[Edgar Arce], Botello, S.[Salvador],
Hidden Markov measure field models for image segmentation,
PAMI(25), No. 11, November 2003, pp. 1380-1387.
IEEE Abstract. IEEE Top Reference. 0311Find a label field that divides the image into regions. Applied to MRI data. See also MPM-MAP algorithm for motion segmentation, The. BibRef

Rivera, M., Ocegueda, O., Marroquin, J.L.,
Entropy-Controlled Quadratic Markov Measure Field Models for Efficient Image Segmentation,
IP(16), No. 12, December 2007, pp. 3047-3057.
WWW Version. 0711 BibRef

Marroquin, J.L., Santana, E.A., Botello, S.,
Markov random measure fields for image analysis,
ICIP02(I: 765-768).
IEEE Abstract. IEEE Top Reference. 0210 BibRef

Li, F.[Feng], Peng, J.[Jiaxiong],
Double random field models for remote sensing image segmentation,
PRL(25), No. 1, January 2004, pp. 129-139.
WWW Version. 0311 BibRef

Paget, R.[Rupert],
Strong Markov Random Field Model,
PAMI(26), No. 3, March 2004, pp. 408-413.
IEEE Abstract. IEEE Top Reference. 0402 BibRef

Deng, H.[Huawu], Clausi, D.A.,
Gaussian MRF Rotation-Invariant Features for Image Classification,
PAMI(26), No. 7, July 2004, pp. 951-955.
IEEE Abstract. IEEE Top Reference. 0406 BibRef
Earlier:
Advanced gaussian MRF rotation-invariant texture features for classification of remote sensing imagery,
CVPR03(II: 685-690).
IEEE Abstract. IEEE Top Reference. 0307Develop a circular MRF model to recover rotation invariant textures. Compare to Laplacian pyramid, isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features. BibRef

Sarkar, A., Banerjee, A., Banerjee, N., Brahma, S., Kartikeyan, B., Chakraborty, M., Majumder, K.L.,
Landcover Classification in MRF Context Using Dempster-Shafer Fusion for Multisensor Imagery,
IP(14), No. 5, May 2005, pp. 634-645.
WWW Version. 0505 BibRef

Sarkar, A., Banerjee, N., Nair, P., Banerjee, A., Brahma, S., Kartikeyan, B., Majumder, K.L.,
A MRF Based Segmentatiom Approach to Classification Using Dempster Shafer Fusion for Multisensor Imagery,
ICIAR04(II: 421-428).
WWW Version. 0409 BibRef

Li, Y.J.[Yu-Jian],
Hidden Markov models with states depending on observations,
PRL(26), No. 7, 15 May 2005, pp. 977-984.
WWW Version. 0506 BibRef

Destrempes, F., Mignotte, M., Angers, J.F.,
A stochastic method for Bayesian estimation of hidden Markov random field models with application to a color model,
IP(14), No. 8, August 2005, pp. 1096-1108.
WWW Version. 0508 BibRef

Destrempes, F., Angers, J.F., Mignotte, M.,
Fusion of Hidden Markov Random Field Models and Its Bayesian Estimation,
IP(15), No. 10, October 2006, pp. 2920-2935.
WWW Version. 0609 BibRef

Chen, L.[Ling], Man, H.[Hong],
Fast Schemes for Computing Similarities between Gaussian HMMs and Their Applications in Texture Image Classification,
JASP(2005), No. 13, 2005, pp. 1984-1993.
WWW Version. 0603 BibRef

Bicego, M.[Manuele], Murino, V.[Vittorio], Figueiredo, M.A.T.[Mário A. T.],
A sequential pruning strategy for the selection of the number of states in hidden Markov models,
PRL(24), No. 9-10, June 2003, pp. 1395-1407.
WWW Version. 0304 BibRef

Bicego, M.[Manuele], Dovier, A.[Agostino], Murino, V.[Vittorio],
Designing the Minimal Structure of Hidden Markov Model by Bisimulation,
EMMCVPR02(75 ff.).
HTML Version. 0205 BibRef

