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A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation

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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.

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Authors and Affiliations

  1. Department of Computer Science, University of Bonn, Germany

    Daniel Cremers

  2. Department of Applied Mathematics, Tel Aviv University, Israel

    Nir Sochen

  3. Department of Mathematics and Computer Science, University of Mannheim, Germany

    Christoph Schnörr

Authors
  1. Daniel Cremers
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  2. Nir Sochen
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  3. Christoph Schnörr
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Cremers, D., Sochen, N. & Schnörr, C. A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation. Int J Comput Vision 66, 67–81 (2006). https://doi.org/10.1007/s11263-005-3676-z

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  • DOI: https://doi.org/10.1007/s11263-005-3676-z

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