7.10.3 Markov Random Field Models

Chapter Contents (Back)
Markov Random Field. MRF. See also MRF Models for Segmentation. See also Energy Minimization, Energy Maximization Computation, Function Solving. See also MRF Optimization, Energy Minimization.

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

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), No. 4, April 1980, pp. 357-370.
WWW Link. BibRef 8004
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), No. 4, April 1980, pp. 371-375.
WWW Link. BibRef 8004

Kashyap, R.L.,
Random Field Models of Images,
CGIP(12), No. 3, March 1980, pp. 257-270.
WWW Link. 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.
WWW Link. 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.
IEEE DOI 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).
IEEE DOI 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 Link.
Unsupervised Parallel Image Classification Using a Hierarchical Markovian Model,
ICCV95(169-174).
IEEE DOI 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 Link. 9607
BibRef

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

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

Miles, R.E.,
A survey of geometrical probability in the plane, with emphasis on stochastic image modeling,
CGIP(12), No. 1, January 1980, pp. 1-24.
WWW Link. 0501
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.
IEEE DOI 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 DOI 9805
BibRef
Earlier:
Multispectral and Color Image Modeling and Synthesis Using Random Field Models,
ICIP96(III: 991-994).
IEEE DOI 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.
IEEE DOI BibRef 9905
Earlier:
A correlation structure based approach to neighborhood selection in random field models of texture images,
ICIP94(III: 383-387).
IEEE DOI 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 DOI 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 DOI BibRef 9108

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

Zhang, J.[Jun],
Parameter reduction for the compound Gauss-Markov model,
IP(4), No. 3, March 1995, pp. 382-386.
IEEE DOI 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 DOI BibRef 9510
Earlier: CVPR93(642-643).
IEEE DOI 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 DOI BibRef 9504
Earlier:
Bayesian spatial classifiers based on tree approximations to Markov random fields,
ICIP94(II: 202-206).
IEEE DOI 9411
Applied 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.
IEEE DOI BibRef 9603
Earlier: ICCV95(115-120).
IEEE DOI 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.
IEEE DOI 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.
IEEE DOI BibRef 9301

Zhang, J.,
An Alternating Minimization Algorithm for Binary Image Restoration,
IP(21), No. 2, February 2012, pp. 883-888.
IEEE DOI 1201
BibRef

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.
IEEE DOI 9607
BibRef

Zhang, J.,
The Convergence of Mean-Field Procedures for MRFs,
IP(5), No. 12, December 1996, pp. 1662-1665.
IEEE DOI 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 DOI 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 DOI 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.
IEEE DOI 9610
BibRef
And: Correction: IP(7), No. 2, February 1998, pp. 253-253.
IEEE DOI 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 Link. 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 DOI 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).
IEEE DOI 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).
IEEE DOI 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.
IEEE DOI 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.
DOI Link 9705
BibRef

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

Smyth, P.P., Taylor, C.J., Adams, J.,
Texture Analysis using Local Property Maps,
BMVC95(xx-yy).
PDF File. 9509
BibRef

Fessler, J.A.,
On the Convergence of Mean Field Procedures for MRFs,
IP(7), No. 6, June 1998, pp. 917.
IEEE DOI 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.
IEEE DOI 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.
DOI Link 0003
BibRef
Earlier:
Texture Analysis through Markov Random Fields: Urban Areas Extraction,
ICIP99(IV:430-434).
IEEE DOI 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).
IEEE DOI 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. 0211
Urban 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.
IEEE DOI 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 DOI 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 Link. 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.
Elsevier DOI 0105
BibRef

Cai, J.H.[Jin-Hai], Liu, Z.Q.[Zhi-Qiang],
Hidden Markov Models with Spectral Features for 2D Shape Recognition,
PAMI(23), No. 12, December 2001, pp. 1454-1458.
IEEE DOI 0112
For contour descriptions. 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 Link. 0201
BibRef

