Joint Segmentation of Piecewise Constant Autoregressive Processes by Using a Hierarchical Model and a Bayesian Sampling Approach

Author

Dobigeon, Nicolas ; Tourneret, Jean-Yves ; Davy, Manuel

Author_Institution

IRIT/ENSEEIHT/TeSA, Toulouse

Volume

3

fYear

2006

fDate

14-19 May 2006

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

Keywords

Bayes methods; autoregressive processes; sensor fusion; signal sampling; Bayesian inference; Bayesian sampling approach; Gibbs sampling strategy; hierarchical model; piecewise constant autoregressive processes; segmentation algorithm; sensors; signal-by-signal segmentation; Autoregressive processes; Bayesian methods; Image processing; Image sampling; Image segmentation; Inference algorithms; Parameter estimation; Sampling methods; Signal processing; Signal sampling;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on

Conference_Location

Toulouse

ISSN

1520-6149

Print_ISBN

1-4244-0469-X

Type

conf

DOI

10.1109/ICASSP.2006.1660575

Filename

1660575