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Object Localization Based on Markov Random Fields and Symmetry Interest Points

  • René Donner
  • Branislav Micusik
  • Georg Langs
  • Lech Szumilas
  • Philipp Peloschek
  • Klaus Friedrich
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4792)

Abstract

We present an approach to detect anatomical structures by configurations of interest points, from a single example image. The representation of the configuration is based on Markov Random Fields, and the detection is performed in a single iteration by the max-sum algorithm. Instead of sequentially matching pairs of interest points, the method takes the entire set of points, their local descriptors and the spatial configuration into account to find an optimal mapping of modeled object to target image. The image information is captured by symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • René Donner
    • 1
    • 2
  • Branislav Micusik
    • 2
  • Georg Langs
    • 1
    • 3
  • Lech Szumilas
    • 2
  • Philipp Peloschek
    • 4
  • Klaus Friedrich
    • 4
  • Horst Bischof
    • 2
  1. 1.Institute for Computer Graphics and Vision, Graz University of TechnologyAustria
  2. 2.Pattern Recognition and Image Processing Group, Vienna University of TechnologyAustria
  3. 3.GALEN Group, Laboratoire de Mathématiques Appliquées aux Systèmes, Ecole Centrale de ParisFrance
  4. 4.Department of Radiology, Medical University of ViennaAustria

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