Abstract
This paper describes ways to enhance the process of object recognition by providing an object recognition system with the abilities to anticipate the occurrences of types of objects in an image and to acquire the information needed to form such anticipations. Two heuristics have been identified in our study. Following the recency heuristic, one anticipates the reappearance in the near future of (types of) objects encountered 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 kind of object. If these anticipations hold true, then a significant number of the regions in the image can be accounted for with a severely pruned search space, leaving a problem significantly reduced in complexity. We borrow from probability theory to develop a notion of conditional anticipation used by the co-occurrence heuristic, and relate the use of co-occurrence information to the psychology of learning and memory. The proposed approach has been implemented and evaluated on the knowledge-based object recognition system (KOREL). KOREL automatically acquires models of object views and recognizes 3D objects in 2D digitized line drawing images even in the presence of modest occlusion. Experimental results on the enhanced recognition process are presented.
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