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A Neural Field Model for Motion Estimation

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Mathematical Image Processing

Part of the book series: Springer Proceedings in Mathematics ((PROM,volume 5))

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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Authors and Affiliations

  1. NeuroMathComp Project Team, INRIA Sophia-Antipolis Méditerranée, 2004 Route des Lucioles, 06902, Sophia Antipolis, France

    Émilien Tlapale, Pierre Kornprobst & Olivier Faugeras

  2. INCM,UMR 6193 CNRS-Université de la Méditerranée 31, chemin Joseph Aiguier, 13402, Marseille cedex, France

    Guillaume S. Masson

Authors
  1. Émilien Tlapale
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  2. Pierre Kornprobst
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  3. Guillaume S. Masson
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  4. Olivier Faugeras
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Corresponding author

Correspondence to Pierre Kornprobst .

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Editors and Affiliations

  1. , UMR 6628-MAPMO, Université d'Orléans, Orléans Cedex 2, 45067, France

    Maïtine Bergounioux

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Tlapale, É., Kornprobst, P., Masson, G.S., Faugeras, O. (2011). A Neural Field Model for Motion Estimation. In: Bergounioux, M. (eds) Mathematical Image Processing. Springer Proceedings in Mathematics, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19604-1_9

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