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A hybrid object recognition architecture

  • Oral Presentations: Sensory Processing Sensory Processing II: Object Recognition
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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

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.

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References

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Authors and Affiliations

  1. University of Bielefeld, 33501, Bielefeld, Germany

    Gunther Heidemann, Franz Kummert, Helge Ritter & Gerhard Sagerer

Authors
  1. Gunther Heidemann
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  2. Franz Kummert
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  3. Helge Ritter
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  4. Gerhard Sagerer
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Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Heidemann, G., Kummert, F., Ritter, H., Sagerer, G. (1996). A hybrid object recognition architecture. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_54

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  • DOI: https://doi.org/10.1007/3-540-61510-5_54

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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