Descripteur
Documents disponibles dans cette catégorie (485)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests / Nina Amiri in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)
[article]
Titre : Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests Type de document : Article/Communication Auteurs : Nina Amiri, Auteur ; Przemyslaw Polewski, Auteur ; Marco Heurich, Auteur ; Peter Krzystek, Auteur ; Andrew K. Skidmore, Auteur Année de publication : 2018 Article en page(s) : pp 265 - 274 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Bavière (Allemagne)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier local
[Termes IGN] lasergrammétrie
[Termes IGN] Pinophyta
[Termes IGN] segmentation
[Termes IGN] semis de points
[Vedettes matières IGN] Inventaire forestierMots-clés libres : Bavarian Forest National Park Résumé : (auteur) The development of new approaches to individual tree crown delineation for forest inventory and management is an important area of ongoing research. The increasing availability of high density ALS (Airborne Laser Scanning) point clouds offers the opportunity to segment the individual tree crowns and deduce their geometric properties with a high level of accuracy. Top-down segmentation methods such as normalized cut are established approaches for delineation of single trees in ALS point clouds. However, overlapping crowns and branches of nearby trees frequently cause over- and under-segmentation due to the difficulty of defining a single criterion for stopping the partitioning process. In this work, we investigate an adaptive stopping criterion based on the visual appearance of trees within the point clouds. We focus on coniferous trees due to their well-defined crown shapes in comparison to deciduous trees. This approach is based on modeling the coniferous tree crowns with elliptic paraboloids to infer whether a given 3D scene contains exactly one or more than one tree. For each processed scene, candidate tree peaks are generated from local maxima found within the point cloud. Next, paraboloids are fitted at the peaks using a random sample consensus procedure and classified based on their geometric properties. The decision to stop or continue partitioning is determined by finding a set of non-overlapping paraboloids. Experiments were performed on three plots from the Bavarian Forest National Park in Germany. Based on validation data from the field inventory, results show that our approach improves the segmentation quality by up to 10% across plots with different properties, such as average tree height and density. This indicates that the new adaptive stopping criterion for normalized cut segmentation is capable of delineating tree crowns more accurately than a static stopping criterion based on a constant Ncut threshold value. Numéro de notice : A2018-670 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.006 Date de publication en ligne : 29/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90405
in ISPRS Journal of photogrammetry and remote sensing > vol 141 (July 2018) . - pp 265 - 274[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018073 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018072 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Human mobility semantics analysis : a probabilistic and scalable approach / Xiaohui Guo in Geoinformatica, vol 22 n° 3 (July 2018)
[article]
Titre : Human mobility semantics analysis : a probabilistic and scalable approach Type de document : Article/Communication Auteurs : Xiaohui Guo, Auteur ; Richong Zhang, Auteur ; Xudong Liu, Auteur ; Jinpeng Huai, Auteur Année de publication : 2018 Article en page(s) : pp 507 - 539 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données localisées
[Termes IGN] données spatiotemporelles
[Termes IGN] mobilité humaine
[Termes IGN] programmation stochastique
[Termes IGN] segmentation sémantique
[Termes IGN] trace numériqueRésumé : (Auteur) The popularity of smart mobile devices generated data, e.g., check-ins and geo-tagged status, offers new opportunity for better understanding human mobility regularity. Existing works on this problem usually resort to explicit frequency statistics models, such as association rules and sequential patterns, and rely on Euclidean distance to measure the spatial dependence. However, the noisiness and uncertainty natures of geospatial data hinder these methods’ application on human mobility in robust and intuitive way. Moreover, the mobility spatial data volume and accumulation speed challenge the traditional methods in efficiency, scalability, and time-space complexity aspects. In this context, we leverage full Bayesian sequential modeling, to revisit mobility regularity discovery from high level probabilistic semantic knowledge perspective, and to alleviate the inherent in mobility modeling and geo-data noisiness