A multiphase dynamic labeling model for variational recognition-driven image segmentation

Daniel Cremers, Nir Sochen, Christoph Schnörr

Research output: Contribution to journalArticlepeer-review

Abstract

We propose a variational framework for the integration of multiple competing shape priors into level set based segmentation schemes. By optimizing an appropriate cost functional with respect to both a level set function and a (vector-valued) labeling function, we jointly generate a segmentation (by the level set function) and a recognition-driven partition of the image domain (by the labeling function) which indicates where to enforce certain shape priors. Our framework fundamentally extends previous work on shape priors in level set segmentation by directly addressing the central question of where to apply which prior. It allows for the seamless integration of numerous shape priors such that-while segmenting both multiple known and unknown objects-the level set process may selectively use specific shape knowledge for simultaneously enhancing segmentation and recognizing shape.

Original languageEnglish
Pages (from-to)67-81
Number of pages15
JournalInternational Journal of Computer Vision
Volume66
Issue number1
DOIs
StatePublished - Jan 2006

Keywords

  • Dynamic labeling
  • Image segmentation
  • Level set methods
  • Recognition modeling
  • Shape priors
  • Variational methods

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