OPTIMAL SNAKE SEGMENTATION OF TARGET AND BACKGROUND WITH INDEPENDENT GAMMA-DENSITY PROBABILITIES, APPLICATION TO SPECKLED AND PREPROCESSED IMAGES

We propose in this paper a snake-based segmentation processor to track the shape of a target with random white intensity appearing on a rand om white spatially disjoint background. We study the optimal solution for Gamma laws and we discuss the relevance of such statistics for rea listic situations. This algorithm, based on an active contour model (s nakes), consists in correlations of a binary reference with the scene image or with pre-processed version of the scene image. This method is a generalization of correlation techniques and thus opens new applica tions for digital and optical correlators.