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Thierry Viéville

Researcher at French Institute for Research in Computer Science and Automation

Publications -  147
Citations -  3605

Thierry Viéville is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Neural coding & Artificial neural network. The author has an hindex of 30, co-authored 142 publications receiving 3431 citations. Previous affiliations of Thierry Viéville include Intuitive Surgical & Institut national de la recherche agronomique.

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Real time correlation-based stereo: algorithm, implementations and applications

TL;DR: The algorithm that has been described is not the most sophisticated available but it has made it robust and reliable thanks to a number of improvements and has shown that real time stereo is possible today at low-cost and can be applied in real applications.
Book ChapterDOI

Canonic representations for the geometries of multiple projective views

TL;DR: It is shown how a special decomposition of general projection matrices, called canonic, enables us to build geometric descriptions for a system of cameras which are invariant with respect to a given group of transformations.
Journal ArticleDOI

Canonical Representations for the Geometries of Multiple Projective Views

TL;DR: This work presents a new unified representation which will be useful when dealing with multiple views in the case of uncalibrated cameras, and shows how a special decomposition of a set of two or three general projection matrices, called canonic, enables us to build geometric descriptions for a system of cameras which are invariant with respect to a given group of transformations.
Journal ArticleDOI

Overview of facts and issues about neural coding by spikes

TL;DR: The aim is to demystify some aspects of coding with spike-timing, through a simple review of well-understood technical facts regarding spike coding, to better understanding of the extent to which computing and modeling with spiking neuron networks might be biologically plausible and computationally efficient.