A Hybrid Object Recognition Architecture

We present an architecture for 3D-object recognition based on the integration of neural and semantic networks. The architecture consists of mainly two components. A neural object recognition system generates object hypotheses, which are verified or rejected by a semantic network. Thus the advantages of both paradigms are combined: in the low level field adaptivity and the ability to learn from examples is realized by a neural network, whereas the high level analysis is performed by representing structured knowledge in a semantic network.