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Auteur Lijun Wei
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Titre : Location retrieval using qualitative place signatures of visible landmarks Type de document : Article/Communication Auteurs : Lijun Wei , Auteur ; Valérie Gouet-Brunet
Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2022 Projets : 1-Pas de projet / Importance : 52 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] descripteur
[Termes IGN] lieu
[Termes IGN] point de repère
[Termes IGN] reconnaissance d'objets
[Termes IGN] relation spatialeRésumé : (auteur) Location retrieval based on visual information is to retrieve the location of an agent (e.g. human, robot) or the area they see by comparing the observations with a certain form of representation of the environment. Existing methods generally require precise measurement and storage of the observed environment features, which may not always be robust due to the change of season, viewpoint, occlusion, etc. They are also challenging to scale up and may not be applicable for humans due to the lack of measuring/imaging devices. Considering that humans often use less precise but easily produced qualitative spatial language and high-level semantic landmarks when describing an environment, a qualitative location retrieval method is proposed in this work by describing locations/places using qualitative place signatures (QPS), defined as the perceived spatial relations between ordered pairs of co-visible landmarks from viewers' perspective. After dividing the space into place cells each with individual signatures attached, a coarse-to-fine location retrieval method is proposed to efficiently identify the possible location(s) of viewers based on their qualitative observations. The usability and effectiveness of the proposed method were evaluated using openly available landmark datasets, together with simulated observations by considering the possible perception error. Numéro de notice : P2022-009 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2208.00783 Date de publication en ligne : 26/07/2022 En ligne : https://doi.org/10.48550/arXiv.2208.00783 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101879
contenu dans The 23rd international conference on MultiMedia Modeling, MMM 2017 / Laurent Amsaleg (2017)
Titre : Adaptive and optimal combination of local features for image retrieval Type de document : Article/Communication Auteurs : Neelanjan Bhowmik Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2017 Autre Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Collection : Lecture notes in Computer Science, ISSN 0302-9743 Projets : POEME / Da Silva, Jean-Claude Conférence : MMM 2017, 23rd international conference on Multimedia Modeling 04/01/2017 06/01/2017 Reykjavik Islande Proceedings Springer Importance : pp 76 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modèle de régression
[Termes IGN] point d'intérêt
[Termes IGN] recherche d'image basée sur le contenuRésumé : (Auteur) With the large number of local feature detectors and descriptors in the literature of Content-Based Image Retrieval (CBIR), in this work we propose a solution to predict the optimal combination of features, for improving image retrieval performances, based on the spatial complementarity of interest point detectors. We review several complementarity criteria of detectors and employ them in a regression based prediction model, designed to select the suitable detectors combination for a dataset. The proposal can improve retrieval performance even more by selecting optimal combination for each image (and not only globally for the dataset), as well as being profitable in the optimal fitting of some parameters. The proposal is appraised on three state-of-the-art datasets to validate its effectiveness and stability. The experimental results highlight the importance of spatial complementarity of the features to improve retrieval, and prove the advantage of using this model to optimally adapt detectors combination and some parameters. Numéro de notice : C2017-021 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-319-51814-5_7 Date de publication en ligne : 01/06/2017 En ligne : https://doi.org/10.1007/978-3-319-51814-5_7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88988 Documents numériques
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Adaptive and optimal combination - preprintAdobe Acrobat PDFAugmenting vehicle localization accuracy with cameras and 3D road infrastructure database / Lijun Wei (2015)
Titre : Augmenting vehicle localization accuracy with cameras and 3D road infrastructure database Type de document : Article/Communication Auteurs : Lijun Wei Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2015 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 8925 Conférence : ECCV 2014, 13th European conference on computer vision, workshops 06/09/2014 12/09/2014 Zurich Suisse Proceedings Springer Importance : pp 194 - 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] base de données routières
[Termes IGN] données localisées 3D
[Termes IGN] précision de localisation
[Termes IGN] signalisation routière
[Termes IGN] véhicule automobileRésumé : (auteur) Accurate and continuous vehicle localization in urban environments has been an important research problem in recent years. In this paper, we propose a landmark based localization method using road signs and road markings. The principle is to associate the online detections from onboard cameras with the landmarks in a pre-generated road infrastructure database, then to adjust the raw vehicle pose predicted by the inertial sensors. This method was evaluated with data sequences acquired in urban streets. The results prove the contribution of road signs and road markings for reducing the trajectory drift as absolute control points. Numéro de notice : C2014-016 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : GEOMATIQUE/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-319-16178-5_13 Date de publication en ligne : 19/03/2015 En ligne : http://dx.doi.org/10.1007/978-3-319-16178-5_13 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83180
