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.
CITATION STYLE
Heidemann, G., Kummert, F., Ritter, H., & Sagerer, G. (1996). A hybrid object recognition architecture. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 305–310). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_54
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