Dempster-Shafer fusion of multisensor signals in nonstationary Markovian context

Eurasip Journal on Advances in Signal Processing

Volumn 2012, Issue 1, 2012, Pages

  Boudaren, Mohamed El Yazid ^a,b,c   Monfrini, Emmanuel ^b   Pieczynski, Wojciech ^b   Aïssani, Amar ^c  

^a ECOLE MILITAIRE POLYTECHNIQUE   (Algeria)

^b INSTITUT NATIONAL DES TÉLÉCOMMUNICATIONS   (France)

^c UNIVERSITY OF SCIENCES AND TECHNOLOGY HOUARI BOUMEDIENE   (Algeria)

Author keywords

Data fusion; Dempster Shafer combination rule; Hidden Markov chains; Multisensor data; Multisensor Markov chains; Nonstationary data; Theory of evidence; Triplet markov chains

Indexed keywords

DEMPSTER-SHAFER COMBINATION RULES; HIDDEN MARKOV CHAINS; MULTI SENSOR; MULTI-SENSOR DATA; NONSTATIONARY DATA; THEORY OF EVIDENCE; TRIPLET MARKOV CHAINS;

BAYESIAN NETWORKS; DATA FUSION; HIDDEN MARKOV MODELS; IMAGE SEGMENTATION;

SIGNAL RECONSTRUCTION;

EID: 84887073204     PISSN: 16876172     EISSN: 16876180     Source Type: Journal    
DOI: 10.1186/1687-6180-2012-134     Document Type: Article

References (32)

1. Raphael C: Automatic segmentation of acoustic musical signals using hidden Markov models. IEEE Trans Pattern Anal Mach Intell 1999,21(4):360-370.
2. Chen M, Kundu A, Zhou J: Off-line handwritten word recognition using a hidden Markov model type stochastic network. IEEE Trans Pattern Anal Mach Intell 1994,16(5):481-496.
3. Cappé O, Moulines E, Ryden T: Inference in Hidden Markov Models. Springer, New York; 2005.
4. Koski T: Hidden Markov Models for Bioinformatics. Kluwer Academic Publishers, Dordrecht; 2001.
5. Thomas LC, Allen DE, Morkel-Kingsbury N: A hidden Markov chain model for the term structure of bond credit risk spreads. Int Rev Financ Anal 2002,11(3):311-329.
6. Le Ber F, Benoît M, Schott C, Mari J-F, Mignolet C: Studying crop sequences with Carrot Age, a HMM based data mining software. Ecol Model 2006,191(1):170-185. (Pubitemid 41735461)
7. Baum LE, Petrie T, Soules G, Weiss N: A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann Math Stat 1970, 41:164-171.
8. Fornay GD: The Viterbi algorithm. Proc IEEE 1973,61(3):268-277.
9. Rabiner LR: A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 1989,77(2):257-286.
10. McLachlan G, Krishnan T: EM Algorithm and Extensions. Wiley, New York; 1997.
11. Delmas J-P: An equivalence of the EM and ICE algorithm for exponential family. IEEE Trans Signal Process 1997,45(10):2613-2615.
12. Lanchantin P, Pieczynski W: Unsupervised restoration of hidden non stationary Markov chain using evidential priors. IEEE Trans Signal Process 2005,53(8):3091-3098. (Pubitemid 41372818)
13. Shafer G: A Mathematical Theory of Evidence. Princeton University Press, Princeton; 1976.
14. Pieczynski W: Multisensor triplet Markov chains and theory of evidence. Int J Approx Reason 2007,45(1):1-16. (Pubitemid 46617870)
15. Pieczynski W, Benboudjema D: Multisensor triplet Markov fields and theory of evidence. Image Vis Comput 2006,24(1):61-69. (Pubitemid 43063222)
16. Bloch I: Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recognit Lett 1996,17(8):905-919. (Pubitemid 126384194)
17. Cobb BR, Shenoy PP: A Comparison of Methods for Transforming Belief Function Models to Probability Models, vol. 2711. Springer-Verlag, Berlin; 2003.
18. Denoeux T: Analysis of evidence-theoristic decision rules for pattern classification. Pattern Recognit 1997,30(7):1095-1107. (Pubitemid 127406517)
19. Fouque L, Appriou A, Pieczynski W: An evidential Markovian model for data fusion and unsupervised image classification, 3rd International Conference on Information Fusion (FUSION). France, Paris; 2000:pp. 25-31.
20. Janez F, Appriou A: Theory of evidence and non-exhaustive frames of discernment: plausibilities correction methods. Int J Approx Reason 1998,18(1):1-19. (Pubitemid 128393701)
21. Smets P: Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. Int J Approxim Reason 1993,9(1):1-35.
22. Sudano JJ: Equivalence between belief theories and naive Bayesian fusion for systems with independent evidential data, Sixth International Conference on Information Fusion (FUSION). Cairns, Australia; 2003:pp. 1357-1364.
23. Yager RR, Kacprzyk J, Fedrizzi M: Advances in the Dempster-Shafer Theory of Evidence. Wiley, New York; 1994.
24. Bendjebbour A, Delignon Y, Fouque L, Samson V, Pieczynski W: Multisensor images segmentation using Dempster-Shafer fusion in Markov fields context. IEEE Trans Geosci Remote Sens 2001,39(8):1789-1798. (Pubitemid 32935693)
25. Lanchantin P, Lapuyade-Lahorgue J, Pieczynski W: Unsupervised segmentation of randomly switching data hidden with non-Gaussian correlated noise. Signal Process 2011,91(2):163-175.
26. Derrode S, Pieczynski W: Signal and image segmentation using Pairwise Markov chains. IEEE Trans Signal Process 2004,52(9):2477-2489.
27. Boudaren MY, Pieczynski W, Monfrini E: Unsupervised segmentation of non-stationary data hidden with non-stationary noise, IEEE International Workshop on Systems, Signal Processing and their Applications (WoSSPA). Tipasa, Algeria; 2011:pp. 255-258.
28. Lapuyade-Lahorgue J, Pieczynski W: Unsupervised segmentation of hidden semi-Markov non stationary chains. Signal Process 2012,92(1):29-42.
29. Hégarat-Mascle SL, Bloch I, Vidal-Madjar D: Introduction of neighborhood information on evidence theory and application to data fusion of radar and optical images with partial cloud cover. Pattern Recognit 1998,31(11):1811-1823. (Pubitemid 29007528)
30. Pieczynski W: EM and ICE in hidden and triplet Markov models, Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA). Chania, Greece; 2010:pp. 1-9.
31. Fjortoft R, Delignon Y, Pieczynski W, Sigelle M, Tupin F: Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields. IEEE Trans Geosci Remote Sens 2003,41(3):675-686.
32. Willsky AS: Multiresolution Markov models for signal and image processing. Proc IEEE 2002,90(8):1396-1458. (Pubitemid 43785865)