Interactive Image Segmentation Using Level Sets and Dempster-Shafer Theory of Evidence

  • Björn Scheuermann
  • Bodo Rosenhahn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

Variational frameworks based on level set methods are popular for the general problem of image segmentation. They combine different feature channels in an energy minimization approach. In contrast to other popular segmentation frameworks, e.g. the graph cut framework, current level set formulations do not allow much user interaction. Except for selecting the initial boundary, the user is barely able to guide or correct the boundary propagation. Based on Dempster-Shafer theory of evidence we propose a segmentation framework which integrates user interaction in a novel way. Given the input image, the proposed algorithm determines the best segmentation allowing the user to take global influence on the boundary propagation.

Keywords

Image Segmentation Segmentation Result Feature Channel Evidence Theory Variational Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Björn Scheuermann
    • 1
  • Bodo Rosenhahn
    • 1
  1. 1.Leibniz Universität HannoverGermany

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