HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
The combinatorics of object recognition in cluttered environments using constrained search
Artificial Intelligence
A fast parallel algorithm for thinning digital patterns
Communications of the ACM
Computer Vision
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A new way to solve the matching problem between model and image features is described in this paper. Matches between features accumulate in a region of an abstract space; a space similar to the Hough space. In such a space, found clusters determine possible 2D rotations and scale changes of the object in the image. Finally the relative position between model and image features is verified in each cluster. The use of a space of accumulation drastically reduces the complexity of matching. The proposed approach has been tested with several images with very promising results.