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Convex Multi-class Image Labeling by Simplex-Constrained Total Variation

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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

Multi-class labeling is one of the core problems in image analysis. We show how this combinatorial problem can be approximately solved using tools from convex optimization. We suggest a novel functional based on a multidimensional total variation formulation, allowing for a broad range of data terms. Optimization is carried out in the operator splitting framework using Douglas-Rachford Splitting. In this connection, we compare two methods to solve the Rudin-Osher-Fatemi type subproblems and demonstrate the performance of our approach on single- and multichannel images.

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Author information

Authors and Affiliations

  1. Image and Pattern Analysis Group (IPA) HCI, Dept. of Mathematics and Computer Science, University of Heidelberg, Germany

    Jan Lellmann, Jörg Kappes, Jing Yuan, Florian Becker & Christoph Schnörr

Authors
  1. Jan Lellmann
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  2. Jörg Kappes
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  3. Jing Yuan
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  4. Florian Becker
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  5. Christoph Schnörr
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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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Lellmann, J., Kappes, J., Yuan, J., Becker, F., Schnörr, C. (2009). Convex Multi-class Image Labeling by Simplex-Constrained Total Variation. 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_13

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  • DOI: https://doi.org/10.1007/978-3-642-02256-2_13

  • 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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