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Image Segmentation by Nonparametric Clustering Based on the Kolmogorov-Smirnov Distance

  • Eric J. Pauwels
  • Greet Frederix
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1843)

Abstract

In this paper we introduce a non-parametric clustering algorithm for 1-dimensional data. The procedure looks for the simplest (i.e. smoothest) density that is still compatible with the data. Compatibility is given a precise meaning in terms of the Kolmogorov-Smirnov statistic. After discussing experimental results for colour segmentation, we outline how this proposed algorithm can be extended to higher dimensions.

Keywords

Image Segmentation Gaussian Mixture Model Independent Component Analysis Salient Region Colour Segmentation 
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 2000

Authors and Affiliations

  • Eric J. Pauwels
    • 1
    • 2
  • Greet Frederix
    • 2
    • 3
  1. 1.Centre for Mathematics and Computer Science (CWI)AmsterdamThe Netherlands
  2. 2.ESAT-PSI, K.U.LeuvenHeverleeBelgium
  3. 3.Dept. of MathematicsK.U.LeuvenHeverleeBelgium

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