ML Anthology

Learning Rotational Features for Filament Detection

Germán González, François Fleuret, Pascal Fua
CVPR 2009 pp. 1582-1589
doi:10.1109/CVPR.2009.5206511 /cvpr/2009/gonzalez2009cvpr-learning/

Abstract

State-of-the-art approaches for detecting filament-like structures in noisy images rely on filters optimized for signals of a particular shape, such as an ideal edge or ridge. While these approaches are optimal when the image conforms to these ideal shapes, their performance quickly degrades on many types of real data where the image deviates from the ideal model, and when noise processes violate a Gaussian assumption.

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Text

González et al. "Learning Rotational Features for Filament Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009. doi:10.1109/CVPR.2009.5206511

Markdown

[González et al. "Learning Rotational Features for Filament Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009.](https://mlanthology.org/cvpr/2009/gonzalez2009cvpr-learning/) doi:10.1109/CVPR.2009.5206511

BibTeX

@inproceedings{gonzalez2009cvpr-learning,
  title     = {{Learning Rotational Features for Filament Detection}},
  author    = {González, Germán and Fleuret, François and Fua, Pascal},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2009},
  pages     = {1582-1589},
  doi       = {10.1109/CVPR.2009.5206511},
  url       = {https://mlanthology.org/cvpr/2009/gonzalez2009cvpr-learning/}
}