ML Anthology

Increased Discrimination in Level Set Methods with Embedded Conditional Random Fields

Dana Cobzas, Mark Schmidt
CVPR 2009 pp. 328-335
doi:10.1109/CVPR.2009.5206812 /cvpr/2009/cobzas2009cvpr-increased/

Abstract

We propose a novel approach for improving level set segmentation methods by embedding the potential functions from a discriminatively trained conditional random field (CRF) into a level set energy function. The CRF terms can be efficiently estimated and lead to both discriminative local potentials and edge regularizers that take into account interactions among the labels. Unlike discrete CRFs, the use of a continuous level set framework allows the natural use of flexible continuous regularizers such as shape priors. We show promising experimental results for the method on two difficult medical image segmentation tasks.

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Cite

Text

Cobzas and Schmidt. "Increased Discrimination in Level Set Methods with Embedded Conditional Random Fields." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009. doi:10.1109/CVPR.2009.5206812

Markdown

[Cobzas and Schmidt. "Increased Discrimination in Level Set Methods with Embedded Conditional Random Fields." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009.](https://mlanthology.org/cvpr/2009/cobzas2009cvpr-increased/) doi:10.1109/CVPR.2009.5206812

BibTeX

@inproceedings{cobzas2009cvpr-increased,
  title     = {{Increased Discrimination in Level Set Methods with Embedded Conditional Random Fields}},
  author    = {Cobzas, Dana and Schmidt, Mark},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2009},
  pages     = {328-335},
  doi       = {10.1109/CVPR.2009.5206812},
  url       = {https://mlanthology.org/cvpr/2009/cobzas2009cvpr-increased/}
}