Applying uncertainty reasoning to model based object recognition

An architecture for reasoning with uncertainty about the identities of objects in a scene is described. The main components of this architecture create and assign credibility to object hypotheses based on feature-match, object, relational, and aspect consistencies. The Dempster-Shafer formalism is used for representing uncertainty, so these credibilities are expressed as belief functions which are combined using Dempster's combination rule to yield the system's aggregate belief in each object hypothesis. One of the principal objections to the use of Dempster's rule is that its worst-case time complexity is exponential in the size of the hypothesis set. The structure of the hypothesis sets developed by this system allow for a polynomial implementation of the combination rule. Experimental results affirm the effectiveness of the method in assessing the credibility of candidate object hypotheses.<
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