Image Segmentation by Nonparametric Clustering Based on the Kolmogorov-Smirnov Distance
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.
Cite
Text
Pauwels and Frederix. "Image Segmentation by Nonparametric Clustering Based on the Kolmogorov-Smirnov Distance." European Conference on Computer Vision, 2000. doi:10.1007/3-540-45053-X_6Markdown
[Pauwels and Frederix. "Image Segmentation by Nonparametric Clustering Based on the Kolmogorov-Smirnov Distance." European Conference on Computer Vision, 2000.](https://mlanthology.org/eccv/2000/pauwels2000eccv-image/) doi:10.1007/3-540-45053-X_6BibTeX
@inproceedings{pauwels2000eccv-image,
title = {{Image Segmentation by Nonparametric Clustering Based on the Kolmogorov-Smirnov Distance}},
author = {Pauwels, Eric J. and Frederix, Greet},
booktitle = {European Conference on Computer Vision},
year = {2000},
pages = {85-99},
doi = {10.1007/3-540-45053-X_6},
url = {https://mlanthology.org/eccv/2000/pauwels2000eccv-image/}
}