Fast Unsupervised Segmentation Using Active Contours and Belief Functions

In this paper, we study Active Contours (AC) based globally segmentation for vector valued images using evidential Kullback-Leibler (KL) distance. We investigate the evidential framework to fuse multiple features issued from vector-valued images. This formulation has two main advantages: 1) by the combination of foreground/background issued from the multiple channels in the same framework. 2) the incorporation of the heterogeneous knowledge and the reduction of the imprecision due to the noise. The statistical relation between the image channels is ensured by the Dempster-Shafer rule. We illustrate the performance of our segmentation algorithm using some challenging color and textured images.