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Rasterisation-based progressive photon mapping / Iordanis Evangelou in The Visual Computer, vol 36 n° 10 - 12 (October 2020)  

Public [article]  inThe Visual Computer > vol 36 n° 10 - 12 (October 2020) . - pp 1993 - 2004 Titre : Rasterisation-based progressive photon mapping Type de document : Article/Communication Auteurs : Iordanis Evangelou, Auteur ; Georgios Papaioannou, Auteur ; Konstantinos Vardis, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1993 - 2004 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] architecture pipeline [Termes descripteurs IGN] cartographie [Termes descripteurs IGN] implémentation (informatique) [Termes descripteurs IGN] lancer de rayons [Termes descripteurs IGN] photon [Termes descripteurs IGN] processeur graphique [Termes descripteurs IGN] rastérisation Résumé : (auteur) Ray tracing on the GPU has been synergistically operating alongside rasterisation in interactive rendering engines for some time now, in order to accurately capture certain illumination effects. In the same spirit, in this paper, we propose an implementation of progressive photon mapping entirely on the rasterisation pipeline, which is agnostic to the specific GPU architecture, in order to synthesise images at interactive rates. While any GPU ray tracing architecture can be used for photon mapping, performing ray traversal in image space minimises acceleration data structure construction time and supports arbitrarily complex and fully dynamic geometry. Furthermore, this strategy maximises data structure reuse by encompassing rasterisation, ray tracing and photon gathering tasks in a single data structure. Both eye and light paths of arbitrary depth are traced on multi-view deep G-buffers, and photon flux is gathered by a properly adapted multi-view photon splatting. In contrast to previous methods exploiting rasterisation to some extent, due to our novel indirect photon splatting approach, any event combination present in photon mapping is captured. We evaluate our method using typical test scenes and scenarios for photon mapping methods and show how our approach outperforms typical GPU-based progressive photon mapping. Numéro de notice : A2020-412 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s00371-020-01897-3 date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1007/s00371-020-01897-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95935 [article]

CSVM architectures for pixel-wise object detection in high-resolution remote sensing images / Youyou Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6059 - 6070 Titre : CSVM architectures for pixel-wise object detection in high-resolution remote sensing images Type de document : Article/Communication Auteurs : Youyou Li, Auteur ; Farid Melgani, Auteur ; Binbin He, Auteur Année de publication : 2020 Article en page(s) : pp 6059 - 6070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] processeur graphique Résumé : (auteur) Detecting objects becomes an increasingly important task in very high resolution (VHR) remote sensing imagery analysis. With the development of GPU-computing capability, a growing number of deep convolutional neural networks (CNNs) have been designed to address the object detection challenge. However, compared with CPU, GPU is much more costly. Therefore, GPU-based methods are less attractive in practical applications. In this article, we propose a CPU-based method that is based on convolutional support vector machines (CSVMs) to address the object detection challenge in VHR images. Experiments are conducted on three VHR and two unmanned aerial vehicle (UAV) data sets with very limited training data. Results show that the proposed CSVM achieves competitive performance compared to U-Net which is an efficient CNN-based model designed for small training data sets. Numéro de notice : A2020-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2972289 date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2972289 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95705 [article]

Local terrain modification method considering physical feature constraints for vector elements / Jiangfeng She in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 452 - 470 Titre : Local terrain modification method considering physical feature constraints for vector elements Type de document : Article/Communication Auteurs : Jiangfeng She, Auteur ; Junyan Liu, Auteur ; Junzhong Tan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 452 - 470 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] altitude [Termes descripteurs IGN] analyse vectorielle [Termes descripteurs IGN] contrainte d'intégrité [Termes descripteurs IGN] déformation de surface [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] interpolation [Termes descripteurs IGN] processeur graphique [Termes descripteurs IGN] rastérisation [Termes descripteurs IGN] relief [Termes descripteurs IGN] superposition de données [Termes descripteurs IGN] surface du sol [Termes descripteurs IGN] terrain [Termes descripteurs IGN] traitement parallèle [Termes descripteurs IGN] zone tampon Résumé : (auteur) Many studies have been focused on rendering 2D vector elements on 3D terrain, and a series of algorithms have been proposed. Most of these algorithms struggle to provide a seamless overlay between vector elements and an irregular terrain surface. Despite their importance, the physical characteristics of vector elements are often ignored, which distorts the surface of vector elements. For example, if vector elements that represent roads and rivers are simply overlaid on terrain, the phenomena of uneven surfaces and rivers going uphill may occur because of elevation fluctuation. To correct these deficiencies, terrain should be modified according to the physical characteristics of vectors. We propose a local terrain modification method: First, the elevation of terrain covered by vector elements is recalculated according to vectors’ physical characteristics. Second, the multigrid method is used to realize a smooth transition between the modified terrain and its surrounding area. Finally, by setting different transition ranges and comparing the visualization effects, rules are given for the selection of a suitable range. After modification, the terrain conforms to vectors’ physical characteristics, and the overall relief is undamaged. The proposed method was applied to a CPU–GPU parallel heterogeneous model and demonstrated a high level of performance. Numéro de notice : A2020-489 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1770128 date de publication en ligne : 06/07/2020 En ligne : https://doi.org/10.1080/15230406.2020.1770128 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95660 [article]

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Bayesian iterative reconstruction methods for 3D X-ray Computed Tomography / Camille Chapdelaine (2019)  

