| Paper: | SPTM-L1.1 | ||
| Session: | Bayesian Approaches and Particle Filters | ||
| Time: | Tuesday, May 16, 10:30 - 10:50 | ||
| Presentation: | Lecture | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, Classification Theory and Applications | ||
| Title: | Joint Segmentation of Piecewise Constant Autoregressive Processes by using a Hierarchical Model and a Bayesian Sampling Approach | ||
| Authors: | Nicolas Dobigeon, Jean-Yves Tourneret, IRIT / ENSEEIHT / TéSA, France; Manuel Davy, LAGIS, France | ||
| 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. | ||
©2018 Conference Management Services, Inc. -||- email: webmaster@icassp2006.org -||- Last updated Friday, August 17, 2012