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Titre : Introduction to Deep Learning : From Logical Calculus to Artificial Intelligence Type de document : Monographie Auteurs : Sandro Skansi, Auteur Editeur : Springer Nature Année de publication : 2018 Importance : 196 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-319-73004-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] codage
[Termes IGN] estimation par noyau
[Termes IGN] matrice de covariance
[Termes IGN] Perceptron multicouche
[Termes IGN] Python (langage de programmation)
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] réseau neuronal convolutif
[Termes IGN] sciences cognitives
[Termes IGN] théorie des probabilitésRésumé : (auteur) This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.
Topics and features:
Introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning
Discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network
Examines convolutional neural networks, and the recurrent connections to a feed-forward neural network
Describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning
Presents a brief history of artificial intelligence and neural networks, and reviews interesting
open research problems in deep learning and connectionism
This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.Note de contenu : 1- From Logic to Cognitive Science
2- Mathematical and Computational Prerequisites
3- Machine Learning Basics
4- Feedforward Neural Networks
5- Modifications and Extensions to a Feed-Forward Neural Network
6- Convolutional Neural Networks
7- Recurrent Neural Networks
8- Autoencoders
9- Neural Language Models
10- An Overview of Different Neural Network Architectures
11- ConclusionNuméro de notice : 25787 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-73004-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94990
Titre : Probability and statistics : A course for physicists and engineers Type de document : Guide/Manuel Auteurs : Arak M. Mathai, Auteur ; Hans J. Haubold, Auteur Editeur : Berlin, New York : Walter de Gruyter Année de publication : 2018 Importance : 582 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-3-11-056253-8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] densité de probabilité
[Termes IGN] distribution, loi de
[Termes IGN] échantillonnage (statistique)
[Termes IGN] estimation statistique
[Termes IGN] régression
[Termes IGN] théorie des probabilités
[Termes IGN] variable aléatoireRésumé : (éditeur) This textbook offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, as well as basics of sampling distributions, estimation and hypothesis testing. As the basis for courses on space and atmospheric science, remote sensing, geographic information systems, meteorology, climate and satellite communications at UN-affiliated regional centers, various applications of the formal theory are discussed as well. These include applied topics such as model building and experiment design. Designed for students in engineering and physics with applications in mind. Note de contenu : Introduction
1- Random phenomena
2- Probability
3- Random variables
4- Expected values
5- Commonly used density functions
6- Commonly used density functions
7- Commonly used density functions
8- Some multivariate distributions
9- Collection of random variables
10- Sampling distributions
11- Estimation
12- Interval estimation
13- Tests of statistical hypotheses
14- Model building and regression
15- Design of experiments and analysis of variance
16- Questions and answersNuméro de notice : 25970 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel de cours DOI : 10.1515/9783110562545 En ligne : https://doi.org/10.1515/9783110562545 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96610 Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game / Dawei Zai in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)
[article]
Titre : Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game Type de document : Article/Communication Auteurs : Dawei Zai, Auteur ; Jonathan Li, Auteur ; Yulan Guo, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 15 - 29 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] enregistrement de données
[Termes IGN] matrice de covariance
[Termes IGN] semis de points
[Termes IGN] télémétrie laser terrestre
[Termes IGN] théorie des jeuxRésumé : (Auteur) It is challenging to automatically register TLS point clouds with noise, outliers and varying overlap. In this paper, we propose a new method for pairwise registration of TLS point clouds. We first generate covariance matrix descriptors with an adaptive neighborhood size from point clouds to find candidate correspondences, we then construct a non-cooperative game to isolate mutual compatible correspondences, which are considered as true positives. The method was tested on three models acquired by two different TLS systems. Experimental results demonstrate that our proposed adaptive covariance (ACOV) descriptor is invariant to rigid transformation and robust to noise and varying resolutions. The average registration errors achieved on three models are 0.46 cm, 0.32 cm and 1.73 cm, respectively. The computational times cost on these models are about 288 s, 184 s and 903 s, respectively. Besides, our registration framework using ACOV descriptors and a game theoretic method is superior to the state-of-the-art methods in terms of both registration error and computational time. The experiment on a large outdoor scene further demonstrates the feasibility and effectiveness of our proposed pairwise registration framework. Numéro de notice : A2017-729 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88426
