Segmentation Via Multiple Looks Of Image Sequences Using The A Priori Probability Mask

A multiple-look segmentation technique for processing image sequences which uses a probability mask is presented. By designing the segmentation process in two stages; the probability mask formation, and the refinement by the Maximum A Posteriori(MAP) decision rule, a more reliable silhouette of a moving object is obtained for classification. Experimental results are presented showing the a priori probability mask formation and a comparison of the results of the Maximum Likelihood(ML) decision rule with those of the MAP decision rule. Here we assume that geometry(or shape) does not change within the "window" of looks required to perform the segmentation.