Modeling Bayesian Estimation for Deformable Contours
Abstract
A novel trainable snake model called EigenSnake, is presented in the Bayesian framework. In the EigenSnake, prior knowledge of a specific object shape, such as that of face outlines and facial features, is derived from a training set of the shape and incorporated into a Bayesian snake model in the form of the prior distribution. Further, a "shape space", which is constructed on the basis of a set of eigenvectors obtained from principle component analysis, is used to restrict and stabilize the search for the optimal solution. The effectiveness is demonstrated by experiments, which shows that the EigenSnake produces more reliable and accurate results than existing models.
Cite
Text
Li and Lu. "Modeling Bayesian Estimation for Deformable Contours." IEEE/CVF International Conference on Computer Vision, 1999. doi:10.1109/ICCV.1999.790376Markdown
[Li and Lu. "Modeling Bayesian Estimation for Deformable Contours." IEEE/CVF International Conference on Computer Vision, 1999.](https://mlanthology.org/iccv/1999/li1999iccv-modeling/) doi:10.1109/ICCV.1999.790376BibTeX
@inproceedings{li1999iccv-modeling,
title = {{Modeling Bayesian Estimation for Deformable Contours}},
author = {Li, Stan Z. and Lu, Juwei},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1999},
pages = {991-996},
doi = {10.1109/ICCV.1999.790376},
url = {https://mlanthology.org/iccv/1999/li1999iccv-modeling/}
}