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Investigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)
[article]
Titre : Investigating the role of image retrieval for visual localization Type de document : Article/Communication Auteurs : Martin Humenberger, Auteur ; Yohann Cabon, Auteur ; Noé Pion, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : 1811 - 1836 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse visuelle
[Termes IGN] base de données d'images
[Termes IGN] estimation de pose
[Termes IGN] flou
[Termes IGN] localisation basée image
[Termes IGN] localisation basée vision
[Termes IGN] point de repère
[Termes IGN] précision de localisation
[Termes IGN] Ransac (algorithme)
[Termes IGN] réalité de terrain
[Termes IGN] structure-from-motion
[Termes IGN] vision par ordinateurRésumé : (auteur) Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for both of them. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes which often differs from the requirements of visual localization. In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms. First, we introduce a novel benchmark setup and compare state-of-the-art retrieval representations on multiple datasets using localization performance as metric. Second, we investigate several definitions of “ground truth” for image retrieval. Using these definitions as upper bounds for the visual localization paradigms, we show that there is still significant room for improvement. Third, using these tools and in-depth analysis, we show that retrieval performance on classical landmark retrieval or place recognition tasks correlates only for some but not all paradigms to localization performance. Finally, we analyze the effects of blur and dynamic scenes in the images. We conclude that there is a need for retrieval approaches specifically designed for localization paradigms. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization. Numéro de notice : A2022-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01615-7 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1007/s11263-022-01615-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101070
in International journal of computer vision > vol 130 n° 7 (July 2022) . - 1811 - 1836[article]La modélisation des eaux / Michel Kasser in Géomètre, n° 2197 (décembre 2021)
[article]
Titre : La modélisation des eaux Type de document : Article/Communication Auteurs : Michel Kasser , Auteur Année de publication : 2021 Article en page(s) : pp 41 - 41 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] altimétrie par radar
[Termes IGN] image SWOT
[Termes IGN] océanographie dynamique
[Termes IGN] océanographie spatiale
[Termes IGN] précision centimétrique
[Termes IGN] précision de localisation
[Termes IGN] salinité
[Termes IGN] surface de la mer
[Termes IGN] vague
[Termes IGN] variation temporelleRésumé : (Auteur) Grâce à l’altimétrie radar, il est possible de mesurer la hauteur de la surface des mers, avec des applications fortes pour la connaissance de la Terre. Numéro de notice : A2021-893 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99250
in Géomètre > n° 2197 (décembre 2021) . - pp 41 - 41[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 063-2021111 RAB Revue Centre de documentation En réserve L003 Disponible Analysis of positional uncertainty of road networks in volunteered geographic information with a statistically defined buffer-zone method / Wen-Bin Zhang in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)
[article]
Titre : Analysis of positional uncertainty of road networks in volunteered geographic information with a statistically defined buffer-zone method Type de document : Article/Communication Auteurs : Wen-Bin Zhang, Auteur ; Yee Leung, Auteur ; Jiang-Hong Ma, Auteur Année de publication : 2019 Article en page(s) : pp 1807 - 1828 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données localisées
[Termes IGN] données localisées des bénévoles
[Termes IGN] incertitude de position
[Termes IGN] OpenStreetMap
[Termes IGN] précision de localisation
[Termes IGN] qualité des données
[Termes IGN] réseau routier
[Termes IGN] SIG participatif
[Termes IGN] zone tamponRésumé : (auteur) Volunteered geographic information (VGI) is crowdsourced information that can enrich and enhance research and applications based on geo-referenced data. However, the quality of VGI is of great concern, and positional accuracy is a fundamental basis for the VGI quality assurance. A buffer-zone method can be used for its assessment, but the buffer radius in this technique is subjectively specified; as result, different selections of the buffer radius lead to different positional accuracies. To solve this problem, a statistically defined buffer zone for the positional accuracy assessment in VGI is proposed in this study. To facilitate practical applications, we have also developed an iterative method to obtain a theoretically defined buffer zone. In addition to the positional accuracy assessment, we have derived a measure of positional quality, which comprises the assessment of positional accuracy and the level of confidence in such assessment determined with respect to a statistically defined buffer zone. To illustrate and substantiate the theoretical arguments, both numerical simulations and real-life experiments are performed using OpenStreetMap. The experimental results confirm the high significance of the proposed statistical approach to the buffer zone-based assessment of the positional uncertainty in VGI. Numéro de notice : A2019-390 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1606430 Date de publication en ligne : 29/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1606430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93483
in International journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1807 - 1828[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019092 RAB Revue Centre de documentation En réserve L003 Disponible A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena / Guiming Zhang in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)
[article]
Titre : A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur ; A - Xing Zhu, Auteur Année de publication : 2019 Article en page(s) : pp 1873 - 1893 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Aves
[Termes IGN] carte thématique
[Termes IGN] distribution spatiale
[Termes IGN] données localisées des bénévoles
[Termes IGN] échantillon
[Termes IGN] erreur d'échantillon
[Termes IGN] erreur de positionnement
[Termes IGN] erreur systématique
[Termes IGN] habitat (nature)
[Termes IGN] modèle de simulation
[Termes IGN] phénomène géographique
[Termes IGN] pondération
[Termes IGN] précision de localisation
[Termes IGN] régression logistique
[Termes IGN] representativité
[Termes IGN] science citoyenne
[Termes IGN] Wisconsin (Etats-Unis)Résumé : (auteur) Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy. Numéro de notice : A2019-392 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615071 Date de publication en ligne : 10/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615071 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93490
in International journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1873 - 1893[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019092 RAB Revue Centre de documentation En réserve L003 Disponible
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
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