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Nonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou (2020)
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Titre : Nonparametric Bayesian learning for collaborative robot multimodal introspection Type de document : Monographie Auteurs : Xuefeng Zhou, Auteur ; Hongmin Wu, Auteur ; Juan Rojas, Auteur ; et al., Auteur Editeur : Springer Nature Année de publication : 2020 Importance : 137 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-981-1562631-- Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] classification bayesienne
[Termes IGN] inférence
[Termes IGN] interface homme-machine
[Termes IGN] modèle de Markov caché
[Termes IGN] modèle mathématique
[Termes IGN] problème de Dirichlet
[Termes IGN] robotiqueRésumé : (éditeur) This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students. Note de contenu : 1- Introduction to robot introspection
2- Nonparametric Bayesian modeling of multimodal time series
3- Incremental learning robot task representation and identification
4- Nonparametric Bayesian method for robot anomaly monitoring
5- Nonparametric Bayesian method for robot anomaly diagnose
6- Learning policy for robot anomaly recovery based on robot introspectionNuméro de notice : 25965 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007%2F978-981-15-6263-1 En ligne : https://link.springer.com/book/10.1007%2F978-981-15-6263-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96557 On the joint exploitation of optical and SAR satellite imagery for grassland monitoring / Anatol Garioud (2020)
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Titre : On the joint exploitation of optical and SAR satellite imagery for grassland monitoring Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Sébastien Giordano
, Auteur ; Clément Mallet
, Auteur
Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B3-2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 3, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 3 Importance : pp 591 - 598 Format : 21 x 30 cm Note générale : bibliographie
This research has been funded by the Agence pour le Développement Et la Maîtrise de l’Energie (ADEME) and the Centre National d’Etudes Spatiales (CNES).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] fusion de données
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] prairie
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (auteur) Time series of optical and Synthetic Aperture RADAR (SAR) images provide complementary knowledge about the cover and use of the Earth surface since they exhibit information of distinct physical nature. They have proved to be particularly relevant for monitoring large areas with high temporal dynamics and related to significant ecosystem services. Grasslands are such crucial surfaces, both in terms of economic and environmental issues and the automatic and frequent monitoring of their agricultural practices is required for many purposes. To address this problem, the deep-based SenDVI framework is presented. SenDVI proposes an object-based methodology to estimate NDVI values from Sentinel-1 SAR observations and contextual knowledge (weather, terrain). Values are regressed every 6 days for compliance with monitoring purposes. Very satisfactory results are obtained with this low-level multimodal fusion strategy (R 2 =0.84 on a Sentinel-2 tile). Finer analysis is however required to fully assess the relevance of each modality (Sentinel-1, Sentinel-2, weather, terrain) and feature sets and to propose the simplest conceivable framework. Results show that not all features are necessary and can be discarded while others have a mandatory contribution to the regression task. Moreover, experiments prove that accuracy can be improved by not saturating the network with non-essential information (among contextual knowledge in particular). This allows to move towards more operational solution. Numéro de notice : C2020-004 Affiliation des auteurs : UGE-LASTIG (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B3-2020-591-2020 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-591-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95664 Probabilistic pose estimation and 3D reconstruction of vehicles from stereo images / Maximilian Alexander Coenen (2020)
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Titre : Probabilistic pose estimation and 3D reconstruction of vehicles from stereo images Type de document : Thèse/HDR Auteurs : Maximilian Alexander Coenen, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 857 Importance : 160 p. ISBN/ISSN/EAN : 978-3-7696-5269-7 Note générale : bibliographie
Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Universität HannoverLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] estimation de pose
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle stochastique
[Termes IGN] reconstruction 3D
[Termes IGN] véhicule automobileRésumé : (auteur) The pose estimation and reconstruction of 3D objects from images is one of the major problems that are addressed in computer vision and photogrammetry. The understanding of a 3D scene and the 3D reconstruction of specific objects are prerequisites for many highly relevant applications of computer vision such as mobile robotics and autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a common strategy is to incorporate prior object knowledge into the reconstruction approach by establishing a 3D model and aligning it to the 2D image plane. However, current approaches are limited due to inadequate shape priors and the insufficiency of the derived image observations for a reliable association and alignment with the 3D model. The goal of this thesis is to infer valuable observations from the images and to show how 3D object reconstruction can profit from a more sophisticated shape prior and from a combined incorporation of the different observation types. To achieve this goal, this thesis presents three major contributions for the particular task of 3Dvehicle reconstruction from street-level stereo images. First, a subcategory-aware deformable vehicle model is introduced that makes use of a prediction of the vehicle type for a more appropriate regularisation of the vehicle shape. Second, a Convolutional Neural Network (CNN) is proposed which extracts observations from an image. In particular, the CNN is used to derive a prediction of the vehicle orientation and type, which are introduced as prior information for model fitting. Furthermore, the CNN extracts vehicle key points and wireframes, which