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Auteur Valérie Gouet-Brunet
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Titre : Learning scene geometry for visual localization in challenging conditions Type de document : Article/Communication Auteurs : Nathan Piasco , Auteur ; Désiré Sidibé, Auteur ; Valérie Gouet-Brunet , Auteur ; Cédric Demonceaux, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Conférence : ICRA 2019, International Conference on Robotics and Automation 20/05/2019 24/05/2019 Montréal Québec - Canada Proceedings IEEE Importance : pp 9094 - 9100 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse visuelle
[Termes IGN] appariement d'images
[Termes IGN] carte de profondeur
[Termes IGN] descripteur
[Termes IGN] géométrie de l'image
[Termes IGN] image RVB
[Termes IGN] localisation basée vision
[Termes IGN] précision de localisation
[Termes IGN] prise de vue nocturne
[Termes IGN] robotique
[Termes IGN] scène urbaine
[Termes IGN] variation diurne
[Termes IGN] variation saisonnière
[Termes IGN] vision par ordinateurRésumé : (auteur) We propose a new approach for outdoor large scale image based localization that can deal with challenging scenarios like cross-season, cross-weather, day/night and longterm localization. The key component of our method is a new learned global image descriptor, that can effectively benefit from scene geometry information during training. At test time, our system is capable of inferring the depth map related to the query image and use it to increase localization accuracy. We are able to increase recall@1 performances by 2.15% on cross-weather and long-term localization scenario and by 4.24% points on a challenging winter/summer localization sequence versus state-of-the-art methods. Our method can also use weakly annotated data to localize night images across a reference dataset of daytime images. Numéro de notice : C2019-002 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICRA.2019.8794221 Date de publication en ligne : 12/08/2019 En ligne : http://doi.org/10.1109/ICRA.2019.8794221 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93774 Documents numériques
en open access
Learning scene geometry... - pdf auteurAdobe Acrobat PDF Multimodal scene understanding: algorithms, applications and deep learning, ch. 8. Multimodal localization for embedded systems: a survey / Imane Salhi (2019)
Titre de série : Multimodal scene understanding: algorithms, applications and deep learning, ch. 8 Titre : Multimodal localization for embedded systems: a survey Type de document : Chapitre/Contribution Auteurs : Imane Salhi , Auteur ; Martyna Poreba , Auteur ; Erwan Piriou, Auteur ; Valérie Gouet-Brunet , Auteur ; Maroun Ojail, Auteur Editeur : Londres, New York : Academic Press Année de publication : 2019 Importance : pp 199 - 278 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] compréhension de l'image
[Termes IGN] fusion de données
[Termes IGN] géopositionnement
[Termes IGN] instrument embarqué
[Termes IGN] navigation automobile
[Termes IGN] navigation pédestre
[Termes IGN] réalité mixteRésumé : (Auteur) Localization by jointly exploiting multimodal information, like cameras, inertial measurement units (IMU), and global navigation satellite system (GNSS) data, is an active key research topic for autonomous embedded systems such as smart glasses or drones. These systems have become topical for acquisition, modeling, and interpretation for scene understanding. The exploitation of different sensor types improves the robustness of the localization, e.g. by merging the accuracy of one sensor with the reactivity of another one in a flexible manner. This chapter presents a survey of the existing multimodal techniques dedicated to the localization of autonomous embedded systems. Both the algorithmic and the hardware architecture sides are investigated in order to provide a global overview of the key elements to be considered when designing these embedded systems. Several applications in different domains (e.g. localization for mapping, pedestrian localization, automotive navigation and mixed reality) are presented to illustrate the importance of such systems nowadays in scene understanding. Numéro de notice : H2019-001 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1016/B978-0-12-817358-9.00014-7 Date de publication en ligne : 02/08/2019 En ligne : https://doi.org/10.1016/B978-0-12-817358-9.00014-7 Format de la ressource électronique : URL chapitre Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93300
Titre : Perspective-n-learned-point: pose estimation from relative depth Type de document : Article/Communication Auteurs : Nathan Piasco , Auteur ; Désiré Sidibé, Auteur ; Cédric Demonceaux, Auteur ; Valérie Gouet-Brunet , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Conférence : BMVC 2019, British Machine Vision Conference 09/09/2019 12/09/2019 Cardiff Royaume-Uni OA Proceedings Importance : 15 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de profondeur
[Termes IGN] classification par réseau neuronal
[Termes IGN] estimation de pose
[Termes IGN] géométrie de l'image
[Termes IGN] recherche d'image basée sur le contenuRésumé : (Auteur) In this paper we present an online camera pose estimation method that combines Content-Based Image Retrieval (CBIR) and pose refinement based on a learned representation of the scene geometry extracted from monocular images. Our pose estimation method is two-step, we first retrieve an initial 6 Degrees of Freedom (DoF) location of an unknown-pose query by retrieving the most similar candidate in a pool of geo-referenced images. In a second time, we refine the query pose with a Perspective-n-Point (PnP) algorithm where the 3D points are obtained thanks to a generated depth map from the retrieved image candidate. We make our method fast and lightweight by using a common neural network architecture to generate the image descriptor for image indexing and the depth map used to create the 3D points required in the PnP pose refinement step. We demonstrate the effectiveness of our proposal through extensive experimentation on both indoor and outdoor scenes, as well as generalisation capability of our method to unknown environment. Finally, we show how to deploy our system even if geometric information is missing to train our monocular-image-to-depth neural networks. Numéro de notice : C2019-025 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans Date de publication en ligne : 12/11/2019 En ligne : https://bmvc2019.org/wp-content/uploads/papers/0981-paper.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94320 Documents numériques
