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Complementary Optic Flow

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5681))

Abstract

We introduce the concept of complementarity between data and smoothness term in modern variational optic flow methods. First we design a sophisticated data term that incorporates HSV colour representation with higher order constancy assumptions, completely separate robust penalisation, and constraint normalisation. Our anisotropic smoothness term reduces smoothing in the data constraint direction instead of the image edge direction, while enforcing a strong filling-in effect orthogonal to it. This allows optimal complementarity between both terms and avoids undesirable interference. The high quality of our complementary optic flow (COF) approach is demonstrated by the current top ranking result at the Middlebury benchmark.

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Authors and Affiliations

  1. Mathematical Image Analysis Group Faculty of Mathematics and Computer Science, Saarland University, Saarbrücken, Germany

    Henning Zimmer, Andrés Bruhn, Joachim Weickert & Levi Valgaerts

  2. Departamento de Informática y Sistemas, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Agustín Salgado

  3. Institut für Informationsverarbeitung, University of Hannover, Hannover, Germany

    Bodo Rosenhahn

  4. Max-Planck Institute for Informatics, Saarbrücken, Germany

    Henning Zimmer & Hans-Peter Seidel

Authors
  1. Henning Zimmer
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  2. Andrés Bruhn
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  3. Joachim Weickert
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  4. Levi Valgaerts
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  5. Agustín Salgado
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  6. Bodo Rosenhahn
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  7. Hans-Peter Seidel
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Editor information

Editors and Affiliations

  1. Institut für Informatik III, Rö, Universität Bonn, merstraße 164, 53117, Bonn, Germany

    Daniel Cremers

  2. Computer Science Department, University of Western Ontario, N6A 5A5, London, ON, Canada

    Yuri Boykov

  3. Microsoft Research, J.J. Thomson Avenue, CB3 OFB, Cambridge, United Kingdom

    Andrew Blake

  4. Institut für Informatik III, Universität Bonn, Römerstraße 164, 53117, Bonn, Germany

    Frank R. Schmidt

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© 2009 Springer-Verlag Berlin Heidelberg

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Zimmer, H. et al. (2009). Complementary Optic Flow. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2009. Lecture Notes in Computer Science, vol 5681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03641-5_16

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  • DOI: https://doi.org/10.1007/978-3-642-03641-5_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03640-8

  • Online ISBN: 978-3-642-03641-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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