induced uncertainty. Specifically, the mobility semantics is embodied by virtue of underlying geospatial topics and topical transitions of mobility trajectories. A classic variational inference is derived to estimate posterior and predictive probabilities, and furthermore, the stochastic optimization is utilized to mitigate the costly computational overhead in message passing subroutine. The experimental results confirm that our approach not only reasonably recognizes the geospatial mobility semantic patterns, but also scales up well to embrace the massive spatial-temporal human mobility activity data. Numéro de notice : A2018-310 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-017-0295-0 Date de publication en ligne : 10/04/2017 En ligne : https://doi.org/10.1007/s10707-017-0295-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90757
in Geoinformatica > vol 22 n° 3 (July 2018) . - pp 507 - 539[article]Range-image: Incorporating sensor topology for lidar point cloud processing / Pierre Biasutti in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)
[article]
Titre : Range-image: Incorporating sensor topology for lidar point cloud processing Type de document : Article/Communication Auteurs : Pierre Biasutti , Auteur ; Jean-François Aujol, Auteur ; Mathieu Brédif , Auteur ; Aurélie Bugeau, Auteur Année de publication : 2018 Projets : GOTMI / Papadakis, Nicolas Article en page(s) : pp 367 - 375 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection de partie cachée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] histogramme
[Termes IGN] image 2D
[Termes IGN] objet mobile
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] topologie capteurRésumé : (auteur) This paper proposes a novel methodology for lidar point cloud processing that takes advantage of the implicit topology of various lidar sensors to derive 2D images from the point cloud while bringing spatial structure to each point. The interest of such a methodology is then proved by addressing the problems of segmentation and disocclusion of mobile objects in 3D lidar scenes acquired using street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle those problems directly in the 3D space. This work promotes an alternative approach by using this image representation of the 3D point cloud, taking advantage of the fact that the problem of disocclusion has been intensively studied in the 2D image processing community over the past decade. Using the image derived from the sensor data by exploiting the sensor topology, a semi-automatic segmentation procedure based on depth histograms is presented. Then, a variational image inpainting technique is introduced to reconstruct the areas that are occluded by objects. Experiments and validation on real data prove the effectiveness of this methodology both in terms of accuracy and speed. Numéro de notice : A2018-230 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.6.367 Date de publication en ligne : 01/06/2018 En ligne : https://doi.org/10.14358/PERS.84.6.367 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90171
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 6 (juin 2018) . - pp 367 - 375[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2018061 RAB Revue Centre de documentation En réserve L003 Disponible Documents numériques
en open access
Range-image: Incorporating sensor topology - version HALAdobe Acrobat PDF A voxel- and graph-based strategy for segmenting man-made infrastructures using perceptual grouping laws: comparison and evaluation / Yusheng Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)
[article]
Titre : A voxel- and graph-based strategy for segmenting man-made infrastructures using perceptual grouping laws: comparison and evaluation Type de document : Article/Communication Auteurs : Yusheng Xu, Auteur ; Ludwig Hoegner, Auteur ; Sebastian Tuttas, Auteur ; Uwe Stilla, Auteur Année de publication : 2018 Article en page(s) : pp 377 - 391 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bati
[Termes IGN] données localisées 3D
[Termes IGN] octree
[Termes IGN] partition des données
[Termes IGN] prise en compte du contexte
[Termes IGN] reconstruction 3D
[Termes IGN] scène urbaine
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] théorie des graphes