Cai, J.H.[Jin-Hai], Liu, Z.Q.[Zhi-Qiang],
Markov Process In Pattern Recognition,
IJIG(1), No. 2, April 2001, pp. 287-311. 0104
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.
Elsevier DOI 0205
BibRef

Yu, Y.H.[Yi-Hua], Cheng, Q.S.[Qian-Sheng],
MRF parameter estimation by an accelerated method,
PRL(24), No. 9-10, June 2003, pp. 1251-1259.
WWW Link. 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. 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 Link. 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. 0311
Find 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.
IEEE DOI 0711
BibRef

Marroquin, J.L., Santana, E.A., Botello, S.,
Markov random measure fields for image analysis,
ICIP02(I: 765-768).
IEEE DOI 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 Link. 0311
BibRef

Paget, R.[Rupert],
Strong Markov Random Field Model,
PAMI(26), No. 3, March 2004, pp. 408-413.
IEEE Abstract. 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. 0406
BibRef
Earlier:
Advanced gaussian MRF rotation-invariant texture features for classification of remote sensing imagery,
CVPR03(II: 685-690).
IEEE DOI 0307
Develop 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.
IEEE DOI 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).
Springer DOI 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 Link. 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.
IEEE DOI 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.
IEEE DOI 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 Link. 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 Link. 0304
See also Investigating Hidden Markov Models Capabilities in 2D Shape Classification. BibRef

Bicego, M.[Manuele], Dovier, A.[Agostino], Murino, V.[Vittorio],
Designing the Minimal Structure of Hidden Markov Model by Bisimulation,
EMMCVPR01(75-90).
Springer DOI 0205
BibRef

Bicego, M.[Manuele], Cristani, M.[Marco], Murino, V.[Vittorio],
Sparseness Achievement in Hidden Markov Models,
CIAP07(67-72).
IEEE DOI 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.
IEEE DOI 0606
BibRef
Earlier:
Parameter Estimation of Multi-Dimensional Hidden Markov Models: A Scalable Approach,
ICIP05(III: 149-152).
IEEE DOI 0512
BibRef
Earlier: A2, A1, A3:
Stochastic modeling of volume images with a 3-d hidden markov model,
ICIP04(IV: 2359-2362).
IEEE DOI 0505
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.
IEEE DOI 0606
BibRef

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

Caputo, B., Bouattour, S., Niemann, H.,
Robust appearance-based object recognition using a fully connected Markov random field,
ICPR02(III: 565-568).
IEEE DOI 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 File. 0209
BibRef

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

Wallraven, C., Caputo, B., Graf, A.,
Recognition with local features: the kernel recipe,
ICCV03(257-264).
IEEE DOI 0311
SVM 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 Link. 0704
BibRef
Earlier:
Fast Edge Preserving Picture Recovery by Finite Markov Random Fields,
CIAP05(277-286).
Springer DOI 0509
Markov 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).
IEEE DOI 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.
IEEE DOI 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 Link. 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 Link. 0711
Discriminative models; Generative models; Mutual information independence; Output-dependent hidden Markov model BibRef

Roth, S.[Stefan], Black, M.J.[Michael J.],
Fields of Experts,
IJCV(82), No. 2, April 2009, pp. xx-yy.
Springer DOI 0903
BibRef
Earlier:
Steerable Random Fields,
ICCV07(1-8).
IEEE DOI 0710
BibRef
Earlier:
Fields of Experts: A Framework for Learning Image Priors,
CVPR05(II: 860-867).
IEEE DOI 0507
Markov random fields See also Efficient Belief Propagation with Learned Higher-Order Markov Random Fields. BibRef

Schelten, K.[Kevin], Roth, S.[Stefan],
Connecting non-quadratic variational models and MRFs,
CVPR11(2641-2648).
IEEE DOI 1106
Spatially-discrete Markov random fields (MRFs) and spatially-continuous variational approach. BibRef

