Unsupervised image segmentation using Markov random field models

Pattern Recognition

Volumn 33, Issue 4, 2000, Pages 587-602

  Barker, S A ^a,b   Rayner, P J W ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b NUANCE COMMUNICATIONS   (United States)

Author keywords

Markov chain; Markov random field; Monte Carlo; Reversible jump; Simulated annealing; Unsupervised segmentation

Indexed keywords

ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; IMAGE ANALYSIS; IMAGE QUALITY; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS;

HIERARCHICAL MARKOV RANDOM FIELD MODELS; UNSUPERVISED SEGMENTATION ALGORITHMS;

IMAGE SEGMENTATION;

EID: 0033874010     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(99)00074-6     Document Type: Article

References (22)

1. M.A. Tanner, Tools for Statistical Inference, Springer, Berlin, 1993.
2. B.S. Manjunath, R. Chellappa, Unsupervised texture segmentation using Markov random fields, IEEE Trans. Pattern Anal. Machine Intell. 13 (5) (1991) 478-482.
3. F.S. Cohen, Z. Fan, Maximum likelihood unsupervised texture segmentation, CVGIP: Graphical Models and Image Processing 54 (3) (1992) 239-251.
4. H.H. Nguyen, P. Cohen, Gibbs random fields, fuzzy clustering, and the unsupervised segmentation of images, CVGIP: Graphical Models and Image Processing 55 (1) (1993) 1-19.
5. C. Graffigne, D. Geman, S. Geman, P. Dong, Boundary detection by constrained optimization, IEEE Trans. Pattern Anal. Machine Intell. 12 (7) (1990) 609-628.
6. C. Kervrann, F. Heitz, A Markov random field model-based approach to unsupervised texture segmentation using local and global statistics, IEEE Trans. Image Processing 4 (6) (1995) 856-862.
7. D.K. Panjwani, G. Healey, Markov Random field models for unsupervised segmentation of textured color images, IEEE Trans. Pattern Anal. Machine Intell. 17 (10) (1995) 939-954.
8. C.S. Won, H. Derin, Unsupervised segmentation of noisy and textured images using Markov random fields, CVGIP: Graphical Models and Image Processing 54 (4) (1992) 308-328.
9. J.W. Modestino, D.A. Langan, J. Zhang, Cluster validation for unsupervised stochastic model-based image segmentation, Proc. ICIP94, 1994, pp. 197-201.
10. S. Geman, D. Geman, Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Machine Intell. 6 (6) (1984) 721-741.
11. J. Besag, Statistical analysis of non-lattice data, The Statistician 24 (3) (1975) 179-195.
12. J.M. Bernardo, A.F.M. Smith, Bayesian Theory, Wiley, New York, 1994.
13. J. Besag, On the statistical analysis of dirty pictures, J. Roy. Statist. Soc. Ser. B 48 (1986) 259-302.
14. B. Chalmond, An iterative gibbsian technique for reconstruction of m-ary images, Pattern Recognition 22 (6) (1989) 747-761.
15. L. Tierny, Markov chains for exploring posterior distributions, Ann. Statist. 22 (5) (1994) 1701-1762.
16. P.J. Green, Reversible jump Markov Chain Monte Carlo computation and Bayesian model determination, Biometrika 82 (4) (1996) 711-732.
17. P. Billingsley, Measure Theory, Addison-Wesley, Reading, MA, 1960.
18. S. Richardson, P.J. Green, On Bayesian analysis of mixtures with an unknown number of components, J. Roy. Statist. Soc. Ser. B 59 (4) (1997) 731-792.
19. R. Srichander, Efficient schedules for simulated annealing, Eng. Optim. 24 (1995) 161-176.
20. P.N. Strenski, S. Kirkpatrick, Analysis of finite length annealing schedules, Algorithmica 6 (1991) 346-366.
21. R. Chellappa, L.M. Kanal, A. Rosenfeld, (Eds.), Two-dimensional discrete Gaussian Markov random field models for image processing, Progress in Pattern Recognition, vol. 2, Elsevier, Amsterdam, 1985, pp. 79-112.
22. R.J. Baxter, Exactly solved models in statistical mechanics, Academic Press, London, 1982.