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From Inpainting to Active Contours

  • Conference paper
Variational, Geometric, and Level Set Methods in Computer Vision (VLSM 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3752))

Abstract

We introduce a novel type of region based active contour using image inpainting. Usual region based active contours assume that the image is divided into several semantically meaningful regions and attempt to differentiate them through recovering dynamically statistical optimal parameters for each region. In case when perceptually distinct regions have similar intensity distributions, the methods mentioned above fail. In this work, we formulate the problem as optimizing a ”background disocclusion” criterion, a disocclusion that can be performed by inpainting. We look especially at a family of inpainting formulations that includes the Chan and Shen Total Variation Inpainting (more precisely a regularization of it). In this case, the optimization leads formally to a coupled contour evolution equation, an inpainting equation, as well as a linear PDE depending on the inpainting. The contour evolution is implemented in the framework of level sets. Finally, the proposed method is validated on various examples.

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Author information

Authors and Affiliations

  1. Departement of Innovation, The IT University of Copenhagen, Rued Langgaardsvej 7, 2300 Kbh S, Denmark

    François Lauze & Mads Nielsen

Authors
  1. François Lauze
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  2. Mads Nielsen
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Editor information

Editors and Affiliations

  1. MAS - Ecole Centrale Paris, Grande Voie des Vignes, 92295, Chatenay-Malabry, France

    Nikos Paragios

  2. I.N.R.I.A, 2004 route des lucioles,, 06902, Sophia-Antipolis, France

    Olivier Faugeras

  3. Department of Mathematics, UCLA,  

    Tony Chan

  4. Image and Pattern Analysis Group, Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany

    Christoph Schnörr

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© 2005 Springer-Verlag Berlin Heidelberg

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Lauze, F., Nielsen, M. (2005). From Inpainting to Active Contours. In: Paragios, N., Faugeras, O., Chan, T., Schnörr, C. (eds) Variational, Geometric, and Level Set Methods in Computer Vision. VLSM 2005. Lecture Notes in Computer Science, vol 3752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11567646_9

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  • DOI: https://doi.org/10.1007/11567646_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29348-4

  • Online ISBN: 978-3-540-32109-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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