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Joint Pattern Recognition Symposium

DAGM 2004: Pattern Recognition pp 415–423Cite as

Level Set Based Image Segmentation with Multiple Regions

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3175))

Abstract

We address the difficulty of image segmentation methods based on the popular level set framework to handle an arbitrary number of regions. While in the literature some level set techniques are available that can at least deal with a fixed amount of regions greater than two, there is very few work on how to optimise the segmentation also with regard to the number of regions. Based on a variational model, we propose a minimisation strategy that robustly optimises the energy in a level set framework, including the number of regions. Our evaluation shows that very good segmentations are found even in difficult situations.

We gratefully acknowledge partial funding by the Deutsche Forschungsgemeinschaft (DFG) and many interesting discussions with Mikaël Rousson from INRIA Sophia-Antipolis.

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Author information

Authors and Affiliations

  1. Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarland University, Building 27, 66041, Saarbrücken, Germany

    Thomas Brox & Joachim Weickert

Authors
  1. Thomas Brox
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  2. Joachim Weickert
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Editor information

Editors and Affiliations

  1. Max Planck Institute for Biological Cybernetics,, Spemannstr. 38, D-72076, Tübingen, Germany

    Carl Edward Rasmussen

  2. Max-Planck-Institute for Biological Cybernetics,, Spemannstr. 38, 72076, Tübingen, Germany

    Heinrich H. Bülthoff

  3. MPI for Biological Cybernetics, Spemannstr. 38, 72076, Tübingen, Germany

    Bernhard Schölkopf

  4. School of Psychology, University of Wales, Bangor, UK

    Martin A. Giese

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© 2004 Springer-Verlag Berlin Heidelberg

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Brox, T., Weickert, J. (2004). Level Set Based Image Segmentation with Multiple Regions. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_51

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  • DOI: https://doi.org/10.1007/978-3-540-28649-3_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22945-2

  • Online ISBN: 978-3-540-28649-3

  • eBook Packages: Springer Book Archive

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