A Hierarchical Recursive Partial Active Basis Model

Recognition of occluded objects in computer vision is a very hard problem. In this work we propose an algorithm to construct a structure of a model using learned active basis models, then use it to do inference over the most probable detected parts of an object, to allow partial recognition using the standard sum-max-maps algorithm used for active basis. We tested our method and present some improvements on occluded face detection using our algorithm, we also present some experiments with other partially occluded objects.

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