Comparative Analysis of Kernel Methods for Statistical Shape Learning
Abstract
Prior knowledge about shape may be quite important for image segmentation. In particular, a number of different methods have been proposed to compute the statistics on a set of training shapes, which are then used for a given image segmentation task to provide the shape prior. In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding for doing shape analysis. The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set.
Cite
Text
Rathi et al. "Comparative Analysis of Kernel Methods for Statistical Shape Learning." European Conference on Computer Vision, 2006. doi:10.1007/11889762_9Markdown
[Rathi et al. "Comparative Analysis of Kernel Methods for Statistical Shape Learning." European Conference on Computer Vision, 2006.](https://mlanthology.org/eccv/2006/rathi2006eccv-comparative/) doi:10.1007/11889762_9BibTeX
@inproceedings{rathi2006eccv-comparative,
title = {{Comparative Analysis of Kernel Methods for Statistical Shape Learning}},
author = {Rathi, Yogesh and Dambreville, Samuel and Tannenbaum, Allen R.},
booktitle = {European Conference on Computer Vision},
year = {2006},
pages = {96-107},
doi = {10.1007/11889762_9},
url = {https://mlanthology.org/eccv/2006/rathi2006eccv-comparative/}
}