ML Anthology

Shape Constrained Image Segmentation by Parametric Distributional Clustering

Thomas Zöller, Joachim M. Buhmann
CVPR 2004 pp. 386-393
doi:10.1109/CVPR.2004.228 /cvpr/2004/zoller2004cvpr-shape/

Abstract

The automated segmentation of images into semantically meaningful parts requires shape information since lowlevel feature analysis alone often fails to reach this goal. We introduce a novel method of shape constrained image segmentation which is based on mixtures of feature distributions for color and texture as well as probabilistic shape knowledge. The combined approach is formulated in the framework of Bayesian statistics to account for the robustness requirement in image understanding. Experimental evidence shows that semantically meaningful segments are inferred, even when image data alone gives rise to ambiguous segmentations.

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Text

Zöller and Buhmann. "Shape Constrained Image Segmentation by Parametric Distributional Clustering." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004. doi:10.1109/CVPR.2004.228

Markdown

[Zöller and Buhmann. "Shape Constrained Image Segmentation by Parametric Distributional Clustering." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004.](https://mlanthology.org/cvpr/2004/zoller2004cvpr-shape/) doi:10.1109/CVPR.2004.228

BibTeX

@inproceedings{zoller2004cvpr-shape,
  title     = {{Shape Constrained Image Segmentation by Parametric Distributional Clustering}},
  author    = {Zöller, Thomas and Buhmann, Joachim M.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2004},
  pages     = {386-393},
  doi       = {10.1109/CVPR.2004.228},
  url       = {https://mlanthology.org/cvpr/2004/zoller2004cvpr-shape/}
}