Learning Aspect Graph Representations from View Sequences

Part of Advances in Neural Information Processing Systems 2 (NIPS 1989)

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Michael Seibert, Allen Waxman


In our effort to develop a modular neural system for invariant learn(cid:173) ing and recognition of 3D objects, we introduce here a new module architecture called an aspect network constructed around adaptive axo-axo-dendritic synapses. This builds upon our existing system (Seibert & Waxman, 1989) which processes 20 shapes and classifies t.hem into view categories (i.e., aspects) invariant to illumination, position, orientat.ion, scale, and projective deformations. From a sequence 'of views, the aspect network learns the transitions be(cid:173) tween these aspects, crystallizing a graph-like structure from an initially amorphous network . Object recognition emerges by ac(cid:173) cumulating evidence over multiple views which activate competing object hypotheses.