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Variational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)
[article]
Titre : Variational bayesian compressive multipolarization indoor radar imaging Type de document : Article/Communication Auteurs : Van Ha Tang, Auteur ; Abdesselam Bouzerdoum, Auteur ; Son Lam Phung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7459 - 7474 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] acquisition comprimée
[Termes IGN] détection à travers-le-mur
[Termes IGN] estimation bayesienne
[Termes IGN] fouillis d'échos
[Termes IGN] image radar
[Termes IGN] inférence statistique
[Termes IGN] modèle stochastique
[Termes IGN] polarisation
[Termes IGN] positionnement en intérieur
[Termes IGN] reconstruction d'imageRésumé : (auteur) This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced. Numéro de notice : A2021-647 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3051955 Date de publication en ligne : 26/01/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3051955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98354
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7459 - 7474[article]Background segmentation in multicolored illumination environments / Nikolas Ladas in The Visual Computer, vol 37 n° 8 (August 2021)
[article]
Titre : Background segmentation in multicolored illumination environments Type de document : Article/Communication Auteurs : Nikolas Ladas, Auteur ; Paris Kaimakis, Auteur ; Yiorgos Chrysanthou, Auteur Année de publication : 2021 Article en page(s) : pp 2221 - 2233 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification pixellaire
[Termes IGN] détection d'ombre
[Termes IGN] éclairage
[Termes IGN] éclairement lumineux
[Termes IGN] modèle stochastique
[Termes IGN] objectif grand angulaire
[Termes IGN] réflectance
[Termes IGN] segmentation d'imageRésumé : (auteur) We present an algorithm for the segmentation of images into background and foreground regions. The proposed algorithm utilizes a physically based formulation of scene appearance which explicitly models the formation of shadows originating from color light sources. This formulation enables a probabilistic model to distinguish between shadows and foreground objects in challenging images. A key component of the proposed method is an algorithm for estimating the illumination arriving at the scene. We evaluate our algorithm using synthetic and real-world data and show that the proposed method performs favorably against other commonly used segmentation methods. Numéro de notice : A2021-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01981-8 Date de publication en ligne : 06/10/2020 En ligne : https://doi.org/10.1007/s00371-020-01981-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98225
in The Visual Computer > vol 37 n° 8 (August 2021) . - pp 2221 - 2233[article]Structure-aware indoor scene reconstruction via two levels of abstraction / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
[article]
Titre : Structure-aware indoor scene reconstruction via two levels of abstraction Type de document : Article/Communication Auteurs : Hao Fang, Auteur ; Cihui Pan, Auteur ; Hui Huang, Auteur Année de publication : 2021 Article en page(s) : pp 155 - 170 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image optique
[Termes IGN] maillage
[Termes IGN] maille triangulaire
[Termes IGN] niveau d'abstraction
[Termes IGN] polygone
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] scène intérieureRésumé : (auteur) In this paper, we propose a novel approach that reconstructs the indoor scene in a structure-aware manner and produces two meshes with different levels of abstraction. To be precise, we start from the raw triangular mesh of indoor scene and decompose it into two parts: structure and non-structure objects. On the one hand, structure objects are defined as significant permanent parts in the indoor environment such as floors, ceilings and walls. In the proposed algorithm, structure objects are abstracted by planar primitives and assembled into a polygonal structure mesh. This step produces a compact structure-aware watertight model that decreases the complexity of original mesh by three orders of magnitude. On the other hand, non-structure objects are movable objects in the indoor environment such as furniture and interior decoration. Meshes of these objects are repaired and simplified according to their relationship with respect to structure primitives. Finally, the union of all the non-structure meshes and structure mesh comprises the scene mesh. Note that structure mesh and scene mesh preserve various levels of abstraction and can be used for different applications according to user preference. Our experiments on both LIDAR and RGBD data scanned from simple to large scale indoor scenes indicate that the proposed framework generates structure-aware results while being robust and scalable. It is also compared qualitatively and quantitatively against popular mesh approximation, floorplan generation and piecewise-planar surface reconstruction methods to demonstrate its performance. Numéro de notice : A2021-561 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.06.007 Date de publication en ligne : 23/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98119
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 155 - 170[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021081 SL Revue Centre de documentation Revues en salle Disponible 081-2021083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Deformation analysis of a reference wall towards the uncertainty investigation of terrestrial laser scanners / Berit Schmitz in Journal of applied geodesy, vol 15 n° 3 (July 2021)
[article]
Titre : Deformation analysis of a reference wall towards the uncertainty investigation of terrestrial laser scanners Type de document : Article/Communication Auteurs : Berit Schmitz, Auteur ; Heiner Kuhlmann, Auteur ; Christoph Holst, Auteur Année de publication : 2021 Article en page(s) : pp 189-206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] déformation d'édifice
[Termes IGN] déformation géométrique
[Termes IGN] données laser
[Termes IGN] incertitude géométrique
[Termes IGN] matrice de covariance
[Termes IGN] modèle de déformation tectonique
[Termes IGN] modèle stochastique
[Termes IGN] mur
[Termes IGN] qualité des données
[Termes IGN] tachéomètre électronique
[Termes IGN] télémètre laser terrestreRésumé : (Auteur) The perfect knowledge about the scanned object’s geometry is essential for the empirical analysis of the stochastic properties of terrestrial laser scanners (TLSs). The Bonn reference wall is intended to be used as a reference for TLS quality investigations. Therefore, it is necessary to know the geometry of the wall at each time of scanning to avoid the misinterpretation of possible movements as systematic effects in the scanner. For this reason, we investigate the stability of the Bonn reference wall in this study. This includes the definition of a geodetic datum, the quantification of displacements, and the establishment of a suited deformation model. Since we discover a movement of about 1 mm within one day and up to 7 mm over the year, it is necessary to establish a cause-response deformation model to correct the wall movements in the scans. This study proposes two dynamic deformation models to compensate for the movements of the wall within one day and within a year. Our results show that it is better to measure the initial geometry of the wall each day since 89 % of the relative movements can be reduced to a maximum of 0.25 mm with a standard deviation of 0.16 mm (0.23 mm without modeling). If the shape is not initially known each day, the standard deviation of the displacements can be reduced from 1.10 mm to 0.61 mm, but the largest residuals still amount up to 2.5 mm, which is not sufficient for stochastic TLS investigations. Numéro de notice : A2021-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0025 Date de publication en ligne : 25/03/2021 En ligne : https://doi.org/10.1515/jag-2020-0025 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98103
in Journal of applied geodesy > vol 15 n° 3 (July 2021) . - pp 189-206[article]Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)
[article]
Titre : Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization Type de document : Article/Communication Auteurs : Jiali Han, Auteur ; Mengqi Rong, Auteur ; Hanqing Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 57 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire de Markov
[Termes IGN] données lidar
[Termes IGN] espace intérieur
[Termes IGN] maillage
[Termes IGN] programmation linéaire
[Termes IGN] Ransac (algorithme)
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] vectorisationRésumé : (Auteur) Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Field (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches. Numéro de notice : A2021-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.019 Date de publication en ligne : 15/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97779
in ISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 57 - 74[article]Réservation
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