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Image segmentation by nonparametric clustering based on the kolmogorov-smirnov distance

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2000) 1843 85-99
DOI: 10.1007/3-540-45053-x_6
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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.

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APA

Pauwels, E. J., & Frederix, G. (2000). Image segmentation by nonparametric clustering based on the kolmogorov-smirnov distance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1843, pp. 85–99). Springer Verlag. https://doi.org/10.1007/3-540-45053-x_6

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