Extension of higher-order HMC modeling with application to image segmentation

Digital Signal Processing: A Review Journal

Volumn 18, Issue 5, 2008, Pages 849-860

  Benyoussef, Lamia ^a   Carincotte, Cyril ^b   Derrode, Stéphane ^a  

^a Ecole Centrale Marseille   (France)

^b Multitel   (Belgium)

Author keywords

Higher order hidden Markov chain; Iterative conditional estimation; Maximal posterior mode; Unsupervised image segmentation

Indexed keywords

CHAINS; ITERATIVE METHODS; MARKOV PROCESSES; SIGNAL THEORY; SYNTHETIC APERTURE RADAR;

CLASSIFICATION ALGORITHM; HIDDEN MARKOV CHAINS; HIGHER ORDER MODELING; ITERATIVE CONDITIONAL ESTIMATION; MAXIMAL POSTERIOR MODE; MODEL PARAMETERS ESTIMATION; SYNTHETIC APERTURE RADAR (SAR) IMAGES; UNSUPERVISED IMAGE SEGMENTATION;

IMAGE SEGMENTATION;

EID: 47249166117     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2007.10.010     Document Type: Article

References (32)

1. Geman S., and Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intell. 6 6 (1984) 721-741
2. Besag J. On the statistical analysis of dirty pictures. J. R. Statist. Soc. 48 (1986) 259-302
3. Marroquin J., Mitter S., and Poggio T. Probabilistic solution of ill-posed problems in computational vision. J. Acoust. Soc. Am. 82 (1987) 76-89
4. Bunke H. Hidden Markov Models: Applications in Computer Vision. In: Bunke H., and Caelli T. (Eds). Machine Perception and Artificial Intelligence, vol. 45 (2001), World Scientific, Singapore
5. Chalmond B. Modeling and Inverse Problems in Image Analysis. Applied Mathematical Sciences vol. 155 (2003), Springer-Verlag, Berlin
6. Winkler G. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction. Applications of Mathematics-Stochastic Modeling and Applied Probability. second ed. vol. 27 (2003), Springer-Verlag, Berlin
7. Descombes X., Moctezuma M., Maître H., and Rudant J.-P. Coastline detection by a Markovian segmentation on SAR images. Signal Process. 55 1 (1996) 123-132
8. Smits P., and Dellepiane S. Synthetic aperture radar image segmentation by a detail preserving Markov random field approach. IEEE Trans. Geosci. Remote Sens. 35 4 (1997) 844-857
9. Kasetkasem T., and Varshney P. An image change detection algorithm based on Markov random field models. IEEE Trans. Geosci. Remote Sens. 40 8 (2002) 1815-1823
10. Pieczynski W. Statistical image segmentation. Machine Graphics Vision 1 (1992) 261-268
11. Younes L. Estimation and annealing for Gibbsian fields. Ann. Inst. H. Poincaré 24 2 (1988) 269-294
12. Celeux G., Forbes F., and Peyrard N. EM procedures using mean field-like approximations for Markov model-based image segmentation. Pattern Recogn. 36 1 (2003) 131-144
13. Giordana N., and Pieczynski W. Estimation of generalized multisensor hidden Markov chain and unsupervised image segmentation. IEEE Trans. Pattern Anal. Machine Intell. 19 5 (1997) 465-475
14. Skarbek W. Generalized Hilbert scan in image printing. Theoretical Foundations of Computer Vision (1992), Akademie-Verlag, Berlin
15. Fjørtoft R., Delignon Y., Pieczynski W., Sigelle M., and Tupin F. Unsupervised segmentation of radar images using hidden Markov chain and hidden Markov random field. IEEE Trans. Geosci. Remote Sens. 41 3 (2003) 675-686
16. Rabiner L.R. A tutorial on HMMs and selected applications in speech recognition. Proc. IEEE 77 2 (1989) 257-286
17. Levinson S.E. Continuously variable duration hidden Markov models for automatic speech recognition. Comput. Speech Lang. 1 1 (1986) 29-45
18. Jordan Z.G., and Jordan M.I. Factorial hidden Markov models. Machine Learn. 29 (1997) 245273
19. Weber S.B.K., Ikbal S., and Bourlard H. Robust speech recognition and feature extraction using HMM2. Comput. Speech Lang. 17 2-3 (2003) 195-211
20. Berchtold A. The double chain Markov model. Commun. Statist. Theory Methods 28 (1999) 2569-2589
21. Derrode S., and Pieczynski W. Signal and image segmentation using pairwise Markov chains. IEEE Trans. Signal Process. 52 9 (2004) 2477-2489
22. Pieczynski W. Multisensor triplet Markov chains and theory of evidence. Int. J. Approx. Reason. 45 1 (2007) 1-16
23. E. de Villiers, J. du Preez, The advantage of using higher-order HMM for segmenting acoustic files, in: 12th Annual Symposium of the Pattern Recognition Association of South Africa, Franschhoek, South Africa, November 2001
24. Mari J., Haton J., and Kriouille A. Automatic word recognition based on second-order HMMs. IEEE Trans. Speech Audio Process. 5 1 (1997) 22-25
25. Boys R., and Henderson D. A comparison of reversible jump MCMC algorithms for DNA sequence segmentation using HMMs. Comp. Sci. Stat. 33 (2002) 1-15
26. O. Aycard, J.-F. Mari, F. Washington, Learning to automatically detect features for mobile robots using second-order HMMs, in: Proc. of the Workshop on Reasoning with Uncertainty in Robotics, Acapulco, Mexico, July 2003
27. Jelinek F. Statistical Methods for Speech Recognition. Language, Speech and Communication (1997), MIT Press, Cambridge, MA
28. Baum L., Petrie T., Soules G., and Weiss N. A maximization technique occuring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Statist. 41 (1970) 164-171
29. Devijver P. Baum's forward-backward algorithm revisited. Pattern Recogn. Lett. 3 (1985) 369-373
30. F. Girard-Ardhuin, G. Mercier, R. Garello, Oil slick detection by SAR imagery: Potential and limitation, in: Marine Technology and Ocean Science Conference (OCEANS'03), San Diego, 22-26 September 2003, pp. 22-26
31. Derrode S., and Mercier G. Multiscale oil slick segmentation from SAR image using a vector HMC model. Pattern Recogn. 40 3 (2007) 1135-1147
32. S. Derrode, G. Mercier, J.-M. LeCaillec, R. Garello, Estimation of sea-ice SAR clutter statistics from Pearson system of distributions, in: IEEE International Geoscience and Remote Sensing Symposium (IGARSS'01), Sydney, Australia, 9-13 July 2001