The authors present 3D-POLY, a working system for recognizing objects in the presence of occlusion and against cluttered backgrounds. The time complexity of this system is only O(n/sup 2/) for single-object recognition, where n is the number of features on the project. The organisation of the feature data for the models is based on a data structure called the feature sphere. Efficient constant-time algorithms for assigning a feature to its proper place on a feature sphere and for extracting the neighbors of a given feature from the feature sphere representation are presented. For hypothesis generation, local feature sets are used. The combination of the feature sphere idea for streamlining verification and the local feature sets for hypothesis generation results in a system whose time complexity has a low-order polynomial bound.
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