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Gabor Wavelet Networks for Object Representation

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Multi-Image Analysis

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2032))

Abstract

In this article we want to introduce first the Gabor wavelet network as a model based approach for an effective and efficient object representation. The Gabor wavelet network has several advantages such as invariance to some degree with respect to translation, rotation and dilation. Furthermore, the use of Gabor filters ensured that geometrical and textural object features are encoded. The feasibility of the Gabor filters as a model for local object features ensures a considerable data reduction while at the same time allowing any desired precision of the object representation ranging from a sparse to a photo-realistic representation. In the second part of the paper we will present an approach for the estimation of a head pose that is based on the Gabor wavelet networks.

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Author information

Authors and Affiliations

  1. Christian-Albrechts-University of Kiel, Institute of Computer Science and Applied Mathematics, Cognitive Systems, Preßerstraße 1-9, 24105, Kiel, Germany

    Volker Krüger & Gerald Sommer

Authors
  1. Volker Krüger
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  2. Gerald Sommer
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Editor information

Editors and Affiliations

  1. Center for image Technology and Robotics (CITR Tamaki), Auckland, New Zealand

    Reinhard Klette  & Georgy Gimel’farb  & 

  2. 2039 Beckman Institute, University of Illinois, 405 N. Mathews, Urbana, IL, 61801, USA

    Thomas Huang

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© 2001 Springer-Verlag Berlin Heidelberg

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Krüger, V., Sommer, G. (2001). Gabor Wavelet Networks for Object Representation. In: Klette, R., Gimel’farb, G., Huang, T. (eds) Multi-Image Analysis. Lecture Notes in Computer Science, vol 2032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45134-X_9

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  • DOI: https://doi.org/10.1007/3-540-45134-X_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42122-1

  • Online ISBN: 978-3-540-45134-1

  • eBook Packages: Springer Book Archive

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