An experimental and theoretical analysis of the noise model in the geometric hashing technique is presented. The efficacy of the technique is discussed in the cases of affine transformations, similarities, and grid motions. The efficacy of the voting procedure, in geometric hashing is discussed, and it is shown that its introduction significantly reduces the expected burden of the verification stage. The discussion is illustrated by results of simulations which have been performed to test the expected performance of the algorithm.>

New techniques are described for model-based recognition of the objects in 3-D space. The recognition is performed from single gray-scale images taken from unknown viewpoints. The objects in the scene may be overlapping and partially occluded. An efficient matching algorithm, which assumes affine approximation to the prospective viewing transformation, is proposed. The algorithm has an offline model preprocessing (shape representation) phase which is independent of the scene information and a recognition phase based on efficient indexing. It has a straightforward parallel implementation. The algorithm was successfully tested in recognition of industrial objects appearing in composite occluded scenes.>

Novel techniques are described for model-based recognition of 3-D objects from unknown viewpoints using single-gray-scale images. The objects in the scene may be overlapping and partially occluded. Efficient matching algorithms, which assume affine approximation to the perspective viewing transformation, are proposed. The study is currently restricted to flat rigid 3-D objects. Point, line and curve matching algorithms are presented. The study especially emphasizes the curve matching problem. Experimental results are included.>

A general method for model-based object recognition in occluded scenes is presented. It is based on geometric hashing. The method stands out for its efficiency. We describe the general framework of the method and illustrate its applications for various recogni- tion problems both in 3-D and 2-D. Special attention is given to the recognition of 3-D objects in occluded scenes from 2-D gray scale images. New experimental results are included for this important case.

Techniques are described for model-based recognition of 3-D objects from unknown viewpoints using single gray-scale images. The method is especially useful for recognition of scenes with overlapping and partially occluded objects. An efficient matching algorithm, which assumes affine approximation to the perspective viewing transformation, is proposed. The algorithm has an off line model preprocessing phase and a recognition phase to reduce matching complexity. The algorithm was successfully tested in recognition of flat industrial objects appearing in composite occluded scenes.>