An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions
Abstract
The statistical theory of shape plays a prominent role in applications such as object recognition and medical imaging. An important parameterized family of probability densities defined on the locations of landmark-points is given by the offset-normal shape distributions introduced in [7]. In this paper we present an EM algorithm for learning the parameters of the offset-normal shape distribution from shape data. To improve model flexibility we also provide an EM algorithm to learn mixtures of offset-normal distributions.
Cite
Text
Welling. "An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions." Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.Markdown
[Welling. "An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions." Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.](https://mlanthology.org/aistats/2005/welling2005aistats-expectation/)BibTeX
@inproceedings{welling2005aistats-expectation,
title = {{An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions}},
author = {Welling, Max},
booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics},
year = {2005},
pages = {389-396},
volume = {R5},
url = {https://mlanthology.org/aistats/2005/welling2005aistats-expectation/}
}