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IPMI
2007
Springer

Geometrically Proper Models in Statistical Training

10 years 3 months ago
Geometrically Proper Models in Statistical Training
In deformable model segmentation, the geometric training process plays a crucial role in providing shape statistical priors and appearance statistics that are used as likelihoods. Also, the geometric training process plays a crucial role in providing shape probability distributions in methods finding significant differences between classes. The quality of the training seriously affects the final results of segmentation or of significant difference finding between classes. However, the lack of shape priors in the training stage itself makes it difficult to enforce shape legality, i.e., making the model free of local self-intersection or creases. Shape legality not only yields proper shape statistics but also increases the consistency of parameterization of the object volume and thus proper appearance statistics. In this paper we propose a method incorporating explicit legality constraints in training process. The method is mathematically sound and has proved in practice to lead to shape...
Qiong Han, Derek Merck, Josh Levy, Christina Villa
Added 16 Nov 2009
Updated 16 Nov 2009
Type Conference
Year 2007
Where IPMI
Authors Qiong Han, Derek Merck, Josh Levy, Christina Villarruel, James N. Damon, Edward L. Chaney, Stephen M. Pizer
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