in ISPRS Journal of photogrammetry and remote sensing > vol 134 (December 2017) . - pp 15 - 29[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017121 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017122 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017123 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Salient object detection in complex scenes via D-S evidence theory based region classification / Chunlei Yang in The Visual Computer, vol 33 n° 11 (November 2017)
[article]
Titre : Salient object detection in complex scenes via D-S evidence theory based region classification Type de document : Article/Communication Auteurs : Chunlei Yang, Auteur ; Jiexin Pu, Auteur ; Yongsheng Dong, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1415 - 1428 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] fusion de données
[Termes IGN] information complexe
[Termes IGN] scène intérieure
[Termes IGN] segmentation d'image
[Termes IGN] théorie de Dempster-Shafer
[Termes IGN] zone saillante 3DRésumé : (Auteur) In complex scenes, multiple objects are often concealed in cluttered backgrounds. Their saliency is difficult to be detected by using conventional methods, mainly because single color contrast can not shoulder the mission of saliency measure; other image features should be involved in saliency detection to obtain more accurate results. Using Dempster-Shafer (D-S) evidence theory based region classification, a novel method is presented in this paper. In the proposed framework, depth feature information extracted from a coarse map is employed to generate initial feature evidences which indicate the probabilities of regions belonging to foreground or background. Based on the D-S evidence theory, both uncertainty and imprecision are modeled, and the conflicts between different feature evidences are properly resolved. Moreover, the method can automatically determine the mass functions of the two-stage evidence fusion for region classification. According to the classification result and region relevance, a more precise saliency map can then be generated by manifold ranking. To further improve the detection results, a guided filter is utilized to optimize the saliency map. Both qualitative and quantitative evaluations on three publicly challenging benchmark datasets demonstrate that the proposed method outperforms the contrast state-of-the-art methods, especially for detection in complex scenes. Numéro de notice : A2017-713 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-016-1288-y En ligne : https://doi.org/10.1007/s00371-016-1288-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88094
in The Visual Computer > vol 33 n° 11 (November 2017) . - pp 1415 - 1428[article]Occupancy modelling for moving object detection from Lidar point clouds: A comparative study / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W4 (September 2017)
[article]
Titre : Occupancy modelling for moving object detection from Lidar point clouds: A comparative study Type de document : Article/Communication Auteurs : Wen Xiao, Auteur ; Bruno Vallet , Auteur ; Y. Xiao, Auteur ; Jon Mills, Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2017 Article en page(s) : pp 171 - 178 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection d'objet
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] grille
[Termes IGN] objet mobile
[Termes IGN] semis de points
[Termes IGN] théorie de Dempster-ShaferRésumé : (auteur) Lidar technology has been widely used in both robotics and geomatics for environment perception and mapping. Moving object detection is important in both fields as it is a fundamental step for collision avoidance, static background extraction, moving pattern analysis, etc. A simple method involves checking directly the distance between nearest points from the compared datasets. However, large distances may be obtained when two datasets have different coverages. The use of occupancy grids is a popular approach to overcome this problem. There are two common theories employed to model occupancy and to interpret the measurements, DempsterShafer theory and probability. This paper presents a comparative study of these two theories for occupancy modelling with the aim of moving object detection from lidar point clouds. Occupancy is modelled using both approaches and their implementations are explained and compared in details. Two lidar datasets are tested to illustrate the moving object detection results Numéro de notice : A2017-913 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-2-W4-171-2017 En ligne : https://doi.org/10.5194/isprs-annals-IV-2-W4-171-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102874
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-2/W4 (September 2017) . - pp 171 - 178[article]Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models / Kai Cui in Geocarto international, vol 32 n° 9 (September 2017)PermalinkLearning to diversify deep belief networks for hyperspectral image classification / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkDelineation of groundwater potential zones using remote sensing and GIS-based data-driven models / Samira Ghorbani Nejad in Geocarto international, vol 32 n° 2 (February 2017)PermalinkComparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)PermalinkFusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)PermalinkAutomatic parameter selection for intensity-based registration of imagery to LiDAR data / Ebadat Ghanbari Parmehr in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkSystematic effects in laser scanning and visualization by confidence regions / Karl Rudolf Koch in Journal of applied geodesy, vol 10 n° 4 (December 2016)PermalinkPermalinkA Bayesian network-based method to alleviate the ill-posed inverse problem: A case study on leaf area index and canopy water content retrieval / Xingwen Quan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)PermalinkStreet environment change detection from mobile laser scanning point clouds / Wen Xiao in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 2015)Permalink