Razlighi, Q.R.[Qolamreza R.], Kehtarnavaz, N.[Nasser], Nosratinia, A.,
Computation of Image Spatial Entropy Using Quadrilateral Markov Random Field,
IP(18), No. 12, December 2009, pp. 2629-2639.
IEEE DOI 0912
BibRef

Razlighi, Q.R.[Qolamreza R.], Rahman, M.T.[Mohammad T.], Kehtarnavaz, N.[Nasser],
Fast computation methods for estimation of image spatial entropy,
RealTimeIP(6), No. 2, June 2011, pp. 137-142.
WWW Link. 1101
BibRef

Kim, M.Y.[Min-Young],
Large margin cost-sensitive learning of conditional random fields,
PR(43), No. 10, October 2010, pp. 3683-3692.
Elsevier DOI 1007
Conditional random fields; Cost-sensitive learning BibRef

Alahari, K.[Karteek], Kohli, P.[Pushmeet], Torr, P.H.S.[Philip H. S.],
Dynamic Hybrid Algorithms for MAP Inference in Discrete MRFs,
PAMI(32), No. 10, October 2010, pp. 1846-1857.
IEEE DOI 1008
BibRef
Earlier:
Reduce, reuse & recycle: Efficiently solving multi-label MRFs,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Ramalingam, S.[Srikumar], Kohli, P.[Pushmeet], Alahari, K.[Karteek], Torr, P.H.S.[Philip H. S.],
Exact inference in multi-label CRFs with higher order cliques,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Zach, C.[Christopher], Kohli, P.[Pushmeet],
A Convex Discrete-Continuous Approach for Markov Random Fields,
ECCV12(VI: 386-399).
Springer DOI 1210
BibRef

Vacha, P.[Pavel], Haindl, M.[Michal], Suk, T.[Tomas],
Colour and rotation invariant textural features based on Markov random fields,
PRL(32), No. 6, 15 April 2011, pp. 771-779.
Elsevier DOI 1103
Image modelling; Colour; Texture; Markov random field; Illumination invariance; Rotation invariance BibRef

Chatzis, S.P.[Sotirios P.], Kosmopoulos, D.I.[Dimitrios I.], Doliotis, P.[Paul],
A conditional random field-based model for joint sequence segmentation and classification,
PR(46), No. 6, June 2013, pp. 1569-1578.
Elsevier DOI 1302
Conditional random field; Sequence segmentation; Sequence classification BibRef

Chatzis, S.P.[Sotirios P.],
A Markov random field-regulated Pitma-Yor process prior for spatially constrained data clustering,
PR(46), No. 6, June 2013, pp. 1595-1603.
Elsevier DOI 1302
Pitman-Yor process; Clustering; Markov random field BibRef

Wang, C.H.[Chao-Hui], Komodakis, N.[Nikos], Paragios, N.[Nikos],
Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey,
CVIU(117), No. 11, 2013, pp. 1610-1627.
Elsevier DOI 1309
Survey, Markov Random Fields. Markov Random Fields BibRef

Versteegen, R.[Ralph], Gimel'farb, G.L.[Georgy L.], Riddle, P.[Patricia],
Texture Modelling with Nested High-Order Markov-Gibbs Random Fields,
CVIU(143), No. 1, 2016, pp. 120-134.
Elsevier DOI 1601
BibRef
And:
Markov-Gibbs Texture Modelling with Learnt Freeform Filters,
SSSPR16(379-389).
Springer DOI 1611
BibRef
Earlier:
Texture modelling with non-contiguous filters,
ICVNZ15(1-6)
IEEE DOI 1701
BibRef
Earlier:
Learning generic third-order MGRF texture models,
IVCNZ13(7-12)
IEEE DOI 1402
Texture synthesis and analysis. Markov processes BibRef

