Abstract : We propose a joint segmentation algorithm for piecewise constant AR processes recorded by several independent sensors. The algorithm is based on a hierarchical Bayesian model. Appropriate priors allow to introduce correlations between the change locations of the observed signals. Numerical problems inherent to Bayesian inference are solved by a Gibbs sampling strategy. The proposed joint segmentation methodology provides interesting results compared to a signal-by-signal segmentation.
https://hal.inria.fr/inria-00119997
Contributeur : Manuel Loth
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Soumis le : mardi 12 décembre 2006 - 16:41:33
Dernière modification le : mercredi 23 mai 2018 - 17:58:05
Document(s) archivé(s) le : mercredi 7 avril 2010 - 00:28:47
Nicolas Dobigeon, Jean-Yves Tourneret, Manuel Davy. JOINT SEGMENTATION OF PIECEWISE CONSTANT AUTOREGRESSIVE PROCESSES BY USING A HIERARCHICAL MODEL AND A BAYESIAN SAMPLING APPROACH. IEEE ICASSP 2006, 2006, Toulouse, France. 2006. 〈inria-00119997〉
