ENHANCING OBJECT RECOGNITION USING REGENCY AND COOCCURRENCE HEURISTICS

This paper describes ways to enhance the process of object recognition by providing an object recognition system with the abilities to antic ipate the occurrences of types of objects in an image and to acquire t he information needed to form such anticipations. Two heuristics have been identified in our study. Following the recency heuristic, one ant icipates the reappearance in the near future of(types of) objects enco untered in the recent past. Given an occurrence of one kind of object, the co-occurrence heuristic sanctions anticipation of occurrences of other types of objects that have frequently occurred with the given ki nd of object. If these anticipations hold true, then a significant num ber of tine regions in the image can be accounted for with a severely pruned search space, leaving a problem significantly reduced in comple xity. We borrow from probability theory to develop a notion of conditi onal anticipation used by the cooccurrence heuristic, and relate the u se of co-occurrence information to the psychology of learning and memo ry. The proposed approach has been implemented and evaluated an the kn owledge-based object recognition system (KOREL). KOREL automatically a cquires models of object views and recognizes 3D objects in 2D digitiz ed line drawing images even in the presence of modest occlusion. Exper imental results on the enhanced recognition process are presented. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.