are well-suited for model association and model fitting. Third, the task of pose estimation and reconstruction is addressed by a versatile probabilistic model. Suitable parametrisations and formulations of likelihood and prior terms are introduced for a joint consideration of the derived observations and prior information in the probabilistic objective function. As the objective function is non-convex and discontinuous, a proper customized strategy based on stochastic sampling is proposed for inference, yielding convincing results for the estimated poses and shapes of the vehicles. To evaluate the performance and to investigate the strengths and limitations of the proposed method, extensive experiments are conducted using two challenging real-world data sets: the publicly available KITTI benchmark and the ICSENS data set, which was created in the scope of this thesis. On both data sets, the benefit of the developed shape prior and of each of the individual components of the probabilistic model can be shown. The proposed method yields vehicle pose estimates with a median error of up to 27 cm for the position and up to 1.7◦for the orientation on the data sets. A comparison to state-of-the-art methods for vehicle pose estimation shows that the proposed approach performs on par or better, confirming the suitability of the developed model and inference procedure. Numéro de notice : 17685 Affiliation des auteurs : non IGN Autre URL associée : vers ResearchGate Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geodäsie und Geoinformatik : Hanovre : 2020 DOI : 10.13140/RG.2.2.19618.86728 En ligne : https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-857.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98165
Titre : Processing and analysis of hyperspectral data Type de document : Monographie Auteurs : Jie Chen, Éditeur scientifique ; Yingying Song, Éditeur scientifique ; Hengchao Li, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 140 p. ISBN/ISSN/EAN : 978-1-78985-109-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] image à haute résolution
[Termes IGN] image hyperspectrale
[Termes IGN] image proche infrarouge
[Termes IGN] qualité des eaux
[Termes IGN] signature spectrale
[Termes IGN] turbidité des eauxRésumé : (Editeur) Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods. Note de contenu : Section One - Theoretical advances of hyperspectral image processing
Chapter 1 - Hyperspectral endmember extraction techniques
Chapter 2 - Hyperspectral image classification
Chapter 3 - Hyperspectral image super-resolution using optimization and DCNN-based methods
Chapter 4 - Fast chaotic encryption for hyperspectral images
Section Two - Applications of hyperspectral image processing
Chapter 5 - NIR hyperspectral imaging for mapping of moisture content distribution in tea buds during dehydration
Chapter 6 - Use of hyperspectral remote sensing to estimate water qualityNuméro de notice : 26560 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.78179 En ligne : http://doi.org/10.5772/intechopen.78179 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98243
Titre : Recent trends in artificial neural networks Type de document : Monographie Auteurs : Ali Sadollah, Éditeur scientifique ; Carlos M. Travieso-Gonzalez, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 150 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-78985-859-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] logique floue
[Termes IGN] réseau neuronal artificielRésumé : (éditeur) Artificial intelligence (AI) is everywhere and it's here to stay. Most aspects of our lives are now touched by artificial intelligence in one way or another, from deciding what books or flights to buy online to whether our job applications are successful, whether we receive a bank loan, and even what treatment we receive for cancer. Artificial Neural Networks (ANNs) as a part of AI maintains the capacity to solve problems such as regression and classification with high levels of accuracy. This book aims to discuss the usage of ANNs for optimal solving of time series applications and clustering. Bounding of optimization methods particularly metaheuristics considered as global optimizers with ANNs make a strong and reliable prediction tool for handling real-life application. This book also demonstrates how different fields of studies utilize ANNs proving its wide reach and relevance. Note de contenu : 1- Time series from clustering: An approach to forecast crime patterns
2- Encountered problems of time series with neural networks: Models and architectures
3- Metaheuristics and artificial neural networks
4- An improved algorithm for optimising the production of biochemical systems
5- Object recognition using convolutional neural networks
6- Prediction of wave energy potential in India: A fuzzy-ANN approach
7- Deep learning training and benchmarks for Earth observation images: Data sets, features, and procedures
8- Data mining technology for structural control systems: Concept, development, and comparisonNuméro de notice : 28497 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.77409 En ligne : https://doi.org/10.5772/intechopen.77409 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99247 Recherche multimodale d'images aériennes multi-date à l'aide d'un réseau siamois / Margarita Khokhlova (2020)
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PermalinkReconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne / Valentin Desbiolles (2020)
PermalinkRegional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification / Viktor Myroniuk in Remote sensing, vol 12 n° 1 (January 2020)
PermalinkSatellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)
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PermalinkSimplicial complexes reconstruction and generalisation of 3d lidar data in urban scenes / Stéphane Guinard (2020)
PermalinkPermalinkSpatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. Hamer in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)
PermalinkSuperpixel-enhanced deep neural forest for remote sensing image semantic segmentation / Li Mi in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
PermalinkUncertainty analysis of remotely-acquired thermal infrared data to extract the thermal Properties of active lava surfaces / James A. Thompson in Remote sensing, vol 12 n° 1 (January 2020)
PermalinkUso de QGIS en la teledetección, Vol. 2. QGIS y sus aplicaciones en la agricultura y la silvicultura / Nicolas Baghdadi (2020)
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