Jonathan Vacher

Learn More
This contribution deals with the Heeger-Bergen pyramid-based texture analysis/synthesis algorithm. It brings a detailed explanation of the original algorithm tested on many characteristic examples. Our analysis reproduces the original results, but also brings a minor improvement concerning non-periodic textures. Inspired by visual perception theories,(More)
Perception is often described as a predictive process based on an optimal inference with respect to a generative model. We study here the principled construction of a generative model specifically crafted to probe motion perception. In that context, we first provide an axiomatic, biologically-driven derivation of the model. This model synthesizes random(More)
A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on optimal inference with respect to a generative model. The present study details the complete formulation of such a gen-erative model intended to probe visual motion perception. It is first derived in a set of axiomatic steps(More)
The study of visual perception is often based on the understanding of experimental data corresponding to responses to a stimulus. Therefore, it is as important to have well-defined and conveniently generated stimuli as to realize and model an experiment. To this purpose, we have extended a class of Gaussian stimuli called Motion Clouds with a generative(More)
  • 1