ML Anthology

Robust Snake Model

Hui Luo, Qiang Lu, Raj Acharya, Roger S. Gaborski
CVPR 2000 pp. 1452-1457
doi:10.1109/CVPR.2000.855854 /cvpr/2000/luo2000cvpr-robust/

Abstract

In this paper, we propose a new deformable model a robust snake model, which solves the primary problems suffered by the conventional snake, such as contour initialization, proper internal parameter setting and the limited capture range of the external energy. A reformulated internal energy is used to serve the smoothness of snake contour without a contraction of the contour. The external energy combines both region and edge information to enlarge the capture range, and also reduces the requirement of initial contour. Both synthetic and real gray-level images are selected to evaluate the performance of the proposed model. Its implementation show it robust, fast and accurate. Initial experimental results are encouraging.

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Cite

Text

Luo et al. "Robust Snake Model." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2000. doi:10.1109/CVPR.2000.855854

Markdown

[Luo et al. "Robust Snake Model." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2000.](https://mlanthology.org/cvpr/2000/luo2000cvpr-robust/) doi:10.1109/CVPR.2000.855854

BibTeX

@inproceedings{luo2000cvpr-robust,
  title     = {{Robust Snake Model}},
  author    = {Luo, Hui and Lu, Qiang and Acharya, Raj and Gaborski, Roger S.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2000},
  pages     = {1452-1457},
  doi       = {10.1109/CVPR.2000.855854},
  url       = {https://mlanthology.org/cvpr/2000/luo2000cvpr-robust/}
}