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Learning by Experience

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Adaptivity and Learning

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Abstract

Machine vision will be a key technique in future robotics. Though we are still far from the construction of robots with human capabilities, machine vision already plays an increasingly important role in many robot applications. In this contribution, we will outline a man machine interaction scenario as an example what can be achieved by now using adaptive methods in machine vision. We will discuss the problems of current vision systems and outline possible future developments.

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Authors
  1. Gunther Heidemann
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  2. Helge Ritter
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Editor information

Editors and Affiliations

  1. Institut für Theoretische Physik, Universität Heidelberg, Philosophenweg 19, 69120, Heidelberg, Germany

    Reimer Kühn  & Ion-Olimpiu Stamatescu  & 

  2. Neurobiologie, Freie Universität Berlin, Königin-Luise-Str. 28/30, 14195, Berlin, Germany

    Randolf Menzel

  3. Institut für Logik, Komplexität und Deduktionssysteme, Universität Karlsruhe, 76128, Karlsruhe, Germany

    Wolfram Menzel

  4. FESt, Schmeilweg 5, 69118, Heidelberg, Germany

    Ulrich Ratsch  & Ion-Olimpiu Stamatescu  & 

  5. FB Informatik, University of Kaiserslautern, 67653, Kaiserslautern, Germany

    Michael M. Richter

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© 2003 Springer-Verlag Berlin Heidelberg

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Heidemann, G., Ritter, H. (2003). Learning by Experience. In: Kühn, R., Menzel, R., Menzel, W., Ratsch, U., Richter, M.M., Stamatescu, IO. (eds) Adaptivity and Learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05594-6_16

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  • DOI: https://doi.org/10.1007/978-3-662-05594-6_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05510-2

  • Online ISBN: 978-3-662-05594-6

  • eBook Packages: Springer Book Archive

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