Show simple item record

Biologically Inspired Dynamic Textures for Probing Motion Perception

hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorVacher, Jonathan
hal.structure.identifierInstitut de Neurosciences de la Timone [INT]
dc.contributor.authorMeso, Andrew
hal.structure.identifierInstitut de Neurosciences de la Timone [INT]
dc.contributor.authorPerrinet, Laurent U.
HAL ID: 3934
ORCID: 0000-0002-9536-010X
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorPeyré, Gabriel
HAL ID: 1211
dc.date.accessioned2018-09-11T11:23:28Z
dc.date.available2018-09-11T11:23:28Z
dc.date.issued2015
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17994
dc.language.isoenen
dc.subjectmotionen
dc.subjectTexture synthesisen
dc.subjectpsychophysicsen
dc.subjectBayesian inferenceen
dc.subjectperceptionen
dc.subject.ddc621.3en
dc.titleBiologically Inspired Dynamic Textures for Probing Motion Perceptionen
dc.typeCommunication / Conférence
dc.description.abstractenPerception 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.en
dc.identifier.citationpages17en
dc.relation.ispartoftitleAdvances in Neural Information Processing Systems 28 (NIPS 2015)en
dc.relation.ispartofpublnameProc. NIPS 2015en
dc.relation.ispartofdate2015
dc.subject.ddclabelTraitement du signalen
dc.relation.conftitleTwenty-ninth Annual Conference on Neural Information Processing Systems (NIPS)en
dc.relation.confdate2015-12
dc.relation.confcityMontrealen
dc.relation.confcountryCanadaen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2018-09-11T11:19:16Z
hal.author.functionaut
hal.author.functionaut
hal.author.functionaut
hal.author.functionaut


Files in this item

Thumbnail
Name:
MotionClouds-NIPS(1).pdf
Size:
1.414Mb
Format:
PDF
View/Open

This item appears in the following Collection(s)

Show simple item record