Bicego, M.[Manuele], Cristani, M.[Marco], Murino, V.[Vittorio],
Sparseness Achievement in Hidden Markov Models,
CIAP07(67-72).
WWW Version. 0709 BibRef

Joshi, D., Li, J., Wang, J.Z.,
A Computationally Efficient Approach to the Estimation of Two- and Three-Dimensional Hidden Markov Models,
IP(15), No. 7, July 2006, pp. 1871-1886.
WWW Version. 0606 BibRef

Ichir, M.M., Mohammad-Djafari, A.,
Hidden Markov Models for Wavelet-Based Blind Source Separation,
IP(15), No. 7, July 2006, pp. 1887-1899.
WWW Version. 0606 BibRef

Caputo, B.,
A spin glass model of a Markov random field,
IJIST(16), No. 5, 2006, pp. 181-188.
WWW Version. 0704 BibRef

Caputo, B., Bouattour, S., Niemann, H.,
Robust appearance-based object recognition using a fully connected Markov random field,
ICPR02(III: 565-568).
WWW Version. 0211 BibRef

Caputo, B., Bouattour, S., Paulus, D.,
A Novel Probabilistic Model for 3-D Object Recognition: Spin-Glass Markov Random Fields,
VMV01(xx-yy).
PDF Version. 0209 BibRef

Caputo, B., Niemann, H.,
To each according to its need: kernel class specific classifiers,
ICPR02(IV: 94-97).
WWW Version. 0211 BibRef

Wallraven, C., Caputo, B., Graf, A.,
Recognition with local features: the kernel recipe,
ICCV03(257-264).
WWW Version. 0311SVM learning applied to local features. BibRef

Caputo, B., Niemann, H.,
From Markov Random Fields to Associative Memories and Back: Spin Glass Markov Random Fields,
SCTV01(xx-yy). 0106 BibRef

Ceccarelli, M.[Michele],
A Finite Markov Random Field approach to fast edge-preserving image recovery,
IVC(25), No. 6, 1 June 2007, pp. 792-804.
WWW Version. 0704 BibRef
Earlier:
Fast Edge Preserving Picture Recovery by Finite Markov Random Fields,
CIAP05(277-286).
WWW Version. 0509Markov random fields; Image denoising; Edge-preserving potentials BibRef

Antoniol, G., Ceccarelli, M.,
A Markov random field approach to microarray image gridding,
ICPR04(III: 550-553).
WWW Version. 0409 BibRef

Blanchet, J.[Juliette], Forbes, F.B.P.[Florence B.P.],
Triplet Markov Fields for the Classification of Complex Structure Data,
PAMI(30), No. 6, June 2008, pp. 1055-1067.
WWW Version. 0804 BibRef

Blanchet, J.[Juliette], Forbes, F.B.P.[Florence B.P.], Schmid, C.,
Markov random fields for textures recognition with local invariant regions and their geometric relationships,
BMVC05(xx-yy).
HTML Version. 0509 BibRef

Hauberg, S.[Søren], Sloth, J.[Jakob],
An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application,
JMIV(31), No. 2-3, July 2008, pp. 165-170.
WWW Version. 0711 BibRef

Xue, J.H.[Jing-Hao], Titterington, D.M.[D. Michael],
Short note on two output-dependent hidden Markov models,
PRL(29), No. 9, 1 July 2008, pp. 1424-1426.
WWW Version. 0711Discriminative models; Generative models; Mutual information independence; Output-dependent hidden Markov model BibRef

Komodakis, N.[Nikos], Tziritas, G.[Georgios], Paragios, N.[Nikos],
Fast, Approximately Optimal Solutions for Single and Dynamic MRFs,
CVPR07(1-8).
WWW Version. 0706 BibRef

