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A Neural Field Model for Motion Estimation

Emilien Tlapale 1 Pierre Kornprobst 1 Guillaume S. Masson 2 Olivier Faugeras 1 
CRISAM - Inria Sophia Antipolis - Méditerranée , INRIA Rocquencourt, ENS-PSL - École normale supérieure - Paris, UNS - Université Nice Sophia Antipolis (1965 - 2019), CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : We propose a bio-inspired approach to motion estimation based on recent neuroscience findings concerning the motion pathway. Our goal is to identify the key biological features in order to reach a good compromise between bio-inspiration and computational efficiency. Here we choose the neural field formalism which provides a sound mathematical framework to describe the model at a macroscopic scale. Within this framework we define the cortical activity as coupled integro-differential equations and we prove the well-posedness of the model. We show how our model performs on some classical computer vision videos, and we compare its behaviour against the visual system on a simple classical video used in psychophysics. As a whole, this article contributes to bring new ideas from computational neuroscience in the domain of computer vision, concerning modelling principles and mathematical formalism.
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Submitted on : Wednesday, July 17, 2013 - 4:13:38 PM
Last modification on : Friday, August 5, 2022 - 3:50:46 AM

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Emilien Tlapale, Pierre Kornprobst, Guillaume S. Masson, Olivier Faugeras. A Neural Field Model for Motion Estimation. Mathematical Image Processing, 2011, Orléans, France. pp.159-180, ⟨10.1007/978-3-642-19604-1_9⟩. ⟨hal-00845749⟩



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