Estimation of generalized mixture in the case of correlated sensors.

This paper deals with unsupervised Bayesian classification of multidimensional data. We propose an extension of a previous method of generalized mixture estimation to the correlated sensors case. The method proposed is valid in the independent data case, as well as in the hidden Markov chain or field model case, with known applications in signal processing, particularly speech or image processing. The efficiency of the method proposed is shown via some simulations concerning hidden Markov fields, with application to unsupervised image segmentation.