[Termes IGN] voxelRésumé : (auteur) In this paper, we report a novel strategy for segmenting 3D point clouds using a voxel structure and graph-based clustering with perceptual grouping laws. It provides a completely automatic solution for partitioning point clouds of man-made infrastructure. Two different segmentation methods using voxel and supervoxel structures are presented and evaluated. To increase the efficiency and the robustness of the segmentation process, the voxelization with octree-based structure is introduced, which can suppress effects of noise, outliers, and unevenly distributed point densities as well. The clustering of over-segmented voxels and supervoxels is achieved using graph theory on the basis of the local contextual information, which is commonly conducted merely with pairwise information in conventional clustering algorithms. The graphical model is constructed according to perceptual grouping laws, considering geometric information associated with points. Experiments using both laser scanning and photogrammetric point clouds have demonstrated that the proposed methods can achieve good results, especially complex scenes and nonplanar object surfaces, with F1-measures better than 0.67 for all the testing samples. Quantitative comparisons between the proposed approaches and other representative segmentation methods also confirm the effectiveness and the efficiency of the former. Moreover, a series of experiments is carried out, to investigate the methods' sensitivity with respect to various parameters on the segmentation results. Numéro de notice : A2018-231 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.6.377 Date de publication en ligne : 01/06/2018 En ligne : https://doi.org/10.14358/PERS.84.6.377 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90173
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 6 (juin 2018) . - pp 377 - 391[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2018061 RAB Revue Centre de documentation En réserve L003 Disponible Do semantic parts emerge in convolutional neural networks? / Abel Gonzalez-Garcia in International journal of computer vision, vol 126 n° 5 (May 2018)
[article]
Titre : Do semantic parts emerge in convolutional neural networks? Type de document : Article/Communication Auteurs : Abel Gonzalez-Garcia, Auteur ; Davide Modolo, Auteur ; Vittorio Ferrari, Auteur Année de publication : 2018 Article en page(s) : pp 476 - 494 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] reconnaissance d'objets
[Termes IGN] rectangle englobant minimum
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) Semantic object parts can be useful for several visual recognition tasks. Lately, these tasks have been addressed using Convolutional Neural Networks (CNN), achieving outstanding results. In this work we study whether CNNs learn semantic parts in their internal representation. We investigate the responses of convolutional filters and try to associate their stimuli with semantic parts. We perform two extensive quantitative analyses. First, we use ground-truth part bounding-boxes from the PASCAL-Part dataset to determine how many of those semantic parts emerge in the CNN. We explore this emergence for different layers, network depths, and supervision levels. Second, we collect human judgements in order to study what fraction of all filters systematically fire on any semantic part, even if not annotated in PASCAL-Part. Moreover, we explore several connections between discriminative power and semantics. We find out which are the most discriminative filters for object recognition, and analyze whether they respond to semantic parts or to other image patches. We also investigate the other direction: we determine which semantic parts are the most discriminative and whether they correspond to those parts emerging in the network. This enables to gain an even deeper understanding of the role of semantic parts in the network. Numéro de notice : A2018-408 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-1048-0 Date de publication en ligne : 17/10/2017 En ligne : https://doi.org/10.1007/s11263-017-1048-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90882
in International journal of computer vision > vol 126 n° 5 (May 2018) . - pp 476 - 494[article]Large-scale supervised learning for 3D Point cloud labeling : Semantic3d.Net / Timo Hackel in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 5 (mai 2018)PermalinkJournées de la recherche IGN 2018 / Anonyme in Géomatique expert, n° 121 (mars - avril 2018)PermalinkCut-Pursuit algorithm for regularizing nonsmooth functionals with graph total variation / Hugo Raguet (2018)PermalinkPermalinkPermalinkPermalinkLocalisation d'objets urbains à partir de sources multiples dont des images aériennes / Lionel Pibre (2018)PermalinkLocalisation par l'image en milieu urbain : application à la réalité augmentée / Antoine Fond (2018)PermalinkModélisation spatio-temporelle multi-niveau à base d'ontologies pour le suivi de la dynamique en imagerie satellitaire / Fethi Ghazouani (2018)PermalinkPermalinkA stixel approach for enhancing semantic image segmentation using prior map information / Sylvain Jonchery (2018)PermalinkSuperPoint Graph : segmentation sémantique de nuages de points LiDAR à grande échelle / Loïc Landrieu (2018)PermalinkOpen land cover from OpenStreetMap and remote sensing / Michael Schultz in International journal of applied Earth observation and geoinformation, vol 