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Titre : SpiNNaker: A spiking neural network architecture Type de document : Monographie Auteurs : Steve Furber, Éditeur scientifique ; Petrut Bogdan, Éditeur scientifique Editeur : Boston, Delft : Now publishers Année de publication : 2020 Importance : 352 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-68083-652-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] cerveau
[Termes IGN] outil logiciel
[Termes IGN] programmation stochastique
[Termes IGN] puce
[Termes IGN] réseau neuronal convolutif
[Termes IGN] système de traitement de l'information
[Termes IGN] vision par ordinateurRésumé : (éditeur) 20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come. Note de contenu : 1- Origins
2- The SpiNNaker Chip
3- Building SpiNNaker Machines
4- Stacks of Software Stacks
5- Applications - Doing Stuff on the Machine
6- From Activations to Spikes
7- Learning in Neural Networks
8- Creating the FutureNuméro de notice : 25978 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie DOI : 10.1561/9781680836523 En ligne : http://dx.doi.org/10.1561/9781680836523 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96705 Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation / Li Mi in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
[article]
Titre : Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation Type de document : Article/Communication Auteurs : Li Mi, Auteur ; Zhenzhong Chen, Auteur Année de publication : 2020 Article en page(s) : pp 140 - 152 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme SLIC
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image à très haute résolution
[Termes IGN] processus stochastique
[Termes IGN] réseau neuronal profond
[Termes IGN] segmentation sémantique
[Termes IGN] superpixelRésumé : (Auteur) Semantic segmentation plays an important role in remote sensing image understanding. Great progress has been made in this area with the development of Deep Convolutional Neural Networks (DCNNs). However, due to the complexity of ground objects’ spectrum, DCNNs with simple classifier have difficulties in distinguishing ground object categories even though they can represent image features effectively. Additionally, DCNN-based semantic segmentation methods learn to accumulate contextual information over large receptive fields that causes blur on object boundaries. In this work, a novel approach named Superpixel-enhanced Deep Neural Forest (SDNF) is proposed to target the aforementioned problems. To improve the classification ability, we introduce Deep Neural Forest (DNF), where the representation learning of deep neural network is conducted by a completely differentiable decision forest. Therefore, better classification accuracy is achieved by combining DCNNs with decision forests in an end-to-end manner. In addition, considering the homogeneity within superpixels and heterogeneity between superpixels, a Superpixel-enhanced Region Module (SRM) is proposed to further alleviate the noises and strengthen edges of ground objects. Experimental results on the ISPRS 2D semantic labeling benchmark demonstrate that our model significantly outperforms state-of-the-art methods thus validate the efficiency of our proposed SDNF. Numéro de notice : A2020-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.006 Date de publication en ligne : 29/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.006 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94403
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Titre : Système de traitement d’images temps réel dédié à la mesure de champs denses de déplacements et de déformations Type de document : Thèse/HDR Auteurs : Seyfeddine Boukhtache, Auteur ; Michel Grédiac, Directeur de thèse ; François Berry, Directeur de thèse Editeur : Clermont-Ferrand : Université Clermont Auvergne Année de publication : 2020 Importance : 169 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse pour obtenir le grade de docteur de l'Université Clermont Auvergne, Spécialité Électronique et Architecture de SystèmesLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] déformation de surface
[Termes IGN] effet thermique
[Termes IGN] interpolation linéaire
[Termes IGN] métrologie
[Termes IGN] pixel
[Termes IGN] réseau neuronal convolutif
