D
David Coeurjolly
Researcher at University of Lyon
Publications - 141
Citations - 2187
David Coeurjolly is an academic researcher from University of Lyon. The author has contributed to research in topics: Digital geometry & Discrete geometry. The author has an hindex of 24, co-authored 128 publications receiving 1900 citations. Previous affiliations of David Coeurjolly include Institut national des sciences Appliquées de Lyon & Lyon College.
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Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning
Morgan A. Schmitz,Matthieu Heitz,Nicolas Bonneel,Fred Maurice Ngolè Mboula,David Coeurjolly,Marco Cuturi,Gabriel Peyré,Jean-Luc Starck +7 more
TL;DR: A new nonlinear dictionary learning method for histograms in the probability simplex that leverages optimal transport theory, relying on Wasserstein barycenters instead of the usual matrix product between dictionary and codes, allowing for nonlinear relationships between atoms and the reconstruction of input data.
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Optimal Separable Algorithms to Compute the Reverse Euclidean Distance Transformation and Discrete Medial Axis in Arbitrary Dimension
TL;DR: This paper presents time optimal algorithms to solve the reverse Euclidean distance transformation and the reversible medial axis extraction problems for d-dimensional images and presents a d- dimensional medial axis filtering process that allows us to control the quality of the reconstructed shape.
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A comparative evaluation of length estimators of digital curves
David Coeurjolly,Reinhard Klette +1 more
TL;DR: This paper compares previously published length estimators in image analysis having digitized curves as input and suggests a new gradient-based method for length estimation and combines a previously proposed length estimator for straight segments with a polygonalization method.
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Generalizations of angular radial transform for 2D and 3D shape retrieval
TL;DR: This paper proposes an 2D extension of the angular radial transform, called GART, which allows applying ART to images while insuring robustness to all possible rotations and to perspective deformations, and a 3D shape descriptor, called 3D ART, which has the same properties that the original transform.
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Discrete bisector function and Euclidean skeleton in 2D and 3D
TL;DR: A new definition and an algorithm for the discrete bisector function is proposed, which is an important tool for analyzing and filtering Euclidean skeletons, and a new thinning algorithm which produces homotopic discrete Euclidesan skeletons is introduced.