Liu, N.[Ni], Gimel'farb, G.L.[Georgy L.], Delmas, P.[Patrice],
Learnable high-order MGRF models for contrast-invariant texture recognition,
CVIU(143), No. 1, 2016, pp. 135-146.
Elsevier DOI 1601
BibRef
Earlier:
Combined ternary patterns for texture recognition,
ICVNZ15(1-6)
IEEE DOI 1701
BibRef
And:
Learning High-Order Structures for Texture Retrieval,
GbRPR15(365-374).
Springer DOI 1511
BibRef
Earlier:
Texture modelling with generic translation- and contrast/offset-invariant 2nd-4th-order MGRFs,
IVCNZ13(370-375)
IEEE DOI 1402
image classification. Markov processes. High-order ordinal MGRF BibRef

Ali, A.M.[Asem M.], Farag, A.A.[Aly A.], Gimel'farb, G.L.[Georgy L.],
Analytical method for MGRF Potts model parameter estimation,
ICPR08(1-4).
IEEE DOI 0812
Markov Gibbs Random Field BibRef


Zhao, H.X.[Hui-Xi], Comer, M.L.[Mary L.], De Graef, M.[Marc],
A unified Markov random field/marked point process image model and its application to computational materials,
ICIP14(6101-6105)
IEEE DOI 1502
Computational modeling BibRef

Feng, S.W.[Si-Wei], Itoh, Y.[Yuki], Parente, M.[Mario], Duarte, M.F.[Marco F.],
Tailoring non-homogeneous Markov chain wavelet models for hyperspectral signature classification,
ICIP14(5167-5171)
IEEE DOI 1502
Computational modeling BibRef

Nizar, B.[Bouhlel], Laugier, P.[Pascal],
Ultrasound tissue characterizationby generalized GAMMA MRF model,
ICIP14(2266-2270)
IEEE DOI 1502
Acoustics BibRef

Simmons, J.[Jeff], Przybyla, C.[Craig], Bricker, S.[Stephen], Kim, D.W.[Dae Woo], Comer, M.[Mary],
Physics of MRF regularization for segmentation of materials microstructure images,
ICIP14(4882-4886)
IEEE DOI 1502
Image segmentation BibRef

Fix, A.[Alexander], Agarwal, S.[Sameer],
Duality and the Continuous Graphical Model,
ECCV14(III: 266-281).
Springer DOI 1408
BibRef

Jiang, F.[Feng], Wang, X.[Xulin], Zhao, D.B.[De-Bin],
From relation between filter-based MRFs model and sparsity based method to the pursuit of natural images space,
ICIP13(93-97)
IEEE DOI 1402
Adaptation models BibRef

Haindl, M.[Michal], Remes, V.[Vaclav], Havlicek, V.[Vojtech],
Potts compound Markovian texture model,
ICPR12(29-32).
WWW Link. 1302
BibRef

Fix, A.[Alexander], Chen, J.[Joyce], Boros, E.[Endre], Zabih, R.[Ramin],
Approximate MRF Inference Using Bounded Treewidth Subgraphs,
ECCV12(I: 385-398).
Springer DOI 1210
BibRef

Gao, Q.[Qi], Roth, S.[Stefan],
How Well Do Filter-Based MRFs Model Natural Images?,
DAGM12(62-72).
Springer DOI 1209
BibRef

Mei, T., Zheng, L., Zhong, S.,
A Joint Pixel and Region Based Multiscale Markov Random Field for Image Classification,
ISPRS12(XXXIX-B3:237-242).
DOI Link 1209
BibRef

Welikanna, D.R., Tamura, M., Tolpekin, V.A., Susaki, J., Maki, M.,
Improving Markov Random Field Based Super Resolution Mapping Through Fuzzy Parameter Integration,
AnnalsPRS(I-7), No. 2012, pp. 183-189.
HTML Version. 1209
BibRef

Lin, D.[Dahua], Fisher, J.W.[John W.],
Manifold guided composite of Markov random fields for image modeling,
CVPR12(2176-2183).
IEEE DOI 1208
BibRef

