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An Improved Riemannian Metric Approximation for Graph Cuts

  • Ondřej Daněk
  • Pavel Matula
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6607)

Abstract

Boykov and Kolmogorov showed that it is possible to find globally minimal contours and surfaces via graph cuts by embedding an appropriate metric approximation into the graph edge weights and derived the requisite formulas for Euclidean and Riemannian metrics [3]. In [9] we have proposed an improved Euclidean metric approximation that is invariant under (horizontal and vertical) mirroring, applicable to grids with anisotropic resolution and with a smaller approximation error. In this paper, we extend our method to general Riemannian metrics that are essential for graph cut based image segmentation or stereo matching. It is achieved by the introduction of a transformation reducing the Riemannian case to the Euclidean one and adjusting the formulas from [9] to be able to cope with non-orthogonal grids. We demonstrate that the proposed method yields smaller approximation errors than the previous approaches both in theory and practice.

Keywords

Edge Weight Voronoi Diagram Riemannian Space Riemannian Metrics Stereo Match 
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

  • Ondřej Daněk
    • 1
  • Pavel Matula
    • 1
  1. 1.Centre for Biomedical Image Analysis, Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

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