6 search results - page 1 / 2 » Efficient computation of robust low-rank matrix approximatio... |
CVPR
2010
IEEE
13 years 3 months ago
2010
IEEE
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer vision applications. The workhorse of this class of problems has long been the ...
CVPR
2005
IEEE
14 years 7 months ago
2005
IEEE
Matrix factorization has many applications in computer vision. Singular Value Decomposition (SVD) is the standard algorithm for factorization. When there are outliers and missing ...
SCIA
2009
Springer
13 years 11 months ago
2009
Springer
Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problems is that they can be efficiently solved usin...
CVPR
2010
IEEE
14 years 1 months ago
2010
IEEE
Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, ...
ICCV
2011
IEEE
12 years 5 months ago
2011
IEEE
Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dic...