Rother, C.[Carsten], Kolmogorov, V.[Vladimir], Lempitsky, V.[Victor], Szummer, M.[Martin],
Optimizing Binary MRFs via Extended Roof Duality,
CVPR07(1-8).
WWW Version. 0706 BibRef

Gu, L.[Lie], Xing, E.P.[Eric P.], Kanade, T.[Takeo],
Learning GMRF Structures for Spatial Priors,
CVPR07(1-6).
WWW Version. 0706 BibRef

Verbeek, J.[Jakob], Triggs, B.[Bill],
Region Classification with Markov Field Aspect Models,
CVPR07(1-8).
WWW Version. 0706 BibRef

Joshi, D., Li, J., Wang, J.Z.,
Parameter Estimation of Multi-Dimensional Hidden Markov Models: A Scalable Approach,
ICIP05(III: 149-152).
WWW Version. 0512 BibRef

Li, J., Joshi, D., Wang, J.Z.,
Stochastic modeling of volume images with a 3-d hidden markov model,
ICIP04(IV: 2359-2362).
WWW Version. 0505 BibRef

Kuruoglu, E.E., Tonazzini, A., Bianchi, L.,
Source separation in noisy astrophysical images modelled by markov random fields,
ICIP04(IV: 2701-2704).
WWW Version. 0505 BibRef

Pichler, A., Fisher, R.B., Vincze, M.,
Decomposition of range images using markov random fields,
ICIP04(II: 1205-1208).
WWW Version. 0505 BibRef

Liu, Z.Q.[Zi-Qiang], Chen, H.[Hong], Shum, H.Y.[Heung-Yeung],
An efficient approach to learning inhomogeneous Gibbs model,
CVPR03(I: 425-431).
IEEE Abstract. IEEE Top Reference. 0307demonstrate the efficiency of our approach by learning a high-dimensional joint distribution of face images and their corresponding caricatures. BibRef

Collet, C., Louys, M., Oberto, A., Bot, C.,
Markov model for multispectral image analysis: application to small magellanic cloud segmentation,
ICIP03(I: 953-956).
IEEE Abstract. IEEE Top Reference. 0312 BibRef

Mertins, A., Jamart, O.,
Decoding of images using soft-bits and Markov random field modeling,
ICIP02(I: 241-244).
IEEE Abstract. IEEE Top Reference. 0210 BibRef

Kim, J.[Junhwan], Zabih, R.[Ramin],
Factorial Markov Random Fields,
ECCV02(III: 321 ff.).
HTML Version. 0205 BibRef

Costen, N.P., Cootes, T.F., Taylor, C.J.,
Markov fields for recognition derived from facial texture error,
BMVC01(Poster Session 2. and Demonstrations).
HTML Version. Manchester Metropolitan University 0110 BibRef

Salles, E., Lee, L.,
Texture Classification by Means of HMM Modeling of AM-FM Features,
ICIP01(III: 182-185).
IEEE Abstract. IEEE Top Reference. 0108 BibRef

Müller, S., Wallhoff, F., Rigoll, G.,
Retrieval of Overlapping and Touching Objects Using Hidden Markov Models,
ICIP01(II: 761-764).
IEEE Abstract. IEEE Top Reference. 0108 BibRef

August, J., Zucker, S.W.,
A Generative Model for Image Contours: A Completely Characterized Non-Gaussian Joint Distribution,
SCTV01(xx-yy). 0106 BibRef

Oukil, A., Serir, A.,
Markovian Random Fields Energy Minimization Algorithms,
ICPR00(Vol III: 522-525).
HTML Version. 0009 BibRef

Sivakumar, K.,
A Morphological Estimator for Clique Potentials of Binary Markov Random Fields,
ICIP00(Vol I: 264-267).
IEEE Abstract. IEEE Top Reference. 0008 BibRef

Paget, R.[Rupert], Longstaff, I.D.[I. Dennis],
Nonparametric Markov Random Field Model Analysis of the MeasTex Test Suite,
ICPR00(Vol III: 927-930).
WWW Version.
WWW Version.
HTML Version. 0009 BibRef