Lin, D.[Dahua], Fisher, J.W.[John W.],
Low level vision via switchable Markov random fields,
CVPR12(2432-2439).
IEEE DOI 1208
BibRef

Colonnese, S.[Stefania], Rinauro, S.[Stefano], Scarano, G.[Gaetano],
Markov Random Fields using complex line process: An application to Bayesian image restoration,
EUVIP11(30-35).
IEEE DOI 1110
See also Bayesian image interpolation using Markov random fields driven by visually relevant image features. BibRef

Schoenemann, T.[Thomas],
Minimizing Count-Based High Order Terms in Markov Random Fields,
EMMCVPR11(17-30).
Springer DOI 1107
BibRef

Tsuboi, Y.[Yuta], Kashima, H.[Hisashi],
A new objective function for sequence labeling,
ICPR08(1-4).
IEEE DOI 0812
discriminative learning of Markov random fields BibRef

Zhou, H.B.[Hong-Bo], Zheng, Z.M.[Zhi-Ming],
Generalized criteria for uniqueness of Gibbs measures,
ICPR08(1-4).
IEEE DOI 0812
BibRef

He, C.[Chu], Ahonen, T.[Timo], Pietikainen, M.[Matti],
A Bayesian Local Binary Pattern texture descriptor,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Sargin, M.E., Altinok, A., Rose, K., Manjunath, B.S.,
Conditional iterative decoding of Two Dimensional Hidden Markov Models,
ICIP08(2552-2555).
IEEE DOI 0810
BibRef

Poon, H.F.[Hoi-Fung], Domingos, P.[Pedro],
Sum-product networks: A new deep architecture,
SIG11(689-690).
IEEE DOI 1201
For partitioning. BibRef

Domingos, P.[Pedro], Kok, S.[Stanley], Lowd, D.[Daniel], Poon, H.F.[Hoi-Fung], Richardson, M.[Matt], Singla, P.[Parag], Sumner, M.[Marc], Wang, J.[Jue],
Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition,
SSPR08(3).
Springer DOI 0812
BibRef

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

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

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

Pichler, A., Fisher, R.B., Vincze, M.,
Decomposition of range images using markov random fields,
ICIP04(II: 1205-1208).
IEEE DOI 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 DOI 0307
demonstrate 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 DOI 0312
BibRef

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

Kim, J.[Junhwan], Zabih, R.[Ramin],
Factorial Markov Random Fields,
ECCV02(III: 321 ff.).
Springer DOI 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 DOI 0108
BibRef

Müller, S., Wallhoff, F., Rigoll, G.,
Retrieval of Overlapping and Touching Objects Using Hidden Markov Models,
ICIP01(II: 761-764).
IEEE DOI 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: 518-521).
IEEE DOI 0009
BibRef

Sivakumar, K.,
A Morphological Estimator for Clique Potentials of Binary Markov Random Fields,
ICIP00(Vol I: 264-267).
IEEE DOI 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).
IEEE DOI
IEEE DOI 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).
Springer DOI 9709
BibRef

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

Tanaka, K., Ichioka, M., Morita, T.,
Statistical-Mechanical Algorithm in MRF Model Based on Variational Principle,
ICPR96(II: 381-388).
IEEE DOI 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).
IEEE DOI 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).
IEEE DOI 9608
(Univ. de Nantes, F) BibRef

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

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

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

Trumbo, M., Vaisey, J.,
Variable decay rate histogram modelling for image compression,
ICIP95(III: 416-419).
IEEE DOI 9510
BibRef
And:
Variable resolution Markov modelling of signal data for image compression,
ICIP95(I: 282-285).
IEEE DOI 9510
BibRef

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

Devijver, P.A.,
Real-time modeling of image sequences based on hidden Markov mesh random field models,
ICPR90(II: 194-199).
IEEE DOI 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).
IEEE DOI 8811
BibRef

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

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

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


Last update:Dec 15, 2017 at 20:32:53