[Termes IGN] temps réelIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Cette thèse s’inscrit dans un cadre pluridisciplinaire. Elle traite de la problématique du temps réel et de celle des performances métrologiques en traitement d’images numériques. Elle s'intéresse plus particulièrement à la photomécanique. Il s'agit d'une discipline récente visant à développer et à utiliser au mieux des systèmes de mesure de champs entiers de petits déplacements et de petites déformations en surface de solides soumis à des sollicitations thermomécaniques. La technique utilisée dans cette thèse est la corrélation des images numériques (CIN), qui se trouve être l'une des plus employées dans cette communauté. Elle représente cependant des limitations à savoir un temps de calcul prohibitif et des performances métrologiques améliorables afin d'atteindre celles des capteurs ponctuels classiques comme les jauges de déformation.Ce travail s'appuie sur deux axes d'étude pour relever ce défi. Le premier repose sur l'optimisation de l'interpolation d'images qui est le traitement le plus coûteux dans la CIN. Une accélération est proposée en utilisant une implémentation matérielle parallélisée sur FPGA, tout en tenant compte de la consommation des ressources matérielles et de la précision. La principale conclusion est qu'un seul FPGA (dans les limites technologiques actuelles) ne suffit pas à implémenter l'intégralité de l'algorithme CIN. Un second axe d'étude a donc été proposé. Il vise à développer et à utiliser des réseaux de neurones convolutifs pour tenter d'atteindre à la fois des performances métrologiques meilleures que la CIN et un traitement en temps réel. Cette deuxième étude a montré l'efficacité d'un tel outil pour la mesure des champs de déplacements et de déformations. Elle ouvre de nouvelles perspectives en termes de performances métrologiques et de rapidité des systèmes de mesure de champs. Note de contenu : Introduction générale
1. Traitement sous-pixellique et performances métrologiques
1.1 Les approches à performance sous-pixelique
1.2 Techniques d’interpolation
1.3 Métrologie par vision
1.4 Le cas particulier de mesure métrologique en photomécanique
1.5 Conclusion
2. Implémentation matérielle : précision et ressources matérielles
2.1 Plateformes matérielles
2.2 Réduire la complexité de calcul : techniques d’approximation
2.3 Précision arithmétique
2.4 Processus d’optimisation de la représentation en virgule fixe
2.5 Conclusion
3. FPGA-based architecture for bi-cubic interpolation : the best trade-off between precision and hardware resource consumption
3.1 Introduction
3.2 Bi-cubic interpolation
3.3 Previous studies
3.4 Proposed architecture
3.5 Resource utilization and precision analysis
3.6 Results
3.7 Conclusion
4. Alternatives to bi-cubic interpolation considering FPGA hardware resource consumption
4.1 Introduction
4.2 Bi-cubic interpolation
4.3 Previous works
4.4 Approximation of the cubic kernel with n-piecewise linear functions
4.5 Combining cubic and linear interpolations
4.6 Results
4.7 Conclusion
5. When Deep Learning Meets Digital Image Correlation
5.1 Introduction
5.2 A short primer on deep learning
5.3 A brief review of CNN-based methods for optical flow estimation
5.4 Dataset
5.5 Fine-tuning networks of the literature
5.6 Tailoring FlowNetS to estimate displacement fields
5.7 Spatial resolution and metrological performance indicator
5.8 Assessing the generalization capability
5.9 Computing time
5.10 Conclusion
6. Conclusion générale et perspectives
6.1 Conclusion
6.2 PerspectivesNuméro de notice : 26530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Électronique et Architecture de Systèmes : Clermont Auvergne : 2020 Organisme de stage : Sigma Clermont nature-HAL : Thèse Date de publication en ligne : 27/03/2021 En ligne : https://tel.archives-ouvertes.fr/tel-03180484/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97561 Torch-Points3D: A modular multi-task framework for reproducible deep learning on 3D point clouds / Thomas Chaton (2020)
Titre : Torch-Points3D: A modular multi-task framework for reproducible deep learning on 3D point clouds Type de document : Article/Communication Auteurs : Thomas Chaton, Auteur ; Nicolas Chaulet, Auteur ; Sofiane Horache, Auteur ; Loïc Landrieu , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Projets : AI4GEO / Conférence : 3DV 2020, International Conference on 3D Vision 25/11/2020 27/11/2020 Fukuoka + en ligne Japon Proceedings IEEE Importance : 12 p. - n° 282 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] apprentissage profond
[Termes IGN] cadre conceptuel
[Termes IGN] code source libre
[Termes IGN] données localisées 3D
[Termes IGN] reproductibilité
[Termes IGN] réseau neuronal profond
