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Object selection with dynamic neural maps

  • Part VI: Speech, Vision, and Pattern Recognition
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

This contribution presents an approach for object selection. It is based on the functional role of attention in its control of action, that is the selection in space and the selection at the object level to serve the extraction of further object related features. The selection is performed on topographic maps which describe by their local activation the existence of an object hypothesis and is achieved by a dynamical two step process of i) competition and ii) region aggregation. The neural dynamics is based on analog neurons, mathematically described with a nonlinear activation dynamic. This method is appropriate for all objects which are detected by their local features, like color or texture. The performance is demonstrated on two real world selection problems.

supported by the BMBF, Grant No. 413-5839-01 M 3014C - MIRIS-Project.

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Authors and Affiliations

  1. Neuroinformatik, Technische Universitat Ilmenau, D-98684, Ilmenau, Germany

    Fred Henrik Hamker & Horst-Michael Gross

Authors
  1. Fred Henrik Hamker
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  2. Horst-Michael Gross
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Hamker, F.H., Gross, HM. (1997). Object selection with dynamic neural maps. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020270

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  • DOI: https://doi.org/10.1007/BFb0020270

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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