Multiscale 3D Shape Analysis using Spherical Wavelets.

Citation:

Nain D, Haker S, Bobick A, Tannenbaum AR. Multiscale 3D Shape Analysis using Spherical Wavelets. Med Image Comput Comput Assist Interv. 2005;8 (Pt 2) :459-67. Copy at http://www.tinyurl.com/yy5g4x96

Date Published:

2005

Abstract:

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.

Last updated on 10/07/2016