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Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > extraction de traits caractéristiques
extraction de traits caractéristiquesSynonyme(s)extraction des caractéristiques extraction de primitiveVoir aussi |
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Facet segmentation-based line segment extraction for large-scale point clouds / Yangbin Lin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Facet segmentation-based line segment extraction for large-scale point clouds Type de document : Article/Communication Auteurs : Yangbin Lin, Auteur ; Cheng Wang, Auteur ; Bili Chen, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4839 - 4854 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] exploration de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (Auteur) As one of the most common features in the man-made environments, straight lines play an important role in many applications. In this paper, we present a new framework to extract line segments from large-scale point clouds. The proposed method is fast to produce results, easy for implementation and understanding, and suitable for various point cloud data. The key idea is to segment the input point cloud into a collection of facets efficiently. These facets provide sufficient information for determining linear features in the local planar region and make line segment extraction become relatively convenient. Moreover, we introduce the concept “number of false alarms” into 3-D point cloud context to filter the false positive line segment detections. We test our approach on various types of point clouds acquired from different ways. We also compared the proposed method with several other methods and provide both quantitative and visual comparison results. The experimental results show that our algorithm is efficient and effective, and produce more accurate and complete line segments than the comparative methods. To further verify the accuracy of the line segments extracted by the proposed method, we also present a line-based registration framework, which employs these line segments on point clouds registration. Numéro de notice : A2017-656 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2639025 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2639025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87066
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 4839 - 4854[article]Multiple cues-based active contours for target contour tracking under sophisticated background / Peng Lv in The Visual Computer, vol 33 n°9 (September 2017)
[article]
Titre : Multiple cues-based active contours for target contour tracking under sophisticated background Type de document : Article/Communication Auteurs : Peng Lv, Auteur ; Qingjie Zhao, Auteur ; Yanming Chen, Auteur ; Liujun Zhao, Auteur Année de publication : 2017 Article en page(s) : pp 1103 - 1119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] couleur (variable spectrale)
[Termes IGN] détection de contours
[Termes IGN] séquence d'images
[Termes IGN] texture d'image
[Termes IGN] traçage
[Termes IGN] vidéo numériqueRésumé : (auteur) In this paper, we propose a novel target contour tracking method under sophisticated background using the multiple cues-based active contour model. To locate the target position, a contour-based mean-shift tracker is designed which combines both color and texture information. To reduce the adverse impact of sophisticated background and also accelerate the curve motion, we propose a two-layer-based target appearance model that combines both discriminative pre-learned-based global layer and voting-based local layer. The proposed appearance model is able to extract rough target region from the complex background, which provides important target region information for our active contour model. We subsequently introduce a dynamical shape model to provide prior target shape information for more stable segmentation. To obtain accurate target boundaries, we design a new multiple cues-based active contour model which integrates with target edge, discriminative region, and shape information. The experimental results on 30 video sequences demonstrate that the proposed method outperforms other competitive contour tracking methods under various tracking environment. Numéro de notice : A2017-406 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-016-1268-2 En ligne : https://doi.org/10.1007/s00371-016-1268-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86286
in The Visual Computer > vol 33 n°9 (September 2017) . - pp 1103 - 1119[article]Urban building reconstruction from raw LiDAR point data / Cheng Yi in Computer-Aided Design, vol 9x (2017)
[article]
Titre : Urban building reconstruction from raw LiDAR point data Type de document : Article/Communication Auteurs : Cheng Yi, Auteur ; et al., Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] espace urbain
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de pointsRésumé : (auteur) We present a method for automatic reconstruction of the volumetric structures of urban buildings, directly from raw LiDAR point clouds. Given the large-scale LiDAR data from a group of urban buildings, we take advantage of the “divide-and-conquer” strategy to decompose the entire point clouds into a number of subsets, each of which corresponds to an individual building. For each urban building, we determine its upward direction and partition the corresponding point data into a series of consecutive blocks, achieved by investigating the distributions of feature points of the building along the upward direction. Next, we propose a novel algorithm, Spectral Residual Clustering (SRC), to extract the primitive elements within the contours of blocks from the sectional point set, which is formed by registering the series of consecutive slicing points. Subsequently, we detect the geometric constraints among primitive elements through individual fitting, and perform constrained fitting over all primitive elements to obtain the accurate contour. On this basis, we execute 3D modeling operations, like extrusion, lofting or sweeping, to generate the 3D models of blocks. The final accurate 3D models are generated by applying the union Boolean operations over the block models. We evaluate our reconstruction method on a variety of raw LiDAR scans to verify its robustness and effectiveness. Numéro de notice : A2017-429 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cad.2017.07.005 En ligne : https://doi.org/10.1016/j.cad.2017.07.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86328
in Computer-Aided Design > vol 9x (2017)[article]3D local feature BKD to extract road information from mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
[article]
Titre : 3D local feature BKD to extract road information from mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Yang Bisheng, Auteur ; Yuan Liu, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 329 - 343 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classificateur
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] densité des points
[Termes IGN] données localisées 3D
[Termes IGN] estimation par noyau
[Termes IGN] extraction du réseau routier
[Termes IGN] semis de points
[Termes IGN] télémétrie laser mobile
[Termes IGN] variable binaireRésumé : (Auteur) Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Numéro de notice : A2017-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.06.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86479
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 329 - 343[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Joint classification and contour extraction of large 3D point clouds / Timo Hackel in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
[article]
Titre : Joint classification and contour extraction of large 3D point clouds Type de document : Article/Communication Auteurs : Timo Hackel, Auteur ; Jan Dirk Wegner, Auteur ; Konrad Schindler, Auteur Année de publication : 2017 Article en page(s) : pp 231 - 245 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] attribut sémantique
[Termes IGN] classification dirigée
[Termes IGN] compréhension de l'image
[Termes IGN] densité des points
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données massives
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) We present an effective and efficient method for point-wise semantic classification and extraction of object contours of large-scale 3D point clouds. What makes point cloud interpretation challenging is the sheer size of several millions of points per scan and the non-grid, sparse, and uneven distribution of points. Standard image processing tools like texture filters, for example, cannot handle such data efficiently, which calls for dedicated point cloud labeling methods. It turns out that one of the major drivers for efficient computation and handling of strong variations in point density, is a careful formulation of per-point neighborhoods at multiple scales. This allows, both, to define an expressive feature set and to extract topologically meaningful object contours.
Semantic classification and contour extraction are interlaced problems. Point-wise semantic classification enables extracting a meaningful candidate set of contour points while contours help generating a rich feature representation that benefits point-wise classification. These methods are tailored to have fast run time and small memory footprint for processing large-scale, unstructured, and inhomogeneous point clouds, while still achieving high classification accuracy. We evaluate our methods on the semantic3d.net benchmark for terrestrial laser scans with
points.Numéro de notice : A2017-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.05.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.05.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86476
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 231 - 245[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Learning and transferring deep joint spectral–spatial features for hyperspectral classification / Jingxiang Yang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkSimultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkInteractive shearing for terrain visualization : an expert study / Jonas Buddeberg in Geoinformatica, vol 21 n° 3 (July - September 2017)PermalinkA novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkChange detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis / Arati Paul in Geocarto international, vol 32 n° 6 (June 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)PermalinkSelf-taught feature learning for hyperspectral image classification / Ronald Kemker in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkAnalytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data / André Dittrich in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkDeep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkDictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification / Minchao Ye in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)Permalink