Some new techniques for evidence-based object recognition: EB-ORS1

The authors have extended the evidence-based object recognition system of Jain and Hoffman (1988) to include some new view-independent features, a new optimized rule generation procedure based upon minimum entropy clustering and a neural network which estimates optimal evidence weights and provides an associated matching procedure. This approach provides an objective definition of the difficulty of an object recognition problem. The authors also evaluate the procedures and performance of the system with two sets of CAD (range) models.<>