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Basic Image Features (BIFs) Arising from Approximate Symmetry Type

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Scale Space and Variational Methods in Computer Vision (SSVM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5567))

Abstract

We consider detection of local image symmetry using linear filters. We prove a simple criterion for determining if a filter is sensitive to a group of symmetries. We show that derivative-of-Gaussian (DtG) filters are excellent at detecting local image symmetry. Building on this, we propose a very simple algorithm that, based on the responses of a bank of six DtG filters, classifies each location of an image into one of seven Basic Image Features (BIFs). This effectively and efficiently realizes Marr’s proposal for an image primal sketch. We summarize results on the use of BIFs for texture classification, object category detection, and pixel classification.

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Author information

Authors and Affiliations

  1. Computer Science, University College London, London, WC1E 6BT, UK

    Lewis D. Griffin, Martin Lillholm & Mike Crosier

  2. Biomedical Engineering, Eindhoven University of Technology, The Netherlands

    Justus van Sande

Authors
  1. Lewis D. Griffin
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  2. Martin Lillholm
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  3. Mike Crosier
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  4. Justus van Sande
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Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Bergen, Johannes Brunsgate 12, 5008, Bergen, Norway

    Xue-Cheng Tai

  2. Department of Informatics and Centre of Mathematics for Applications, University of Oslo, Oslo, Norway

    Knut Mørken

  3. Simula Research Laboratory, M. Linges v 17, Fornebu, P.O.Box 134, N-1325, Lysaker, Norway

    Marius Lysaker

  4. SINTEF ICT, Oslo, Norway

    Knut-Andreas Lie

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© 2009 Springer-Verlag Berlin Heidelberg

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Griffin, L.D., Lillholm, M., Crosier, M., van Sande, J. (2009). Basic Image Features (BIFs) Arising from Approximate Symmetry Type. In: Tai, XC., Mørken, K., Lysaker, M., Lie, KA. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2009. Lecture Notes in Computer Science, vol 5567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02256-2_29

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  • DOI: https://doi.org/10.1007/978-3-642-02256-2_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02255-5

  • Online ISBN: 978-3-642-02256-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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