[Termes IGN] semis de pointsRésumé : (auteur) We introduce Torch-Points3D, an open-source framework designed to facilitate the use of deep networks on 3D data. Its modular design, efficient implementation, and user-friendly interfaces make it a relevant tool for research and productization alike. Beyond multiple quality-of-life features, our goal is to standardize a higher level of transparency and reproducibility in 3D deep learning research, and to lower its barrier to entry. In this paper, we present the design principles of Torch-Points3D, as well as extensive benchmarks of multiple state-of-the-art algorithms and inference schemes across several datasets and tasks. The modularity of Torch-Points3D allows us to design fair and rigorous experimental protocols in which all methods are evaluated in the same conditions. The Torch-Points3D repository : https://github.com/nicolas-chaulet/torch-points3d. Numéro de notice : C2020-019 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/3DV50981.2020.00029 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.1109/3DV50981.2020.00029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96456 Underwater field equipment of a network of landmarks optimized for automatic detection by AI / Laurent Beaudoin (2020)
Titre : Underwater field equipment of a network of landmarks optimized for automatic detection by AI Type de document : Article/Communication Auteurs : Laurent Beaudoin, Auteur ; Loïca Avanthey, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Conférence : IGARSS 2020, 2020 IEEE International Geoscience and Remote Sensing Symposium 26/09/2020 02/10/2020 Waikoloa, Hawaï Etats-Unis proceedings IEEE Importance : n° 9323589 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] détection automatique
[Termes IGN] détection de cible
[Termes IGN] point d'appui
[Termes IGN] reconstruction 3DRésumé : (auteur) To qualify the point clouds obtained by 3D reconstruction of a global study area in close-range remote sensing, control points, whose position has been measured essentially manually in the field with an instrument whose precision is known, are used. In the underwater environment, equipping the field and carrying out these measurements is a complex operation to perform due to the peculiarities of the environment. We present in this article a first step towards the automation of this task, the automatic detection of targets by a deep learning algorithm which will serve to correctly position the control points locally, and a simplification of the manual measurement which will serve in future work to control the results of automatic readings. 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Mousa in Photogrammetric record, vol 34 n° 165 (March 2019)PermalinkCentral place indexing : hierarchical linear indexing systems for mixed-aperture hexagonal discrete global grid systems / Kevin Sahr in Cartographica, vol 54 n° 1 (Spring 2019)PermalinkDeep mapping gentrification in a large Canadian city using deep learning and Google Street View / Lazar Ilic in Plos one, vol 14 n° 3 (March 2019)PermalinkDuPLO: A DUal view Point deep Learning architecture for time series classificatiOn / Roberto Interdonato in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkForest degradation and biomass loss along the Chocó region of Colombia / Victoria Meyer in Carbon Balance and Management, vol 14 (March 2019)PermalinkGeospatial data organization methods with emphasis on aperture-3 hexagonal discrete global grid systems / Ali Mahdavi Amiri in Cartographica, vol 54 n° 1 (Spring 2019)PermalinkInferring user tasks in pedestrian navigation from eye movement data in real-world environments / Hua Liao in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkLearning to segment moving objects / Pavel Tokmakov in International journal of computer vision, vol 127 n° 3 (March 2019)PermalinkSemantic understanding of scenes through the ADE20K dataset / Bolei Zhou in International journal of computer vision, vol 127 n° 3 (March 2019)PermalinkA derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds / Fan Xue in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkGeoTxt: A scalable geoparsing system for unstructured text geolocation / Morteza Karimzadeh in Transactions in GIS, vol 23 n° 1 (February 2019)PermalinkImproving LiDAR classification accuracy by contextual label smoothing in post-processing / Nan Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkLearning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery / Lichao Mou in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)PermalinkAdvanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure / Maged Marghany (2019)PermalinkPermalinkAn approach for identifying and analysing reference features and spatial relations used in mountain emergency calls / Mattia Bunel (2019)PermalinkAnalyse d’images par méthode de Deep Learning appliquée au contexte routier en conditions météorologiques dégradées / Khouloud Dahmane (2019)PermalinkPermalink