Çarkacioglu, A.[Abdurrahman], Yarman-Vural, F.T.[Fatos T.],
Similarity measures for binary and gray level Markov Random Field textures,
CIAP97(I: 127-133).
WWW Version. 9709 BibRef

Budzban, G., Casey, W.,
The effect of stable points on the convergence of Markov random fields,
ICIP98(I: 77-79).
WWW Version. 9810 BibRef

Tanaka, K., Ichioka, M., Morita, T.,
Statistical-Mechanical Algorithm in MRF Model Based on Variational Principle,
ICPR96(II: 381-388).
WWW Version. 9608(Muroran Inst. of Technology, J) BibRef

Mosquera, A., Cabello, D.,
The Markov Random Fields in Functional Neighbors as a Texture Model: Applications in Texture Classification,
ICPR96(II: 815-819).
WWW Version. 9608(Univ. Santiago de Compostela, E) BibRef

Delagnes, P., Barba, D.,
Rectilinear Structure Extraction in Textured Images with an Irregular Graph-Based Markov Random Field Model,
ICPR96(II: 800-804).
WWW Version. 9608(Univ. de Nantes, F) BibRef

Li, S.Z., Huang, Y.H., Fu, J.S.,
Convex MRF potential functions,
ICIP95(II: 296-299).
WWW Version. 9510 BibRef

Yin, H., Allinson, N.M.,
Self-organised parameter estimation and segmentation of MRF model-based texture images,
ICIP94(II: 645-649).
WWW Version. 9411 BibRef

Milanfar, P., Tenney, R.R., Washburn, R.B., Willsky, A.S.,
Modeling and estimation for a class of multiresolution random fields,
ICIP94(III: 397-401).
WWW Version. 9411 BibRef

Ghozi, R., Levy, B.C.,
Critical Markov random fields and fractional Brownian motion in texture synthesis,
ICIP94(III: 426-430).
WWW Version. 9411 BibRef

Chiou, G.I., Hwang, J.N.[Jenq-Neng],
Image sequence classification using a neural network based active contour model and a hidden Markov model,
ICIP94(III: 926-930).
WWW Version. 9411 BibRef

Trumbo, M., Vaisey, J.,
Variable resolution Markov modelling of signal data for image compression,
ICIP95(I: 282-285).
WWW Version. 9510 BibRef

Baddeley, A.J.[Adrian J.], van Lieshout, M.N.M.,
Object recognition using Markov spatial processes,
ICPR92(II:136-139).
WWW Version. 9208 BibRef

Waks, A., Tretiak, O.J., Gregoriou, G.K.,
Restoration of noisy regions modeled by noncausal Markov random fields of unknown parameters,
ICPR90(II: 170-175).
WWW Version. 9208 BibRef

Gao, Y.Q.[Yu Qing], Chen, Y.B.[Yong Bin], Huang, T.Y.[Ta Yi],
A new method for estimation of hidden Markov model parameters,
ICPR90(II: 27-30).
WWW Version. 9208 BibRef

Devijver, P.A.,
Real-time modeling of image sequences based on hidden Markov mesh random field models,
ICPR90(II: 194-199).
WWW Version. 9008 BibRef

Haralick, R.M., Zhang, M.C., Ehrich, R.W.,
Dynamic programming approach for context classification using the Markov random field,
ICPR88(II: 1169-1181).
WWW Version. 8811 BibRef

He, Y.[Yang],
Extended Viterbi algorithm for second order hidden Markov process,
ICPR88(II: 718-720).
WWW Version. 8811 BibRef

Chen, C.C., Dubes, R.C.,
Experiments in Fitting Discrete Markov Random Fields to Textures,
CVPR89(298-303).
IEEE Abstract. IEEE Top Reference. BibRef 8900

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Hierarchical, Multi-Scale Texture Representations and Analysis .