Fuzzy Markov random fields versus chains for multispectral image segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 28, Issue 11, 2006, Pages 1753-1767

  Salzenstein, Fabien ^a,b   Collet, Christophe ^a,c  

^a IEEE   (France)

^b Faculté de Médecine   (France)

^c UNIVERSITÉ DE STRASBOURG   (France)

Author keywords

Fuzzy Markov chain; Fuzzy Markov field; Missing data; Multispectral image segmentation; Parameterized joint density

Indexed keywords

FUZZY MARKOV CHAIN; FUZZY MARKOV FIELD; MISSING DATA; MULTISPECTRAL IMAGE SEGMENTATION; PARAMETERIZED JOINT DENSITY;

ASTRONOMY; BAYESIAN NETWORKS; COVARIANCE MATRIX; DATA REDUCTION; FUZZY SETS; HIDDEN MARKOV MODELS;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; FUZZY LOGIC; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; PROBABILITY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; FUZZY LOGIC; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MARKOV CHAINS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 33947179842     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2006.228     Document Type: Article

References (29)

1. M. Louys, C. Bot, A. Oberto, and C. Collet, "Multiwavelength Image Analysis of the Small Magellanic Cloud Using Hierarchical Markovian Segmentation," Proc. Third Workshop Physics in Signal and Image Processing, pp. 29-31, Jan. 2003.
2. L.E. Baum, T. Petrie, G. Soules, and 'N. Weiss, "Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains," Ann. Math. Statistics, vol. 41, no. 1, pp. 164-171, 1970.
3. S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, pp. 721-741, 1984.
4. J.C. Bezdek, Pattern Recognition and Fuzzy Objective Function Algorithm. New York: Plenum Press, 1981.
5. I. Gath and A.B. Geva, "Unsupervised Optimal Fuzzy Clustering," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 773-781, July 1989.
6. W. Pedrycz, "Fuzzy Sets in Pattern Recognition: Methodology and Methods," Pattern Recognition, vol. 23, nos. 1/2, pp. 121-146, 1990.
7. L.A. Zadeh, "Fuzzy Sets," Information and Control, vol. 8, pp. 338-352, 1965.
8. H. Caillol, A. Hillion, and W. Pieczynski, "Fuzzy Random Fields and Unsupervised Image Segmentation," IEEE Trans. Geoscience Remote Sensing, vol. 31, no. 4, pp. 801-810, 1993.
9. J.T. Kent and K.V. Mardia, "Spatial Classification Using Fuzzy Membership," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, no. 5, pp. 659-671, 1988.
10. F. Salzenstein and W. Pieczynski, "Parameter Estimation in Hidden Fuzzy Markovian Fields and Image Segmentation," Graphics Models Processing, vol. 59, no. 4, pp. 205-220, 1997.
11. S. Ruan, B. Moretti, J. Fadilli, and D. Bloyet, "Fuzzy Markovien Segmentation in Application of Magnetic Resonance Images," Computer Vision and limage Understanding, vol. 85, pp. 54-69, 2002.
12. M. Germain, M. Voorons, J.M. Boucher, and G.B. Bénié, "Fuzzy Statistical Classification for Multiband Image Fusion," Proc. Fifth Int'l Conf. Information Fusion, vol. 1, pp. 178-184, July 2002.
13. J.-M. Laferte, P. Ferez, and F. Heitz, "Discrete Markov Image Modeling and Inference on the Quad-Tree," IEEE Trans. Image Processing, vol. 9, no. 3, pp. 390-404, 2000.
14. N. Giordana and W. Pieczynski, "Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 465475, May 1997.
15. J.-N. Provost, C. Collet, P. Rostaing, P. Ferez, and P. Bouthemy, "Segmentation of Multispectrnl Spot Images for the Reconstruction of Water Depth Maps," Computer Vision and linage Understanding, vol. 93, no. 2, pp. 155-174, Jan. 2004.
16. F. Salzenstein, C. Collet, and M. Petremand, "Champs de Markov Flous pour Imagerie Multispectrale-Fuzzy Markov Random Fields for Multispectral Images," Traitement du Signal, vol. 21, no. 1, pp. 37-55, 2004.
17. Y. Lin, W. Chcn, and F.H. Chan, "Multiclass Segmentation Based on Generalized Fuzzy Gibbs Random Fields," Proc. Int'l Conf. Image Processing (ICIP), Sept. 2003.
18. M.A. Mohammed and P. Gader, "Generalized Hidden Markov Models-Part I: Thebritical Frameworks," IEEE Trans. Fuzzy Systems, vol. 8, no. 1, pp. 67-81, 2000.
19. K. Avrachenkov and E. Sanchez, "Fuzzy Chains and Decision-Making," Fuzzy Optimization and Decision Making, vol. 1, no. 2, pp. 143-159, 2002.
20. C. Carincotte, S. Derrode, G. Sicot, and J.M. Boucher, "Unsuper-vised Image Segmentation Based on a New Fuzzy Hidden Markov Chain Model," Proc. IEEE Int'l Conf. Acoustics, Speech, Signal Processing (ICASSP), May 2004.
21. C. Carincotte, S. Derrode, and S. Bourennane, "Multivariate Fuzzy Hiddenmarkov Chains Model Applied to Unsupervised Multiscale SAR Image Segmentation," Proc. IEEE Int'l Conf. Fuzzy Systems, 2005.
22. R. Fjortoft, Y. Delignon, W. Pieczynski, M. Sigelle, and F. Tupin, "Unsupervised Segmentation of Radar Images Using Hidden Markov Chains and Hidden Markov Random Fields," IEEE Trans. Ccoscience and Remote Sensing, vol. 41, no. 3, pp. 675-686, 2003.
23. J. Maroquin, S. Mitte, and T. Poggio, " Probabilistic Solution of Ill-Posed Problems in Computational Vision," J. Am. Statistical Assoc., vol. 82, pp. 76-89, 1987.
24. D. Hubert, "Über die Stetige Abbildung einer Linie auf ein Flächenstück," Math. Ann., vol. 38, pp. 459-460, 1891.
25. B. Benmiloud and W. Pieczynski,'"Estimation des Paramtres dans les Chanes de Markov Caches et Segmentation d'Images,". Traitement du Signal, vol. 12, no. 5, pp. 433454, 1995.
26. W. Pieczynski, "Statistical Image Segmentation," Machine Graphics Vision, vol. 1, no. 1, pp. 261-268, 1992.
27. P. Devijver, "Baum's Forward Backward Algorithm Revisited," Pattern Recognition Letters, vol. 3, pp. 369-373, 1985.
28. L. Younes, "Parametric Inference for Imperfectly Observed Gibbsian Fields," Probability Theory and Related Fields, vol. 82, pp. 625-645, 1989.
29. M.S. Grouse, R.D. Novak, and R.G. Baraniuk, "Wavelet-Based Signal Processing Using Hidden Markov Models," JEEE Trans. Signal Processing, vol. 46, no. 4, pp. 886-902, 1998.