Reasoning about Occlusions during Hypothesis Verification

In this paper we study the limitations of current verification strategies in object recognition and suggest how they may be enhanced. On the whole object topology is exploited little during verification. In practice, understanding the connectivity relationships between features in the image, or on the object, can lead to significantly more accurate evaluations of recognition hypotheses. We study how topology reasoning allows us to hypothesize the presence of occlusions in the image. Analysis of these hypotheses provides information which turns out to be crucial to the quality of our overall verification results.