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We introduce a novel form of shape representation based on recording the distribution of pairwise geometric relationships between local shape features. It is shown that the geometric histograms used to record these distributions can be easily and robustly acquired from image data and can support recognition even when the shape extracted from the image is badly degraded by fragmentation noise and occlusion. Moreover, the processing involved in establishing correspondences between model and image features is both simple and parallel and has many advantages over previous search based methods. 1
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Evans et al. (1993) studied this question.