Quick and energy-efficient bayesian computing of binocular disparity using stochastic digital signals

Abstract : Reconstruction of the tridimensional geometry of a visual scene using the binocular disparity information is an important issue in computer vision and mobile robotics, which can be formulated as a bayesian inference problem. However, computation of the full disparity distribution with an advanced bayesian model is usually an intractable problem, and proves computationally challenging even with a simple model. In this paper, we show how probabilistic hardware using distributed memory and alternate representation of data as stochastic bit-streams can solve that problem with high performance and energy efficiency. We put forward a way to express discrete probability distributions using stochastic data representations and perform bayesian fusion using those representations, and show how that approach can be applied to diparity computation. We evaluate the system using a simulated stochastic implementation and discuss possible hardware implementations of such architectures and their potential for sensorimotor processing and robotics.
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Pré-publication, Document de travail
Manuscript initially submitted for publication to International Journal of Approximate reasoning,.. 2016
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https://hal.archives-ouvertes.fr/hal-01366558
Contributeur : Alexandre Coninx <>
Soumis le : mercredi 14 septembre 2016 - 18:54:27
Dernière modification le : lundi 29 mai 2017 - 14:25:41
Document(s) archivé(s) le : jeudi 15 décembre 2016 - 15:48:15

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  • HAL Id : hal-01366558, version 1

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Alexandre Coninx, Pierre Bessière, Jacques Droulez. Quick and energy-efficient bayesian computing of binocular disparity using stochastic digital signals. Manuscript initially submitted for publication to International Journal of Approximate reasoning,.. 2016. <hal-01366558>

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