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Probabilistic Hypothesis Verification

  • Conference paper
Mustererkennung 1998

Part of the book series: Informatik aktuell ((INFORMAT))

  • 124 Accesses

Abstract

We describe a method for hypothesis verification for view-based recognition using Bayes rule. For feature positions we assume conditional densities which are based on the observation that deviations in the relative position of feature points increase with the distance of feature points. We advocate the use of curve segments with constant sign of curvature instead of circular or elliptical arc segments. Our approach is validated with real and artificial data of mostly curved objects.

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References

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Author information

Authors and Affiliations

  1. Forschungsgruppe Bildverstehen, TU München, Orleansstr. 34, 81667, München, Germany

    Dietrich Büsching

  2. School of Computer Studies, University of Leeds, Leeds, LS2 9JT, UK

    Dietrich Büsching

Authors
  1. Dietrich Büsching
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Editor information

Editors and Affiliations

  1. IPVR - Institute für Parallele und Verteilte Höchstleistungsrechner, Universität Stuttgart, Breitwiesenstrasse 20-22, D-70565, Stuttgart, Germany

    Paul Levi  & Michael Schanz  & 

  2. AEG Sensorsysteme GmbH, Junkerstrasse 2, D-69469, Weinheim, Germany

    Rolf-Jürgen Ahlers

  3. Daimler Benz AG FT3/E, Wilhelm-Runge-Strasse 11, D-89081, Ulm, Germany

    Franz May

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© 1998 Springer-Verlag Berlin Heidelberg

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Büsching, D. (1998). Probabilistic Hypothesis Verification. In: Levi, P., Schanz, M., Ahlers, RJ., May, F. (eds) Mustererkennung 1998. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72282-0_6

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  • DOI: https://doi.org/10.1007/978-3-642-72282-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64935-9

  • Online ISBN: 978-3-642-72282-0

  • eBook Packages: Springer Book Archive

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