Shape Matching and Image Segmentation Using Stochastic Labeling

Abstract

Abstract : New results are presented in the areas of shape matching of nonoccluded and occluded objects in two dimensions, surface approximation by polygons, shape matching of objects in three dimensions, and segmentation of images having unimodal distributions. The same stochastic labeling technique is used in both shape matching and segmentation with various extensions. Shape matching is viewed as a segment matching problem. Unlike the previous work in shape matching of 2-D objects, the technique is based on a stochastic labeling procedure which explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification. To reduce the computation time, the technique is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels. This basic technique has been extended to the situation where various objects partially occlude each other to form an apparent object and our interest is to find all the objects participating in the occlusion. In such a case several hierarchical processes are executed in parallel for every participating object in the occlusion and are coordinated in such a way that the same segment of the apparent object is not matched to the segments of different actual objects. These techniques have been applied to two-dimensional shapes represented by polygons and the power of the techniques is demonstrated by the examples taken from synthetic, aerial, industrial and microscope images, where the matching is done after using the actual segmentation methods.