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Auteur Loïc Landrieu
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Researcher at LASTIG, STRUDEL team (September 2015 - March 2023) then at LIGM (ENPC)
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Titre : Parallel cut pursuit for minimization of the graph total variation Type de document : Article/Communication Auteurs : Hugo Raguet, Auteur ; Loïc Landrieu , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2019 Conférence : ICML 2019, Workshop on Learning and Reasoning with Graph-Structured Representations in International Conference on Machine Learning 15/06/2019 15/06/2019 Long Beach Californie - Etats-Unis Open Access Proceedings Importance : 6 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme Cut Pursuit
[Termes IGN] optimisation (mathématiques)Résumé : (auteur) We present a parallel version of the cut-pursuit algorithm for minimizing functionals involving the graph total variation. We show that the decomposition of the iterate into constant connected components, which is at the center of this method, allows for the seamless parallelization of the otherwise costly graph-cut based refinement stage. We demonstrate experimentally the efficiency of our method in a wide variety of settings, from simple denoising on huge graphs to more complex inverse problems with nondifferentiable penalties. We argue that our approach combines the efficiency of graph-cuts based optimizers with the versatility and ease of parallelization of traditional proximal. Numéro de notice : C2019-051 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Poster nature-HAL : Poster-avec-CL DOI : 10.48550/arXiv.1905.02316 Date de publication en ligne : 07/05/2019 En ligne : https://graphreason.github.io/papers/10.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93350
Titre : Point cloud oversegmentation with graph-structured deep metric learning Type de document : Article/Communication Auteurs : Loïc Landrieu , Auteur ; Mohamed Boussaha , Auteur Editeur : Computer vision foundation CVF Année de publication : 2019 Projets : 1-Pas de projet / Conférence : CVPR 2019, IEEE Conference on Computer Vision and Pattern Recognition 16/06/2019 20/06/2019 Long Beach Californie - Etats-Unis Open Access Proceedings Importance : pp 7432 - 7441 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] données localisées 3D
[Termes IGN] graphe
[Termes IGN] réseau neuronal artificiel
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) We propose a new supervized learning framework foroversegmenting 3D point clouds into superpoints. We castthis problem as learning deep embeddings of the local ge-ometry and radiometry of 3D points, such that the border ofobjects presents high contrasts. The embeddings are com-puted using a lightweight neural network operating on thepoints’ local neighborhood. Finally, we formulate pointcloud oversegmentation as a graph partition problem withrespect to the learned embeddings.This new approach allows us to set a new state-of-the-artin point cloud oversegmentation by a significant margin, ona dense indoor dataset (S3DIS) and a sparse outdoor one(vKITTI). Our best solution requires over five times fewersuperpoints to reach similar performance than previouslypublished methods on S3DIS. Furthermore, we show thatour framework can be used to improve superpoint-basedsemantic segmentation algorithms, setting a new state-of-the-art for this task as well. Numéro de notice : C2019-017 Affiliation des auteurs : LASTIG MATIS (2012-2019) Autre URL associée : vers CVF Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/CVPR.2019.00762 Date de publication en ligne : 09/01/2020 En ligne : https://doi.org/10.1109/CVPR.2019.00762 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93351
Titre : Supervized segmentation with graph-structured deep metric learning Type de document : Article/Communication Auteurs : Loïc Landrieu , Auteur ; Mohamed Boussaha , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2019 Projets : 1-Pas de projet / Conférence : ICML 2019, Workshop on Learning and Reasoning with Graph-Structured Representations in International Conference on Machine Learning 15/06/2019 15/06/2019 Long Beach Californie - Etats-Unis Open Access Proceedings Importance : 15 p. Langues : Français (fre) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] graphe
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) We present a fully-supervized method for learning to segment data structured by an adjacency graph. We introduce the graph-structured contrastive loss, a loss function structured by a ground truth segmentation. It promotes learning vertex embeddings which are homogeneous within desired segments, and have high contrast at their interface. Thus, computing a piecewise-constant approximation of such embeddings produces a graph-partition close to the objective segmentation. This loss is fully backpropagable, which allows us to learn vertex embeddings with deep learning algorithms. We evaluate our methods on a 3D point cloud oversegmentation task, defining a new state-of-the-art by a large margin. These results are based on the published work of Landrieu and Boussaha 2019. Numéro de notice : C2019-050 Affiliation des auteurs : LASTIG MATIS (2012-2019) Autre URL associée : vers ArXiv Nature : Poster nature-HAL : Poster-avec-CL DOI : 10.48550/arXiv.1905.04014 Date de publication en ligne : 19/05/2019 En ligne : https://graphreason.github.io/papers/4.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92819 Time-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series / Vivien Sainte Fare Garnot (2019)
