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Qualitative Representations for Recognition

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Biologically Motivated Computer Vision (BMCV 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2525))

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Abstract

The success of any object recognition system, whether biological or artificial, lies in using appropriate representation schemes. The schemes should efficiently encode object concepts while being tolerant to appearance variations induced by changes in viewing geometry and illumination. Here, we present a biologically plausible representation scheme wherein objects are encoded as sets of qualitative image measurements. Our emphasis on the use of qualitative measurements renders the representations stable in the presence of sensor noise and significant changes in object appearance. We develop our ideas in the context of the task of face-detection under varying illumination. Our approach uses qualitative photometric measurements to construct a face signature (‘ratiotemplate’) that is largely invariant to illumination changes.

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Author information

Authors and Affiliations

  1. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 02142, MA, Cambridge

    Pawan Sinha

Authors
  1. Pawan Sinha
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Editor information

Editors and Affiliations

  1. Max Planck Institute for Biological Cybernetics, Spemannstraße 38, 72076, Tübingen, Germany

    Heinrich H. Bülthoff  & Christian Wallraven  & 

  2. Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, 136-701, Seoul, Korea

    Seong-Whan Lee

  3. Department of Brain and Cognitive Sciences, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 45 Carleton Street, 02142, Cambridge, MA, USA

    Tomaso A. Poggio

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© 2002 Springer-Verlag Berlin Heidelberg

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Sinha, P. (2002). Qualitative Representations for Recognition. In: Bülthoff, H.H., Wallraven, C., Lee, SW., Poggio, T.A. (eds) Biologically Motivated Computer Vision. BMCV 2002. Lecture Notes in Computer Science, vol 2525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36181-2_25

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  • DOI: https://doi.org/10.1007/3-540-36181-2_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00174-4

  • Online ISBN: 978-3-540-36181-7

  • eBook Packages: Springer Book Archive

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