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Beatriz Marcotegui
Researcher at PSL Research University
Publications - 83
Citations - 4325
Beatriz Marcotegui is an academic researcher from PSL Research University. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 24, co-authored 81 publications receiving 2856 citations. Previous affiliations of Beatriz Marcotegui include Colorado School of Mines & Mines ParisTech.
Papers
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Proceedings ArticleDOI
KPConv: Flexible and Deformable Convolution for Point Clouds
Hugues Thomas,Charles R. Qi,Jean-Emmanuel Deschaud,Beatriz Marcotegui,François Goulette,Leonidas J. Guibas +5 more
TL;DR: KPConv is a new design of point convolution, i.e. that operates on point clouds without any intermediate representation, that outperform state-of-the-art classification and segmentation approaches on several datasets.
Journal ArticleDOI
TeleOphta: Machine learning and image processing methods for teleophthalmology
Etienne Decencière,Guy Cazuguel,Guy Cazuguel,Xiwei Zhang,Guillaume Thibault,J.-C. Klein,Fernand Meyer,Beatriz Marcotegui,Gwenole Quellec,Mathieu Lamard,Ronan Danno,D. Elie,Pascale Massin,Z. Viktor,Ali Erginay,B. Laÿ,Agnès Chabouis +16 more
TL;DR: In this paper, a complete prototype for the automatic detection of normal examinations on a teleophthalmology network for diabetic retinopathy screening is presented, which combines pathological pattern mining methods, with specific lesion detection methods, to extract information from the images.
Journal ArticleDOI
Exudate detection in color retinal images for mass screening of diabetic retinopathy
Xiwei Zhang,Guillaume Thibault,Etienne Decencière,Beatriz Marcotegui,Bruno Lay,Ronan Danno,Guy Cazuguel,Gwenole Quellec,Mathieu Lamard,Pascale Massin,Agnès Chabouis,Zeynep Victor,Ali Erginay +12 more
TL;DR: New preprocessing methods are proposed, which perform not only normalization and denoising tasks, but also detect reflections and artifacts in the image, and a random forest algorithm is used to detect the exudates among the candidates.
Journal ArticleDOI
Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning
Andrés Serna,Beatriz Marcotegui +1 more
TL;DR: Quantitative results prove that the automatic and robust approach to detect, segment and classify urban objects from 3D point clouds not only provides a good performance but is also faster than other works reported in the literature.
Journal ArticleDOI
TerraMobilita/iQmulus urban point cloud analysis benchmark
TL;DR: A very detailed semantic tree for urban scenes is proposed and the capacity of a method to separate the points of the scene into these categories is called analysis, which aims at evaluating the classification, detection and segmentation quality of the submitted results.