Stochastic relaxation on partitions with connected components and its application to image segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 20, Issue 6, 1998, Pages 619-636

  Jia Ping, Wang ^a,b  

^a UNIVERSITÉ DE CERGY PONTOISE   (France)

^b UNIVERSITÉ PARIS SACLAY   (France)

Author keywords

Connected components; Gibbs distribution; Hierarchical segmentation; Multiscale segmentation; Simulated annealing; Unsupervised segmentation

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; GRAPH THEORY; RANDOM PROCESSES; SIMULATED ANNEALING;

GENERALIZED METROPOLIS ALGORITHMS; GIBBS DISTRIBUTION; HASTINGS ALGORITHMS;

IMAGE SEGMENTATION;

EID: 0032097261     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.683775     Document Type: Article

References (35)

1. R. Azencott, Simulated Annealing, Seminaire Bourbaki, no. 697, 1987-1988.
2. R. Azencott, Image Analysis and Markov Fields, Proc. Int'l Conf. Ind. and Appl. Math, SIAM, Paris, 1987.
3. R. Azencott, J.-P. Wang, and L. Younes, Texture Classification Using Windowed Fourier Filters, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp. 148-153, Feb. 1997.
4. J. Besag, On the Statistical Analysis of Dirty Pictures, J. Royal Statistical Soc., vol. B48, pp. 259-302, 1986.
5. J.R. Beveridge, J.G. Griffith, R.R. Kohler, A.R. Hanson, and E.M. Riseman, Segmenting Images Using Localized Histograms and Region Merging, Int'l J. Computer Vision, vol. 2, pp. 311-347, 1989.
6. C. Bouman and B. Liu, Multiple Resolution Segmentation of Textured Images, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, pp. 99-113, Feb. 1991.
7. O. Catoni, Rough Large Deviation Estimates for Simulated Annealing: Application to Exponential Schedules, Annals of Probability, vol. 20, pp. 1,109-1,146, 1992.
8. P.C. Chen and T. Pavlidis, Segmentation by Texture Using a Cooccurrence Matrix and a Split-and-Merge Algorithm, Computer Graphics and Image Processing, vol. 10, pp. 172-182, 1979.
9. D.B. Cooper, H. Elliott, F. Cohen, L. Reiss, and P. Symosek, Stochastic Boundary Estimation and Object Recognition, Computer Graphics and Image Processing, vol. 12, pp. 326-356, 1980.
10. H. Derin and H. Elliott, Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, pp. 39-55, Jan. 1987.
11. A. Gagalowicz and C. Graffigne, Blind Texture Segmentation, Proc. Ninth Int'l Conf. Pattern Recognition, Rome, 1988.
12. D. Geman and S. Geman, Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, pp. 721-741, Nov. 1984.
13. D. Geman, S. Geman, C. Graffigne, and P. Dong, Boundary Detection by Constrained Optimization, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 609-628, July 1990.
14. S. Geman and C. Graffigne, Markov Random Field Image Models and Their Applications to Computer Vision, A.M. Gleason, ed., Proc. Int'l Congress of Mathematicians 1986. Providence, R.I.: Am. Math. Soc., 1987.
15. B. Hajek, Cooling Schedules for Optical Annealing, Mathematics of Operations Research, vol. 13, no. 2, May 1988.
16. J.M. Hammersley and D.C. Handscomb, Monte Carlo Methods. London: Methuen and Company, 1964.
17. W.K. Hastings, Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika, vol. 57, pp. 97-109, 1970.
18. F. Heitz, P. Pérez, and P. Bouthemy, Multiscale Minimization of Global Energy Function in Some Visual Recovery Problems, CVGIP: Image Understanding, vol. 59, no. 1, 1994.
19. S.L. Horowitz and T. Pavlidis, Picture Segmentation by a Tree Traversal Algorithm, J. ACM, vol. 23, pp. 368-388, 1976.
20. R. Hu and M.M. Fahmy, Texture Segmentation Based on a Hierarchical Markov Random Field Model, Signal Processing, vol. 26, pp. 285-305, 1992.
21. A.K. Jain and F. Farrokhnia, Unsupervised Texture Segmentation Using Gabor Filters, Pattern Recognition, vol. 24, no. 12, pp. 1,167-1,186, 1991.
22. C. Kervrann and F. Heitz, A Markov Random Field Model-Based Approach to Unsupervised Texture Segmentation Using Local and Global Statistics, IEEE Trans. Image Processing, vol. 4, no. 6, pp. 856-862, June 1995.
23. A. Khotanzad and J.-Y. Chen, Unsupervised Segmentation of Textured Images by Edge Detection in Multidimensional Features, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, pp. 414-421, Apr. 1989.
24. G. Koepfler, C. Lopez, and J.M. Morel, A Multiscale Algorithm for Image Segmentation by Variational Method, SIAM J. Numerical Analysis, vol. 31, no. 1, pp. 282-299, Feb. 1994.
25. S. Lakshmanan and H. Derin, Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, pp. 799-813, Aug. 1989.
26. B.S. Manjunath and R. Chellappa, Unsupervised Texture Segmentation Using Markov Random Field Models, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 5, pp. 478-482, May 1991.
27. D. Mumford and J. Shah, Optimal Approximations by Piecewise Smooth Functions and Associated Variational Problems, Comm. Pure and Applied Math., vol. 17, pp. 577-685, 1989.
28. P.A. Nagin, A.R. Hanson, and E.M. Riseman, Studies in Global and Local Histogram-Guided Relaxation Algorithms, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 4, pp. 263-277, May 1982.
29. H.H. Nguyen and P. Cohen, Gibbs Random Fields, Fuzzy Clustering, and the Unsupervised Segmentation of Textured Images, CVGIP: Graphical Models and Image Processing, vol. 55, no. 1, pp. 1-19, Jan. 1993.
30. P. Pérez and F. Heitz, Restriction of a Markov Random Field on a Graph and Multiresolution Image Analysis, IEEE Trans. Information Theory, vol. 42, no. 1, pp. 180-190, Jan. 1996.
31. W.K. Pratt, Digital Image Processing. New York: Wiley-Interscience, 1991.
32. A. Rosenfeld and A.C. Kak, Digital Picture Processing, vol. 2. New York: Academic Press, 1982.
33. J.-P. Wang, Multiscale Markov Fields: Applications to the Segmentation of Textured Images and Film Fusion (in French), PhD dissertation, Paris-Sud Univ., 1994.
34. J.-P. Wang, Stochastic Relaxation on Partitions With Connected Components and Its Application to Image Segmentation, preprint, CMLA, Ecole Normale Supérieure de Cachan, France.
35. C.S. Won and H. Derin, Unsupervised Segmentation of Noisy and Textured Images Using Markov Random Fields, CVGIP: Graphical Models and Image Processing, vol. 54, no. 4, pp. 308-328, July 1992.