Shape Signature Matching for Object Identification Invariant to Image Transformations and Occlusion

Abstract

This paper introduces a novel shape matching approach for the automatic identification of real world objects in complex scenes. The identification process is applied on isolated objects and requires the segmentation of the image into separate objects, followed by the extraction of representative shape features and the similarity estimation of pairs of objects. In order to enable an efficient object representation, a novel boundary-based shape descriptor is introduced, formed by a set of one dimensional signals called shape signatures. During identification, the cross-correlation metric is used in a novel fashion to gauge the degree of similarity between objects. The invariance of the method to uniform-scaling and partial occlusion is achieved by considering both cases as possible scenarios when correlating shape signatures. The proposed vision system is robust to ambient conditions (partial occlusion) and image transformations (scaling, rotation, translation). The performance of the identifier has been examined in a great range of complex image and prototype object selections.