Unsupervised segmentation of multisensor images using generalized hidden Markov chains

This work addresses the problem of unsupervised multisensor 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 finite 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 difficulty: 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.