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Fast Segmentation Methods Based on Partial Differential Equations and the Watershed Transformation

  • Joachim Weickert
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Segmentation algorithms are presented which combine regularization by nonlinear partial differential equations (PDEs) with a watershed transformation with region merging. We develop efficient algorithms for two well-founded PDE methods. They use an additive operator splitting (AOS) leading to recursive and separable filters. Further speed-up can be obtained by embedding AOS schemes into a pyramid framework. Examples demonstrate that the preprocessing by these PDE techniques eases and stabilizes the segmentation. The typical CPU time for segmenting a 2562 image on a workstation is less than 2 seconds.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Joachim Weickert
    • 1
  1. 1.Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark

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