Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis ar...
In the context of shape and image modeling by manifold learning, we focus on the problem of denoising. A set of shapes or images being known through given samples, we capture its s...
Shrinkage is a well known and appealing denoising technique. The use of shrinkage is known to be optimal for Gaussian white noise, provided that the sparsity on the signal's ...
This paper presents a solution to the cloud removal problem, based in a recently developed image fusion methodology consisting in applying a 1-D pseudo-Wigner distribution (PWD) t...
In this paper, a discriminative representation method of head images is proposed, which is based on parts and poses for identity-independent head pose estimation. Head images are ...