1088 search results - page 8 / 218 » Robust Principal Component Analysis for Computer Vision |
WACV
2002
IEEE
14 years 13 days ago
2002
IEEE
In this paper we present the results of a comparative study of linear and kernel-based methods for face recognition. The methods used for dimensionality reduction are Principal Co...
CVPR
2003
IEEE
14 years 9 months ago
2003
IEEE
Principal component analysis has proven to be useful for understanding geometric variability in populations of parameterized objects. The statistical framework is well understood ...
COLT
2010
Springer
13 years 5 months ago
2010
Springer
We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for contaminated data in the high dimensional regime, where the number of obse...
SMA
2009
ACM
14 years 5 days ago
2009
ACM
Principal curvatures and principal directions are fundamental local geometric properties. They are well deļ¬ned on smooth surfaces. However, due to the nature as higher order diļ...
ECCV
2010
Springer
13 years 12 months ago
2010
Springer
Manifolds are widely used to model non-linearity arising in a range of computer vision applications. This paper treats statistics on manifolds and the loss of accuracy occurring wh...