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An Experimental Comparison of Discrete and Continuous Shape Optimization Methods

  • Maria Klodt
  • Thomas Schoenemann
  • Kalin Kolev
  • Marek Schikora
  • Daniel Cremers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5302)

Abstract

Shape optimization is a problem which arises in numerous computer vision problems such as image segmentation and multiview reconstruction. In this paper, we focus on a certain class of binary labeling problems which can be globally optimized both in a spatially discrete setting and in a spatially continuous setting. The main contribution of this paper is to present a quantitative comparison of the reconstruction accuracy and computation times which allows to assess some of the strengths and limitations of both approaches. We also present a novel method to approximate length regularity in a graph cut based framework: Instead of using pairwise terms we introduce higher order terms. These allow to represent a more accurate discretization of the L 2-norm in the length term.

Keywords

Image Segmentation Volume Resolution Active Contour Model Continuous Setting Discrete Approach 
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 2008

Authors and Affiliations

  • Maria Klodt
    • 1
  • Thomas Schoenemann
    • 1
  • Kalin Kolev
    • 1
  • Marek Schikora
    • 1
  • Daniel Cremers
    • 1
  1. 1.Department of Computer ScienceUniversity of BonnGermany

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