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Biologically Inspired Dynamic Textures for Probing Motion Perception

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MotionClouds-NIPS(1).pdf (1.414Mb)
Date
2015
Dewey
Traitement du signal
Sujet
motion; Texture synthesis; psychophysics; Bayesian inference; perception
Conference name
Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS)
Conference date
12-2015
Conference city
Montreal
Conference country
Canada
Book title
Advances in Neural Information Processing Systems 28 (NIPS 2015)
Publisher
Proc. NIPS 2015
Year
2015
URI
https://basepub.dauphine.fr/handle/123456789/17994
Collections
  • CEREMADE : Publications
Metadata
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Author
Vacher, Jonathan
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Meso, Andrew
202046 Institut de Neurosciences de la Timone [INT]
Perrinet, Laurent U.
202046 Institut de Neurosciences de la Timone [INT]
Peyré, Gabriel
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Type
Communication / Conférence
Item number of pages
17
Abstract (EN)
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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