HyperBF networks for real object recognition
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
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We explore representation of 3D objects in which several distinct 2D views are stored for each object. We demonstrate the ability of a two-layer network of threshold summation units to support such representations. Using unsupervised Hebbian relaxation, we trained the network to recognize ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input view. When tested on novel views of the same objects, the network exhibited a substantial generalization capability. In simulated psychophysical experiments, the network''s behavior was qualitatively similar to that of human subjects.