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Learning 3D recognition models for general objects from unlabeled imagery: an experiment in intelligent brute force

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2 Author(s)
R. C. Nelson ; Dept. of Comput. Sci., Rochester Univ., NY, USA ; A. Selinger

In this paper we explorer the problem of training a general, 3D abject recognition system from unlabeled imagery. In particular, we attempt to identify critical issues and stumbling blocks associated with minimizing the supervision necessary to train such a system. As class learning seems to be a relatively slow and resource intensive process even for people, we consider approaches and perform experiments that entail on the order of 1015 basic operations, even for relatively small databases. This is the current practical limit of the computation that can be achieved. For experiments, we use a recognition system developed previously

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Pattern Recognition, 2000. Proceedings. 15th International Conference on  (Volume:1 )

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