This research features probabilistic hypothesis generation based on indexing approach for the rapid recognition of three dimensional objects. A major concern in practical vision systems is how to retrieve the best matched models without exploring all possible object matches. We have employed a Bayesian framework to achieve efficient indexing of model objects. A decision-theoretic measure of the discriminatory power of a feature for a model object is defined in terms of a posteriori probability. This measure displays belief that a model appears in the scene after a feature is observed. We compute off-line the discriminatory power of features for model objects from CAD model data using computer graphic techniques. In order to speed up the indexing or selection of correct objects, we generate and verify the object hypotheses for features detected in a scene in the order of the discriminatory power of these features for model objects. Experimental results on synthetic and real range images show the effectiveness of our probabilistic indexing scheme.
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