Jonathan Vacher

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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)
This work extends the dynamic texture model [3] testing aspects of its parametrization with an application in psychophysics. Generally the model involves: • Dynamic visual stimulation for cortical recording and psychophysics • Texture synthesis • Biologically relevant stimulation parameters • Mathematical framework • Fast implementation Psychophysics(More)
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