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

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 1843)


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.


  • 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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© 2000 Springer-Verlag Berlin Heidelberg

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Pauwels, E.J., Frederix, G. (2000). Image Segmentation by Nonparametric Clustering Based on the Kolmogorov-Smirnov Distance. In: Vernon, D. (eds) Computer Vision — ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1843. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67686-7

  • Online ISBN: 978-3-540-45053-5

  • eBook Packages: Springer Book Archive

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