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Generic Model Abstraction from Examples

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Book cover Sensor Based Intelligent Robots

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2238))

Abstract

The recognition community has long avoided bridging the representational gap between traditional, low-level image features and generic models. Instead, the gap has been eliminated by either bringing the image closer to the models, using simple scenes containing idealized, textureless objects, or by bringing the models closer to the images, using 3-D CAD model templates or 2-D appearance model templates. In this paper, we attempt to bridge the representational gap for the domain of model acquisition. Specifically, we address the problem of automatically acquiring a generic 2-D view-based class model from a set of images, each containing an exemplar object belonging to that class. We introduce a novel graph-theoretical formulation of the problem, and demonstrate the approach on real imagery.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Rutgers University, 08903, New Brunswick, NJ, USA

    Yakov Keselman

  2. Department of Computer Science, University of Toronto, M5S 3G4, Toronto, Ontario, Canada

    Sven Dickinson

Authors
  1. Yakov Keselman
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  2. Sven Dickinson
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Johns Hopkins University, Baltimore

    Gregory D. Hager

  2. Center for Autonomous Systems, CVAP, Royal Institute of Technology, Sweden

    Henrik Iskov Christensen

  3. Department of Computer Science, University of Bern, German

    Horst Bunke

  4. Institute for Computer Science I, University of Bonn, USA

    Rolf Klein

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© 2002 Springer-Verlag Berlin Heidelberg

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Keselman, Y., Dickinson, S. (2002). Generic Model Abstraction from Examples. In: Hager, G.D., Christensen, H.I., Bunke, H., Klein, R. (eds) Sensor Based Intelligent Robots. Lecture Notes in Computer Science, vol 2238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45993-6_1

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  • DOI: https://doi.org/10.1007/3-540-45993-6_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43399-6

  • Online ISBN: 978-3-540-45993-4

  • eBook Packages: Springer Book Archive

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