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Valérie Gouet-Brunet
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Documents
Identifiants chercheurs
- valerie-gouet-brunet
- 0000-0003-3666-5146
- IdRef : 174365020
Présentation
**Research director (DR1, French Ministry of Ecology)**
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**IGN - Université Gustave Eiffel, LASTIG Lab.**
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IGN-ENSG, F-94160 Saint-Mande, France
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Valérie Gouet-Brunet is research director of the French Ministry of Ecology and carries out her research in the research department of the French mapping agency (IGN) and the Gustave Eiffel University, within the LaSTIG laboratory. There, she is in charge of researches on the description by content, matching, structuring and indexing of large-scale multimedia collections, with a focus on images. Until 2018, she has headed the IGN's MATIS laboratory (35 members) for 4 years, specialized in mathematics and computer science applied to photogrammetry, computer vision and remote sensing for multi-sensor and multi-source imaging. She obtained a PhD in Computer Vision in 2000 from the University of Montpellier II (France) on the area of color image matching with application to intermediate view synthesis, and an habilitation to conduct research at the Pierre and Marie Curie University (France) in 2008 on the area of content-based structuring of collections of still and animated images.
Her main research areas concern the matching and large-scale indexing of multi-date and multi-source images, currently with the main objectives of image collections interlinking and image-based localization, for the application fields of iconographic heritage promotion and embedded navigation. V. Gouet-Brunet has supervised more than fifty researchers and participated in or coordinated some twenty partnership projects of various kinds (national ANR and FUI projects, European projects, bilateral industrial contracts, international research collaborations) on these themes. She is currently coordinating the ALEGORIA project (French ANR 2017), is a member of the steering committee of the European project CSA Time Machine, and member of the board of the Time Machine Organisation.
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Improving Image Description with Auxiliary Modality for Visual Localization in Challenging ConditionsInternational Journal of Computer Vision, 2021, 129 (1), pp.185-202. ⟨10.1007/s11263-020-01363-6⟩
Article dans une revue
hal-02912239v1
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A survey on Visual-Based Localization: On the benefit of heterogeneous dataPattern Recognition, 2018, 74, pp.90 - 109. ⟨10.1016/j.patcog.2017.09.013⟩
Article dans une revue
hal-01744680v1
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Learning Scene Geometry for Visual Localization in Challenging ConditionsInternational Conference on Robotics and Automation, ICRA 2019, May 2019, Montréal, Canada. pp.9094-9100, ⟨10.1109/ICRA.2019.8794221⟩
Communication dans un congrès
hal-02057378v1
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Geometric Camera Pose Refinement With Learned Depth MapsIEEE Int. Conf. on Image Processing, Sep 2019, Taipei, Taiwan. ⟨10.1109/ICIP.2019.8803014⟩
Communication dans un congrès
hal-02123899v1
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Perspective-n-Learned-Point: Pose Estimation from Relative DepthBritish Machine Vision Conference (BMVC), Sep 2019, Cardiff, United Kingdom
Communication dans un congrès
hal-02190840v1
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Apprentissage de modalités auxiliaires pour la localisation basée visionRFIAP 2018, Reconnaissance des Formes, Image, Apprentissage et Perception, Jun 2018, Marne-la-Vallée, France
Communication dans un congrès
hal-01928002v1
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Localisation Basée Vision : de l'hétérogénéité des approches et des donnéesORASIS - Journées francophones des jeunes chercheurs en vision par ordinateur, Jun 2017, Colleville-sur-Mer, France
Communication dans un congrès
hal-01587287v1
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