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MICCAI
2005
Springer

Multiscale 3D Shape Analysis Using Spherical Wavelets

14 years 4 months ago
Multiscale 3D Shape Analysis Using Spherical Wavelets
Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.
Delphine Nain, Steven Haker, Aaron F. Bobick, Alle
Added 15 Nov 2009
Updated 15 Nov 2009
Type Conference
Year 2005
Where MICCAI
Authors Delphine Nain, Steven Haker, Aaron F. Bobick, Allen Tannenbaum
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