Principal component analysis (PCA) along with its extensions to manifolds and outlier
contaminated data have been indispensable in computer vision and machine learning. In …

The study of visual illusions has proven to be a very useful approach in vision science. In
this work we start by showing that, while convolutional neural networks (CNNs) trained for …

We present an adaptation algorithm focused on the description of the data changes under
different acquisition conditions. When considering a source and a destination domain, the …

In vision science, cascades of Linear+ Nonlinear transforms are very successful in modeling
a number of perceptual experiences. However, the conventional literature is usually too …

Kernel methods are powerful machine learning techniques which use generic non-linear
functions to solve complex tasks. They have a solid mathematical foundation and exhibit …

A novel wavelet-based scheme to increase coefficient independence in hyperspectral
images is introduced for lossless coding. The proposed regression wavelet analysis (RWA) …

Recent studies invoke the superiority of the multivariate Total Correlation concept over the
conventional pairwise measures of functional connectivity in biological networks. Those …

It has been demonstrated many times that the behavior of the human visual system is
connected to the statistics of natural images. Since machine learning relies on the statistics …

Information theory is an outstanding framework for measuring uncertainty, dependence, and
relevance in data and systems. It has several desirable properties for real-world …

Abstract Divisive Normalization and the Wilson–Cowan equations are well-known influential
models of nonlinear neural interaction (Carandini and Heeger in Nat Rev Neurosci 13 (1) …