Distributed Bayesian object recognition

A new paradigm for performing realistic object recognition is presented. It is shown how several intuitive notions in the context of geometric hashing can be translated into a well-founded Bayesian approach to object recognition. This interpretation leads to well-justified formulas and gives a precise weighted-voting method for the evidence-gathering phase of geometric hashing. A computational model for performing object recognition in a distributed fashion is described. The validity of the authors´ paradigm is demonstrated by presenting a prototype system that has been implemented on a small cluster of nondedicated workstations. The resulting system is scalable and can recognize models subjected to 2-D rotation, translation and scale changes in real-world digital imagery. The performance of the system is superior by a factor of 2 to that obtained for a similar system on the Connection Machine-2 (CM-2)