Unsupervised Dempster-Shafer fusion of dependent sensors

This paper deals with the problem of statistical unsupervised fusion of dependent sensors with its potential applications to multisensor image segmentation. On the one hand, Bayesian fusions can be of great efficiency, particularly when using hidden Markov models. On the other hand, we give some examples showing that there are situations in which the Dempster-Shafer fusion can be usefully integrated into the classical Bayesian models. The contribution of this paper is then to show how a recent parameter estimation of probabilistic models, valid in the dependent and possible non-Gaussian sensors case, can be extended to situations in which some of the sensors can be evidential. The proposed method allows one to imagine different unsupervised segmentation methods, valid in the Dempster-Shafer framework for dependent and possibly non-Gaussian sensors.