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Indoor point cloud segmentation using iterative Gaussian mapping and improved model fitting / Bufan Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : Indoor point cloud segmentation using iterative Gaussian mapping and improved model fitting Type de document : Article/Communication Auteurs : Bufan Zhao, Auteur ; Xianghong Hua, Auteur ; Kegen Yu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7890 - 7907 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données lidar
[Termes IGN] itération
[Termes IGN] modélisation 3D
[Termes IGN] processus gaussien
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] regroupement de points
[Termes IGN] scène intérieure
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) Indoor scene segmentation based on 3-D laser point cloud is important for rebuilding and classification, especially for permanent building structure. However, the existing segmentation methods mainly focus on the large-scale planar structures but ignore the other sharp structures and details, which would cause accuracy degradation in scene reconstruction. To handle this issue, an iterative Gaussian mapping-based segmentation strategy has been proposed in this article, which goes from rough segmentation to refined one iteratively to decompose the indoor scene into detectable point cloud clusters layer by layer. An improved model fitting algorithm based on the maximum likelihood estimation sampling consensus (MLESAC) algorithm is proposed for refined segmentation, which is called the Prior-MLESAC algorithm, to deal with the extraction of both vertical and nonvertical planar and cylindrical structures. The experimental results demonstrate that planar and cylindrical structures are segmented more completely by the proposed strategy, and more details of the indoor structure are restored than other existing methods. Numéro de notice : A2020-681 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2984943 Date de publication en ligne : 16/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2984943 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96205
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7890 - 7907[article]Pedestrian network generation based on crowdsourced tracking data / Xue Yang in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)
[article]
Titre : Pedestrian network generation based on crowdsourced tracking data Type de document : Article/Communication Auteurs : Xue Yang, Auteur ; Luliang Tang, Auteur ; Chang Ren, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1051 - 1074 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] approche participative
[Termes IGN] base de données multi-représentation
[Termes IGN] correction géométrique
[Termes IGN] correction topographique
[Termes IGN] dimension fractale
[Termes IGN] données localisées des bénévoles
[Termes IGN] estimation par noyau
[Termes IGN] mobilité urbaine
[Termes IGN] navigation pédestre
[Termes IGN] regroupement de pointsRésumé : (auteur) Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness. Numéro de notice : A2020-207 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1702197 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.1080/13658816.2019.1702197 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94888
in International journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 1051 - 1074[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020051 RAB Revue Centre de documentation En réserve L003 Disponible Automatic segmentation of urban point clouds based on the gaussian map / Yinghui Wang in Photogrammetric record, vol 28 n° 144 (December 2013 - February 2014)
[article]
Titre : Automatic segmentation of urban point clouds based on the gaussian map Type de document : Article/Communication Auteurs : Yinghui Wang, Auteur ; Wen Hao, Auteur ; Xiaojuan Ning, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 342 - 361 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] courbe de Gauss
[Termes IGN] milieu urbain
[Termes IGN] regroupement de points
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (Auteur) A comprehensive method to segment urban point clouds based on the Gaussian map is presented. The normals of point clouds are firstly mapped to the Gaussian sphere and then partitioned into groups using a mean shift clustering algorithm. Next, a distance-based clustering method is presented to tackle overlapping surfaces which avoids under-segmentation. Based on the properties of the Gaussian map and the geometric information of the points, primitive shapes such as planes, cylinders, cones and spheres are recognised. Trees, cars, street lights and other objects are then segmented by using the distance-based clustering method after removing the planes, cylinders, cones and spheres from the large urban scenes. Finally, a refinement process based on primitive shapes is implemented to improve the segmentation results which effectively avoids over-segmentation. Experimental results demonstrate that the proposed method can be used as a robust way to segment urban point clouds based on the Gaussian map. Numéro de notice : A2013-627 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12041 Date de publication en ligne : 09/12/2013 En ligne : https://doi.org/10.1111/phor.12041 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32763
in Photogrammetric record > vol 28 n° 144 (December 2013 - February 2014) . - pp 342 - 361[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2013041 RAB Revue Centre de documentation En réserve L003 Disponible View generation for multiview maximum disagreement based active learning for hyperspectral image classification / W. Di in IEEE Transactions on geoscience and remote sensing, vol 50 n° 5 Tome 2 (May 2012)
[article]
Titre : View generation for multiview maximum disagreement based active learning for hyperspectral image classification Type de document : Article/Communication Auteurs : W. Di, Auteur ; Melba M. Crawford, Auteur Année de publication : 2012 Article en page(s) : pp 1942 - 1954 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] classification dirigée
[Termes IGN] image AVIRIS
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] regroupement de pointsRésumé : (Auteur) Active learning (AL) seeks to interactively construct a smaller training data set that is the most informative and useful for the supervised classification task. Based on the multiview Adaptive Maximum Disagreement AL method, this study investigates the principles and capability of several approaches for the view generation for hyperspectral data classification, including clustering, random selection, and uniform subset slicing methods, which are then incorporated with dynamic view updating and feature space bagging strategies. Tests on Airborne Visible/Infrared Imaging Spectrometer and Hyperion hyperspectral data sets show excellent performance as compared with random sampling and the simple version support vector machine margin sampling, a state-of-the-art AL method. Numéro de notice : A2012-189 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2168566 En ligne : https://doi.org/10.1109/TGRS.2011.2168566 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31636
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 5 Tome 2 (May 2012) . - pp 1942 - 1954[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012051B RAB Revue Centre de documentation En réserve L003 Disponible Least visible path analysis in raster terrain / M. Lu in International journal of geographical information science IJGIS, vol 22 n° 6-7 (june 2008)
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Titre : Least visible path analysis in raster terrain Type de document : Article/Communication Auteurs : M. Lu, Auteur ; J.F. Zhang, Auteur ; P. Fan, Auteur ; Zhengjie Fan, Auteur Année de publication : 2008 Article en page(s) : pp 645 - 656 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de données
[Termes IGN] C++
[Termes IGN] données matricielles
[Termes IGN] implémentation (informatique)
[Termes IGN] modèle numérique de surface
[Termes IGN] regroupement de points
[Termes IGN] visibilitéRésumé : (Auteur) Least visible path analysis is a basic function in terrain visibility analysis. However, current least visible path planning is constrained to least-cost path computing on a cost surface obtained from visibility information of all the terrain points on digital elevation models. This kind of method ignores the visibility correlation caused by the overlapped part of the adjacent points' reverse viewsheds. With regard to such a correlation, this paper proposes a new method using the amalgamation of reverse viewsheds to find an optimal path with the minimal visibility dominance. Both methods are implemented using C++ programming, and the experimental results show that the new method produces more accurate least visible paths than the traditional one does. Numéro de notice : A2008-225 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810701602062 En ligne : https://doi.org/10.1080/13658810701602062 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29220
in International journal of geographical information science IJGIS > vol 22 n° 6-7 (june 2008) . - pp 645 - 656[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-08041 RAB Revue Centre de documentation En réserve L003 Disponible 079-08042 RAB Revue Centre de documentation En réserve L003 Disponible Evolution of clusters in dynamic point patterns: with a case study of ants' simulation / Maxim Shoshany in International journal of geographical information science IJGIS, vol 21 n° 6-7 (july 2007)PermalinkThree-dimensional SAR imaging of a ground moving target using the INSAR technique / Qian Zhang in IEEE Transactions on geoscience and remote sensing, vol 42 n° 9 (October 2004)PermalinkModélisation des imprécisions géométriques dans les bases de données géographiques : propagations / Benoit Ravel (1996)Permalink