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A Distributed Multicolumnar System for Primary Cortical Analysis of Real-World Scenes

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ICANN ’93 (ICANN 1993)

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Abstract

In this paper we present a distributed multicolumnar system using an intracolumnar principal component analysis (PCA) for a topology preserving mapping of real-world grey level distributions within a two-dimensional intercolumnar Kohonen Feature Map. A two-stage principal component analysis within each processing column allows a similarity preserving description with only a few highly effective fitting parameters suited for a local translation invariant processing.

Supported by Ministry of Research and Technology (BMFT), Grant No. 413-5839-01 IN 101D — NAMOS-Project

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References

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Author information

Authors and Affiliations

  1. Div. of Neuroinformatics, Technical University of Ilmenau, D-6300, Ilmenau, P.O.Box 327, Germany

    T. Pomierski, H. M. Gross & D. Wendt

Authors
  1. T. Pomierski
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  2. H. M. Gross
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  3. D. Wendt
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Editor information

Editors and Affiliations

  1. Dutch Foundation for Neural Networks, University of Nijmegen, Geert Grooteplein 21, 6525 EZ, Nijmegen, The Netherlands

    Stan Gielen  & Bert Kappen  & 

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© 1993 Springer-Verlag London Limited

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Pomierski, T., Gross, H.M., Wendt, D. (1993). A Distributed Multicolumnar System for Primary Cortical Analysis of Real-World Scenes. In: Gielen, S., Kappen, B. (eds) ICANN ’93. ICANN 1993. Springer, London. https://doi.org/10.1007/978-1-4471-2063-6_32

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  • DOI: https://doi.org/10.1007/978-1-4471-2063-6_32

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  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19839-0

  • Online ISBN: 978-1-4471-2063-6

  • eBook Packages: Springer Book Archive

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