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Titre : Geometric camera pose refinement with learned depth maps Type de document : Article/Communication Auteurs : Nathan Piasco , Auteur ; Désiré Sidibé, Auteur ; Cédric Demonceaux, Auteur ; Valérie Gouet-Brunet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Conférence : ICIP 2019, 26th IEEE International Conference on Image Processing 22/09/2019 25/09/2019 Taipei Taiwan Proceedings IEEE Importance : 5 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme ICP
[Termes IGN] carte de profondeur
[Termes IGN] estimation de pose
[Termes IGN] réseau neuronal convolutif
[Termes IGN] scène intérieure
[Termes IGN] semis de pointsRésumé : (auteur) We present a new method for image-only camera relocalisation composed of a fast image indexing retrieval step followed by pose refinement based on ICP (Iterative Closest Point). The first step aims to find an initial pose for the query by evaluating images similarity with low dimensional global deep descriptors. Subsequently, we predict with a fully convolutional deep encoder-decoder neural network a dense depth map from the image query. We use this depth map to create a local point cloud and refine the initial query pose using an ICP algorithm.We demonstrate the effectiveness of our new approach on various indoor scenes. Compared to learned pose regression methods, our proposal can be used on multiple scenes without the need of a specific weights-setup for each scene, while showing equivalent results. Numéro de notice : C2019-015 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICIP.2019.8803014 Date de publication en ligne : 26/08/2019 En ligne : https://doi.org/10.1109/ICIP.2019.8803014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93279 Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing / Minrui Zheng in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)
[article]
Titre : Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing Type de document : Article/Communication Auteurs : Minrui Zheng, Auteur ; Wenwu Tang, Auteur ; Xiang Zhao, Auteur Année de publication : 2019 Article en page(s) : pp 314 - 345 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] Caroline du Nord (Etats-Unis)
[Termes IGN] données spatiotemporelles
[Termes IGN] géostatistique
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle empirique
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation spatiale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'information foncièreRésumé : (auteur) Artificial neural networks (ANNs) have been extensively used for the spatially explicit modeling of complex geographic phenomena. However, because of the complexity of the computational process, there has been an inadequate investigation on the parameter configuration of neural networks. Most studies in the literature from GIScience rely on a trial-and-error approach to select the parameter setting for ANN-driven spatial models. Hyperparameter optimization provides support for selecting the optimal architectures of ANNs. Thus, in this study, we develop an automated hyperparameter selection approach to identify optimal neural networks for spatial modeling. Further, the use of hyperparameter optimization is challenging because hyperparameter space is often large and the associated computational demand is heavy. Therefore, we utilize high-performance computing to accelerate the model selection process. Furthermore, we involve spatial statistics approaches to improve the efficiency of hyperparameter optimization. The spatial model used in our case study is a land price evaluation model in Mecklenburg County, North Carolina, USA. Our results demonstrate that the automated selection approach improves the model-level performance compared with linear regression, and the high-performance computing and spatial statistics approaches are of great help for accelerating and enhancing the selection of optimal neural networks for spatial modeling. Numéro de notice : A2019-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1530355 Date de publication en ligne : 12/10/2018 En ligne : https://doi.org/10.1080/13658816.2018.1530355 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91689
in International journal of geographical information science IJGIS > Vol 33 n° 1-2 (January - February 2019) . - pp 314 - 345[article]Réservation
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Titre de série : Learning to understand remote sensing images, 1 Titre : Volume 1 Type de document : Monographie Auteurs : Qi Wang, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 426 p. ISBN/ISSN/EAN : 978-3-03897-685-1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse texturale
[Termes IGN] apprentissage profond
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat
[Termes IGN] image radar moirée
[Termes IGN] réseau neuronal convolutifRésumé : (Editeur) With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field. Numéro de notice : 26301A Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03897-685-1 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.3390/books978-3-03897-685-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95033
Titre de série : Learning to understand remote sensing images, 2 Titre : Volume 2 Type de document : Monographie Auteurs : Qi Wang, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 376 p. ISBN/ISSN/EAN : 978-3-03897-699-8 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse texturale
[Termes IGN] apprentissage profond
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat
[Termes IGN] image radar moirée
[Termes IGN] réseau neuronal convolutifRésumé : (Editeur) With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field. Numéro de notice : 26301B Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03897-699-8 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.3390/books978-3-03897-699-8 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95034 Machine learning techniques applied to geoscience information system and remote sensing / Saro Lee (2019)
Titre : Machine learning techniques applied to geoscience information system and remote sensing Type de document : Monographie Auteurs : Saro Lee, Éditeur scientifique ; Hyung-Sup Jung, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 438 p. ISBN/ISSN/EAN : ISBN 978-3-03921-215-6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] géosciences
[Termes IGN] réseau neuronal convolutif
[Termes IGN] système d'information géographique
[Termes IGN] télédétection
[Termes IGN] traitement de données localiséesRésumé : (éditeur) As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing. Numéro de notice : 25831 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif En ligne : https://www.mdpi.com/books/pdfview/book/1533 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95158 Méthodes d'apprentissage statistique pour la détection de la signalisation routière à partir de véhicules traceurs / Yann Méneroux (2019)PermalinkA multi-faceted CNN architecture for automatic classification of mobile LiDAR data and an algorithm to reproduce point cloud samples for enhanced training / Bhavesh Kumar in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkNumérique et territoires / Philippe Cohard (2019)PermalinkPermalinkProjection sur l’évolution de la distribution future de la population en utilisant du Machine Learning et de la géosimulation / Julie Grosmaire (2019)PermalinkPermalinkSegmentation d'image par intégration itérative de connaissances / Mahaman Sani Chaibou Salaou (2019)PermalinkPermalinkDesigning an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes / Sepideh Saeidi in Geocarto international, vol 33 n° 12 (December 2018)PermalinkSuper-resolution of Sentinel-2 images : Learning a globally applicable deep neural network / Charis Lanaras in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)Permalink