Unsupervised segmentation of multisensor images using generalized hidden Markov chains

This work addresses the problem of unsupervised mul-tisensor image segmentation. We propose the use of a recent method which estimates parameters of generalized multisensor Hidden Markov Chains. A Hidden Markov Chain is said to be \generalized" when the exact nature of the noise components is not known; we assume however, that each of them belongs to a nite known set of families of distributions. The observed process is a mixture of distributions and the problem of estimating such a \generalized" mixture contains a supplementary diiculty: one has to label, for each state and each sensor, the exact nature of the corresponding distribution. The general ICE-TEST method recently proposed allows one to solve such problems.