Unsupervised Image Segmentation Using Dempster- Shafer Fusion in a Markov Fields Context

The Dempster-Shafer combination rule can be of great utility in multisensor image segmentation. In addition, the approach based on theory of evidence can be seen as generalizations of the classical Bayesian approach, which is often used in the Hidden Markov Field Model context. Finally, some recent works allow one to use the Dempster - Shafer combination rule in the Markovian context, and different methods so obtained can greatly improve the effectiveness of Markovian methods working alone. The aim of this paper is to make these methods unsupervised by proposing some parameter estimation algorithms. In order to do so, we use some recent methods of generalized mixture estimation, which allows one to estimate mixtures in which the exact nature of components is not known.