Titre : Time-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu , Auteur ; Sébastien Giordano , Auteur ; Nesrine Chehata , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : 1-Pas de projet / Conférence : IGARSS 2019, IEEE International Geoscience And Remote Sensing Symposium 28/07/2019 02/08/2019 Yokohama Japon Proceedings IEEE Importance : 4 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] cultures
[Termes IGN] image Sentinel-MSI
[Termes IGN] série temporelleRésumé : (auteur) In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respective importance of spatial and temporal structures in such data. With this objective, we consider several designs of convolutional, recurrent, and hybrid neural networks, and assess their performance on a large dataset of freely available Sentinel-2 imagery. We find that the best-performing approaches are hybrid configurations for which most of the parameters (up to 90%) are allocated to modeling the temporal structure of the data. Our results thus constitute a set of guidelines for the design of bespoke deep learning models for crop type classification. Numéro de notice : C2019-018 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : URL ArXiv Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2019.8900517 Date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1109/IGARSS.2019.8900517 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93352 Crop-rotation structured classification using multi-source sentinel images and LPIS for crop type mapping / Simon Bailly (2018)
Titre : Crop-rotation structured classification using multi-source sentinel images and LPIS for crop type mapping Type de document : Article/Communication Auteurs : Simon Bailly , Auteur ; Sébastien Giordano , Auteur ; Loïc Landrieu , Auteur ; Nesrine Chehata , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : 1-Pas de projet / Conférence : IGARSS 2018, IEEE International Geoscience And Remote Sensing Symposium, observing, understanding and forecasting the dynamics of our planet 22/07/2018 27/07/2018 Valencia Espagne Proceedings IEEE Importance : pp 1950 - 1953 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] image multitemporelle
[Termes IGN] image Sentinel-MSI
[Termes IGN] surveillance agricoleRésumé : (auteur) Automatic analysis of Sentinel image time series is recommended for monitoring agricultural land use in Europe. To improve classification capacities, we propose a temporal structured classification combining Sentinel images and former vintages of the Land-Parcel IdentAutomatic analysis of Sentinel image time series is recommended for monitoring agricultural land use in Europe. To improve classification capacities, we propose a temporal structured classification combining Sentinel images and former vintages of the Land-Parcel Identification System. Inter-annual crop rotations are learned and combined with the satellite images using a Conditional Random Field. The proposed methodology is tested on a 233 km2 study area located in France and with a 25 categories national nomenclature. The classification results are globally improved.ification System. Inter-annual crop rotations are learned and combined with the satellite images using a Conditional Random Field. The proposed methodology is tested on a 233 km2 study area located in France and with a 25 categories national nomenclature. The classification results are globally improved. Numéro de notice : C2018-054 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2018.8518427 Date de publication en ligne : 05/11/2018 En ligne : https://doi.org/10.1109/IGARSS.2018.8518427 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91343 Cut-Pursuit algorithm for regularizing nonsmooth functionals with graph total variation / Hugo Raguet (2018)PermalinkPermalinkPermalinkSuperPoint Graph : segmentation sémantique de nuages de points LiDAR à grande échelle / Loïc Landrieu (2018)PermalinkCut Pursuit: Fast algorithms to learn piecewise constant functions on general weighted graphs / Loïc Landrieu in SIAM Journal on Imaging Sciences, vol 10 n° 4 (November 2017)PermalinkA structured regularization framework for spatially smoothing semantic labelings of 3D point clouds / Loïc Landrieu in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)PermalinkAnalyse de séries temporelles d’images Sentinel et intégration de connaissances pour la classification en milieu agricole / Simon Bailly (2017)PermalinkComparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)PermalinkPré-segmentation pour la classification faiblement supervisée de scènes urbaines à partir de nuages de points 3D LIDAR / Stéphane Guinard (2017)PermalinkWeakly supervised segmentation-aided classification of urban scenes from 3D LIDAR point clouds / Stéphane Guinard (2017)Permalink