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Three-Dimensional point cloud analysis for building seismic damage information / Fan Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)
[article]
Titre : Three-Dimensional point cloud analysis for building seismic damage information Type de document : Article/Communication Auteurs : Fan Yang, Auteur ; Zhiwei Fan, Auteur ; Chao Wen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 103 - 111 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] analyse de groupement
[Termes IGN] analyse en composantes principales
[Termes IGN] densité des points
[Termes IGN] détection du bâti
[Termes IGN] dommage matériel
[Termes IGN] données localisées 3D
[Termes IGN] extraction de données
[Termes IGN] filtrage de points
[Termes IGN] mur
[Termes IGN] séisme
[Termes IGN] semis de pointsRésumé : (Auteur) Postearthquake building damage assessment requires professional judgment; however, there are factors such as high workload and human error. Making use of Terrestrial Laser Scanning data, this paper presents a method for seismic damage information extraction. This new method is based on principal component analysis calculating the local surface curvature of each point in the point cloud. Then use the nearest point angle algorithm, combined with the data features of the actual measured value to identify point cloud seismic information, and filter the points that tend to the plane by setting the threshold value. Based on the statistical analysis of the normal vector, the raw point cloud data are deplanarized to obtain the preliminary results of seismic damage information. The density clustering algorithm is used to denoise the initially extracted seismic damage information. Ultimately, we can obtain the distribution patterns and characteristics of cracks in the walls of the building. The extraction result of the seismic damage information point cloud data is compared with the photos collected at the site, showing that the algorithm steps successfully identify the crack and shed wall skin information recorded in the site photos (identification rate: 95%). Point cloud distribution maps of cracked and shed siding areas determine quantitative information on seismic damage, providing a higher level of performance and detail than direct contact measurements. Numéro de notice : A2022-065 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00019R3 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.14358/PERS.21-00019R3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99727
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 2 (February 2022) . - pp 103 - 111[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022021 SL Revue Centre de documentation Revues en salle Disponible Analyse contrastive de la perception de la ville entre fictions climatiques et débats publics / Alexandra Li–Combeau-Longuet (2022)
Titre : Analyse contrastive de la perception de la ville entre fictions climatiques et débats publics Type de document : Mémoire Auteurs : Alexandra Li–Combeau-Longuet, Auteur ; Catherine Dominguès , Encadrant ; Sabine Ploux, Encadrant Editeur : Paris : Institut National des Langues et Civilisations Orientales Année de publication : 2022 Projets : PARVIS / Importance : 80 p. Note générale : bibliographie
Master traitement automatique des langues, Parcours Ingénierie MultilingueLangues : Français (fre) Descripteur : [Vedettes matières IGN] Linguistique
[Termes IGN] analyse de données
[Termes IGN] analyse de groupement
[Termes IGN] corpus
[Termes IGN] linguistique informatique
[Termes IGN] villeMots-clés libres : analyse statistique de données textuelles TXM clustering romans de science-fiction Grand Débat National Natural Language Processing (NLP) city statistical analysis of textual data science fiction Résumé : (auteur) Ce travail, s'inscrivant dans le projet PARVIS (PARoles de VIlleS), porte sur l'analyse contrastive de la perception de la ville entre un corpus de débats publics et un corpus de romans de science-fiction (dont des fictions climatiques). Ces corpus ne parlant pas uniquement de la ville, nous utilisons une approche "par lexique" pour définir la ville. Cette approche pose la question de la désambigüisation lexicale, mais aussi de la segmentation en la seule unité comparable entre les deux corpus : la phrase. Nous avons donc commencé par une exploration "gros grain" des corpus afin de formuler des hypothèses sur la perception de la ville, dans laquelle la désambigüisation lexicale et la segmentation en phrase seront abordées. Enfin, une exploration "grain fin" des vecteurs contextuels des mots de la ville a été réalisée dans le but de répondre à ces hypothèses. Note de contenu : Introduction : contexte de travail
Problématiques et objectifs
Partie 1- État de l’art, rappel sur les méthodes utiles
1 Analyse de données textuelles (ADT) ou Textométrie
2 Apprentissage automatique
Partie 2- Exploration du corpus "gros grain" : formulation d’hypothèses sur la perception de la ville
3 Pré-traitements : désambiguïsation
4 Méthode : exploration "gros grains"
5 Résultats de l’exploration "gros grain"
6 Formulation des hypothèses
Partie 3- Exploration "grain fin" du corpus : les contextes d’emploi
7 Méthode : clustering sur les vecteurs contextuels de CamemBERT
8 Analyses des clusters
9 Discussion
Conclusion généraleNuméro de notice : 13867 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Mémoire masters divers Organisme de stage : LASTIG (IGN) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102272 Documents numériques