en open access
Perspective-n-learned-point ... - pdf auteurAdobe Acrobat PDF SUMAC 2019, 1st workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2019)
Titre : SUMAC 2019, 1st workshop on Structuring and Understanding of Multimedia heritAge Contents Type de document : Article/Communication Auteurs : Valérie Gouet-Brunet , Éditeur scientifique ; Margarita Khokhlova , Éditeur scientifique ; Liming Chen, Éditeur scientifique ; Sander Münster, Éditeur scientifique ; Sander Münster Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2019 Projets : 1-Pas de projet / Gouet-Brunet, Valérie Conférence : MM 2019, 27th ACM International Conference on Multimedia 21/10/2019 25/10/2019 Nice France Proceedings ACM Importance : pp 2726 - 2727 Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information
[Termes IGN] bibliothèque numérique
[Termes IGN] données massives
[Termes IGN] interface homme-machine
[Termes IGN] multimedia
[Termes IGN] patrimoine culturel
[Termes IGN] patrimoine immobilier
[Termes IGN] recherche scientifique
[Termes IGN] valorisationRésumé : (auteur) SUMAC 2019 is the first workshop on Structuring and Understanding of Multimedia heritAge Contents. It is held in Nice, France on October 21, 2019 and is co-located with the 27th ACM International Conference on Multimedia. Its objective is to present and discuss the latest and most significant trends and challenges in the analysis, structuring and understanding of multimedia contents dedicated to the valorization of heritage, with the emphasis on the unlocking of and access to the big data of the past. A representative scope of Computer Science methodologies dedicated to the processing of multimedia heritage contents and their exploitation is covered by the works presented, with the ambition of advancing and raising awareness about this fully developing research field. Numéro de notice : 25324 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : GEOMATIQUE/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3343031.3350554 En ligne : https://doi.org/10.1145/3343031.3350554 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93622 SUMAC 2019: The 1st workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2019)
Titre : SUMAC 2019: The 1st workshop on Structuring and Understanding of Multimedia heritAge Contents Type de document : Actes de congrès Auteurs : Valérie Gouet-Brunet , Auteur ; Margarita Khokhlova , Auteur ; Liming Chen, Auteur ; Sander Münster, Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2019 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : SUMAC 2019, 1st workshop on Structuring and Understanding of Multimedia heritAge Contents 21/10/2019 21/10/2019 Nice France Proceedings ACM Langues : Anglais (eng) Résumé : (auteur) SUMAC 2019 is the first workshop on Structuring and Understanding of Multimedia heritAge Contents. It is held in Nice, France on October 21, 2019 and is co-located with the 27th ACM International Conference on Multimedia. Its objective is to present and discuss the latest and most significant trends and challenges in the analysis, structuring and understanding of multimedia contents dedicated to the valorization of heritage, with the emphasis on the unlocking of and access to the big data of the past. A representative scope of Computer Science methodologies dedicated to the processing of multimedia heritage contents and their exploitation is covered by the works presented, with the ambition of advancing and raising awareness about this fully developing research field. Numéro de notice : 17724 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : 10.1145/3343031.3350554 Date de publication en ligne : 15/10/2019 En ligne : https://dl.acm.org/doi/10.1145/3343031.3350554 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100299 Vision-based localization with discriminative features from heterogeneous visual data / Nathan Piasco (2019)PermalinkUnsupervised detection of ruptures in spatial relationships in video sequences based on log‑likelihood ratio / Abdalbassir Abou-Elailah in Pattern Analysis and Applications, vol 21 n° 3 (August 2018)PermalinkA survey on visual-based localization : on the benefit of heterogeneous data / Nathan Piasco in Pattern recognition, vol 74 (February 2018)PermalinkAdéquation algorithme architecture pour la localisation basée image sur système embarqué / David Vandergucht (2018)PermalinkPermalinkComparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs / Abraham Montoya Obeso (2018)PermalinkPermalinkPanorama de la recherche à l'IGN sur la localisation d'une caméra à partir d'images / Nathan Piasco (2018)PermalinkProjet ANR ALEGORIA : structurAtion et vaLorisation du patrimoinE géoGraphique icOnogRaphIque démAtérialisé / Valérie Gouet-Brunet (2018)PermalinkPermalinkPermalinkSemantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery / Clément Dechesne in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkPermalinkCombination of image descriptors for the exploration of cultural photographic collections / Neelanjan Bhowmik in Journal of Electronic Imaging, vol 26 n° 1 (January - February 2017)PermalinkPermalinkHow to combine lidar and very high resolution multispectral images for forest stand segmentation? / Clément Dechesne (2017)PermalinkPermalinkPermalinkPermalinkPermalink
- Member of the steering committee of the Time Machine Organisation, association resulting from the CSA Time Machine project. See https://www.timemachine.eu/time-machine-organisation/