63 (December 2017)PermalinkCut Pursuit: Fast algorithms to learn piecewise constant functions on general weighted graphs / Loïc Landrieu in SIAM Journal on Imaging Sciences, vol 10 n° 4 (November 2017)PermalinkTree size thresholds produce biased estimates of forest biomass dynamics / Eric B. Searle in Forest ecology and management, vol 400 (15 September 2017)PermalinkMapping theories of transformative learning / Daniel Casebeer in Cartographica, vol 52 n° 3 (Fall 2017)PermalinkJoint classification and contour extraction of large 3D point clouds / Timo Hackel in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkVertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds / Hamid Hamraz in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkA novel semisupervised active-learning algorithm for hyperspectral image classification / Zengmao Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkUrban 3D segmentation and modelling from street view images and LiDAR point clouds / Pouria Babahajiani in Machine Vision and Applications, sans n° ([01/06/2017])PermalinkGeometric features and their relevance for 3D point cloud classification / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-1/W1 (May 2017)PermalinkThe differentiation of point symbols using selected visual variables in the mobile augmented reality system / Łukasz Halik in Cartographic journal (the), Vol 54 n° 2 (May 2017)PermalinkSemantic 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)PermalinkUAS, sensors, and data processing in agroforestry: a review towards practical applications / Luis Padua in International Journal of Remote Sensing IJRS, vol 38 n° 8-10 (April 2017)PermalinkA classification-segmentation framework for the detection of individual trees in dense MMS point cloud data acquired in urban areas / Martin Weinmann in Remote sensing, vol 9 n° 3 (March 2017)PermalinkSemi-parametric segmentation of multiple series using a DP-Lasso strategy / Karine Bertin in Journal of Statistical Computation and Simulation, vol 87 n° 6 (2017)PermalinkCartographie de l'occupation des sols à partir de séries temporelles d'images satellitaires à hautes résolutions : identification et traitement des données mal étiquetées / Charlotte Pelletier (2017)PermalinkContributions méthodologiques pour la caractérisation des milieux par imagerie optique et lidar / Nesrine Chehata (2017)PermalinkFusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)PermalinkHow to combine lidar and very high resolution multispectral images for forest stand segmentation? / Clément Dechesne (2017)PermalinkMise en place d’un processus de dessin automatisé de plans d’intérieurs à partir de nuages de points acquis par LIDAR / Léa Talec (2017)PermalinkPré-segmentation pour la classification faiblement supervisée de scènes urbaines à partir de nuages de points 3D LIDAR / Stéphane Guinard (2017)PermalinkRéseaux de neurones convolutifs pour la segmentation sémantique et l'apprentissage d'invariants de couleur / Damien Fourure (2017)PermalinkSegmentation sémantique de données de télédétection multimodale : application aux peuplements forestiers / Clément Dechesne (2017)PermalinkTélédétection pour l'observation des surfaces continentales, ch. 6. Méthodes de traitement de données lidar / Clément Mallet (2017)PermalinkWeakly supervised segmentation-aided classification of urban scenes from 3D LIDAR point clouds / Stéphane Guinard (2017)PermalinkAn attempt to determine the effect of increase of observation correlations on detectability and identifiability of a single gross error / Witold Proszynski in Geodesy and cartography, vol 65 n° 2 (December 2016)PermalinkAutomatic parameter selection for intensity-based registration of imagery to LiDAR data / Ebadat Ghanbari Parmehr in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkPlanar-based adaptive down-sampling of point clouds / Yun-Jou Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 12 (December 2016)PermalinkA robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data / Hamid Hamraz in International journal of applied Earth observation and geoinformation, vol 52 (October 2016)PermalinkEfficient terrestrial laser scan segmentation exploiting data structure / Hamid Mahmoudabadi in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkMeasures of transport mode segmentation of trajectories / Adrain C. Prelipcean in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)PermalinkAutomatic extraction of road networks from GPS traces / Jia Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)PermalinkFrom Aristotle to semantic analysis / Allan Gajadhar in Research information, n° 85 (August - September 2016)PermalinkA novel computer-aided tree species identification method based on burst wind segmentation of 3D bark textures / Alice Ahlem Othmani in Machine Vision and Applications, vol 27 n° 5 (July 2016)Permalink