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Weakly Convex Coupling Continuous Cuts and Shape Priors

  • Bernhard Schmitzer
  • Christoph Schnörr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6667)

Abstract

We introduce a novel approach to variational image segmentation with shape priors. Key properties are convexity of the joint energy functional and weak coupling of convex models from different domains by mapping corresponding solutions to a common space. Specifically, we combine total variation based continuous cuts for image segmentation and convex relaxations of Markov Random Field based shape priors learned from shape databases. A convergent algorithm amenable to large-scale convex programming is presented. Numerical experiments demonstrate promising synergistic performance of convex continuous cuts and convex variational shape priors under image distortions related to noise, occlusions and clutter.

Keywords

Image Segmentation Dependency Graph Convex Relaxation Convex Model Shape Prior 
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 2012

Authors and Affiliations

  • Bernhard Schmitzer
    • 1
  • Christoph Schnörr
    • 1
  1. 1.University of HeidelbergGermany

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