Many successful models for predicting attention in a scene involve three main steps:
convolution with a set of filters, a center-surround mechanism and spatial pooling to …

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 …

In a previous work (X. Otazu, M. Vanrell, & CA Párraga, 2008b), we showed how several
brightness induction effects can be predicted using a simple multiresolution wavelet model …

Tone-mapping operators (TMOs) are designed to generate perceptually similar low-dynamic-
range images from high-dynamic-range ones. We studied the performance of 15 TMOs in …

We reproduce suprathreshold perception phenomena, specifically visual illusions, by Wilson–
Cowan (WC)-type models of neuronal dynamics. Our findings show that the ability to …

We consider the evolution model proposed in Bertalmío (Front Comput Neurosci 8: 71,
2014), Bertalmío et al.(IEEE Trans Image Process 16 (4): 1058–1072, 2007) to describe …

There are many ways in which the human visual system works to reduce the inherent
redundancy of the visual information in natural scenes, coding it in an efficient way. The non …

This study proposes a new representation of discrete Non‐linear AutoRegressive with
eXogenous inputs (NARX) model by developing its coefficients associated to the input, the …

Visual illusions may be explained by the likelihood of patches in real-world images, as
argued by input-driven paradigms in Neuro-Science. However, neither the data nor the tools …

Brightness induction is the modulation of the perceived intensity of an area by the luminance
of surrounding areas. Recent neurophysiological evidence suggests that brightness …