PROBABILISTIC INDEXING FOR OBJECT RECOGNITION

Recent papers have indicated that indexing is a promising approach to fast model-based object recognition became it allows most of the possible matches between sets of image features and sets of model features to be quickly eliminated from consideration. This correspondence describes a system that is capable of indexing using sets of three points undergoing three-dimensional transformations and projection by taking advantage of the probabilistic peaking effect. To be able to index using sets of three points, we must allow false negatives. These are overcome by ensuring that we examine several correct hypotheses. The use of these techniques to speed up the alignment method is described. Results are Even on real and synthetic data.