ML Anthology

Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework

Omer Rotem, Hayit Greenspan, Jacob Goldberger
CVPR 2007
doi:10.1109/CVPR.2007.383232 /cvpr/2007/rotem2007cvpr-combining/

Abstract

In this paper we propose a new segmentation algorithm which combines patch-based information with edge cues under a probabilistic framework. We use a mixture of multiple Gaussians for building the statistical model with color and spatial features, and we incorporate edge information based on texture, color and brightness differences into the EM algorithm. We evaluate our results qualitatively and quantitatively on a large data-set of natural images and compare our results to other state-of-the-art methods.

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Cite

Text

Rotem et al. "Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383232

Markdown

[Rotem et al. "Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/rotem2007cvpr-combining/) doi:10.1109/CVPR.2007.383232

BibTeX

@inproceedings{rotem2007cvpr-combining,
  title     = {{Combining Region and Edge Cues for Image Segmentation in a Probabilistic Gaussian Mixture Framework}},
  author    = {Rotem, Omer and Greenspan, Hayit and Goldberger, Jacob},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383232},
  url       = {https://mlanthology.org/cvpr/2007/rotem2007cvpr-combining/}
}