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rapport_Li-Combeau-Longuet - pdf auteurAdobe Acrobat PDF Improving local adaptive filtering method employed in radiometric correction of analogue airborne campaigns / Lâmân Lelégard (2022)
Titre : Improving local adaptive filtering method employed in radiometric correction of analogue airborne campaigns Type de document : Article/Communication Auteurs : Lâmân Lelégard , Auteur ; Arnaud Le Bris , Auteur ; Sébastien Giordano , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2022 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B3 Projets : HIATUS / Giordano, Sébastien Conférence : ISPRS 2022, Commission 3, 24th ISPRS Congress, Imaging today, foreseeing tomorrow 06/06/2022 11/06/2022 Nice France OA ISPRS Archives Importance : pp 1217 - 1222 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] contraste local
[Termes IGN] correction radiométrique
[Termes IGN] fenêtre (informatique)
[Termes IGN] filtre de Wallis
[Termes IGN] morphologie mathématiqueRésumé : (auteur) An orthophotomosaic is as a single image that can be layered on a map. It is produced from a set of aerial images impaired by radiometric inhomogeneity mostly due to atmospheric phenomena, like hotspot, haze or high altitude clouds shadows as well as the camera itself, like lens vignetting. These create some unsightly radiometric inhomogeneity in the mosaic that could be corrected by using a local adaptive filter, also named Wallis filter. Yet this solution leads to a significant loss of contrast at small scales. This current work introduces two elementary studies. In a first time, in order to quantify the loss of contrast due to the use of Wallis filter, a simple multi-scale score is proposed based on mathematical morphology operations. In a second time, an optimal window size for the filter is identified by considering some systematic radiometric behaviours in the images forming the mosaic through Principal Component Analysis (PCA). These two elementary studies are preliminary steps leading to a method of radiometric correction combining Wallis filtering and PCA. Numéro de notice : C2022-015 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B3-2022-1217-2022 Date de publication en ligne : 31/05/2022 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1217-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100841
Titre : Metalearning : Applications to automated machine learning and data mining Type de document : Monographie Auteurs : Pavel Brazdil, Auteur ; Jan N. van Rijn, Auteur ; Carlos Soares, Auteur ; Joaquin Vanschoren, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2022 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-67024-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] chaîne de traitement
[Termes IGN] échantillonnage
[Termes IGN] modèle stochastique
[Termes IGN] ontologie
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression
[Termes IGN] science des données
[Termes IGN] série temporelleRésumé : (éditeur) This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence. Note de contenu : 1- Basic concepts and architecture
2- Advanced techniques and methods
3- Organizing and Exploiting MetadataNuméro de notice : 28698 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-67024-5 En ligne : https://doi.org/10.1007/978-3-030-67024-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100469 Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation / Hang Zhang in Pattern recognition, vol 121 (January 2022)
[article]
Titre : Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation Type de document : Article/Communication Auteurs : Hang Zhang, Auteur ; Haili Li, Auteur ; Ning Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108201 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification floue
[Termes IGN] classification pixellaire
[Termes IGN] filtre
[Termes IGN] segmentation d'image
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) Spatial information is often used to enhance the robustness of traditional fuzzy c-means (FCM) clustering algorithms. Although some recently emerged improvements are remarkable, the computational complexity of these algorithms is high, which may lead to lack of practicability. To address this problem, an efficient variant named the fuzzy clustering algorithm with variable multi-pixel fitting spatial information (FCM-VMF) is presented. First, a fuzzy clustering algorithm with multi-pixel fitting spatial information (FCM-MF) is developed. Specifically, by dividing the input image into several filter windows, the spatial information of all pixels in each filter window can be obtained simultaneously by fitting the pixels in its corresponding neighbourhood window, which enormously reduces the computational complexity. However, the FCM-MF may result in the loss of edge information. Therefore, the FCM-VMF integrates a variable window strategy with FCM-MF. In this strategy, to preserve more edge information, the sizes of the filter window and generalized neighbourhood window are adaptively reduced. The experimental results show that FCM-VMF is as effective as some recent algorithms. Notably, the FCM-VMF has extremely high efficiency, which means it has a better prospect of application. Numéro de notice : A2022-100 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.patcog.2021.108201 Date de publication en ligne : 26/07/2021 En ligne : https://doi.org/10.1016/j.patcog.2021.108201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99564
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