Biologically Inspired Dynamic Textures for Probing Motion Perception

Abstract : 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 dynamic textures which are defined by stationary Gaussian distributions obtained by the random aggregation of warped patterns. Importantly, we show that this model can equivalently be described as a stochastic partial differential equation. Using this characterization of motion in images, it allows us to recast motion-energy models into a principled Bayesian inference framework. Finally, we apply these textures in order to psychophysically probe speed perception in humans. In this framework, while the likelihood is derived from the generative model, the prior is estimated from the observed results and accounts for the perceptual bias in a principled fashion.
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Communication dans un congrès
Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), Dec 2015, Montreal, Canada. Proc. NIPS 2015, 2015, Proc. NIPS 2015
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Dernière modification le : vendredi 25 mai 2018 - 12:02:06
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  • HAL Id : hal-01225867, version 1
  • ARXIV : 1511.02705

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Jonathan Vacher, Andrew Meso, Laurent U Perrinet, Gabriel Peyré. Biologically Inspired Dynamic Textures for Probing Motion Perception. Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), Dec 2015, Montreal, Canada. Proc. NIPS 2015, 2015, Proc. NIPS 2015. 〈hal-01225867〉

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