Public Titre : Bayesian iterative reconstruction methods for 3D X-ray Computed Tomography Type de document : Thèse/HDR Auteurs : Camille Chapdelaine, Auteur ; Charles Soussen, Directeur de thèse Editeur : Paris-Orsay : Université de Paris 11 Paris-Sud Centre d'Orsay Année de publication : 2019 Importance : 185 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse de Doctorat de l’Université Paris - Saclay préparée à l'Université Paris-Sud, Sciences et Technologies de l’Information et de la Communication (STIC), Traitement du signal et des images Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] artefact [Termes descripteurs IGN] capteur-projecteur [Termes descripteurs IGN] faisceau [Termes descripteurs IGN] inférence [Termes descripteurs IGN] itération [Termes descripteurs IGN] processeur graphique [Termes descripteurs IGN] rayon X [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] reconstruction d'image [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] spectroscopie [Termes descripteurs IGN] théorème de Bayes [Termes descripteurs IGN] tomographie [Termes descripteurs IGN] volume (grandeur) Index. décimale : THESE Thèses et HDR Résumé : (auteur) In industry, 3D X-ray Computed Tomography aims at virtually imaging a volume in order to inspect its interior. The virtual volume is obtained thanks to a reconstruction algorithm based on projections of X-rays sent through the industrial part to inspect. In order to compensate uncertainties in the projections such as scattering or beam-hardening, which are cause of many artifacts in conventional filtered backprojection methods, iterative reconstruction methods bring further information by enforcing a prior model on the volume to reconstruct, and actually enhance the reconstruction quality. In this context, this thesis proposes new iterative reconstruction methods for the inspection of aeronautical parts made by SAFRAN group. In order to alleviate the computational cost due to repeated projection and backprojection operations which model the acquisition process, iterative reconstruction methods can take benefit from the use of high-parallel computing on Graphical Processor Unit (GPU). In this thesis, the implementation on GPU of several pairs of projector and backprojector is detailed. In particular, a new GPU implementation of the matched Separable Footprint pair is proposed. Since many of SAFRAN's industrial parts are piecewise-constant volumes, a Gauss-Markov-Potts prior model is introduced, from which a joint reconstruction and segmentation algorithm is derived. This algorithm is based on a Bayesian approach which enables to explain the role of each parameter. The actual polychromacy of X-rays, which is responsible for scattering and beam-hardening, is taken into account by proposing an error-splitting forward model. Combined with Gauss-Markov-Potts prior on the volume, this new forward model is experimentally shown to bring more accuracy and robustness. At last, the estimation of the uncertainties on the reconstruction is investigated by variational Bayesian approach. In order to have a reasonable computation time, it is highlighted that the use of a matched pair of projector and backprojector is necessary. Note de contenu : 1- X-ray computed tomography : an inverse problem 2- Reconstruction methods in X-ray computed tomography 3- Projection and backprojection operators 4- Gauss-Markov-Potts prior model for joint reconstruction and segmentation 5- Error-splitting forward model and its application with Gauss-Markov-Potts prior 6- Towards the estimation of the uncertainties on the reconstruction by Variational Bayesian Approach 7- Conclusion and perspectives Numéro de notice : 25702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences et Technologies de l’Information et de la Communication (STIC) : Traitement du signal et des images : Paris 11 : 2019 Organisme de stage : Safran En ligne : https://tel.archives-ouvertes.fr/tel-02110033 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94827

Machine learning - advanced techniques and emerging applications / Hamed Farhadi (2018)  

Public Titre : Machine learning - advanced techniques and emerging applications Type de document : Monographie Auteurs : Hamed Farhadi, Editeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2018 Importance : 230 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 9781789237528 9781789237535 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] données massives [Termes descripteurs IGN] informatique en nuage [Termes descripteurs IGN] processeur graphique [Termes descripteurs IGN] statistiques Résumé : (éditeur) The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses. Note de contenu : 1- Hardware accelerator design for machine learning 2- Regression models to predict air pollution from affordable data collections 3- Multiple kernel-based multimedia fusion for automated event detection from tweets 4- Using sentiment analysis and machine learning algorithms to determine citizens’ perceptions 5- Overcoming challenges in predictive modeling of Laser-plasma interaction scenarios. The sinuous route from advanced machine learning to deep learning 6- Machine learning approaches for spectrum management in cognitive radio networks 7- Machine learning algorithm for wireless indoor localization 8- classification of malaria-infected cells using deep convolutional neuronal networks 9- Machine learning in educational technology 10- Sentiment-based semantic rule learning for improved product recommandations 11- A multilevel evolutionary algorithm applied to the maximum satisfiability problems Numéro de notice : 25952 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie DOI : 10.5772/intechopen.69783 En ligne : https://doi.org/10.5772/intechopen.69783 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96406

An efficient data organization and scheduling strategy for accelerating large vector data rendering / Mingqiang Guo in Transactions in GIS, vol 21 n° 6 (December 2017)  

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A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data / Guiming Zhang in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)  

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A new GPU bundle adjustment method for large-scale data / Zhou Shunping ; Xiong Xiaodong ; Junfeng Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)  

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A parallel scheme for large-scale polygon rasterization on CUDA-enabled GPUs / Chen Zhou in Transactions in GIS, vol 21 n° 3 (June 2017)  

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The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing / Lihong Su in Geoinformatica [en ligne], vol 20 n° 4 (October - December 2016)  

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Efficient visualization of urban simulation data using modern GPUs / Aleksandr Zagarskikh in Procedia Computer Science, vol 51 (2015)  

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Out-of-core GPU-based change detection in massive 3D point clouds / Rico Richter in Transactions in GIS, vol 17 n° 5 (October 2013)  

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Visualisation 3D de terrain texturé : préservation au niveau du pixel des qualités géométriques et colorimétriques, une méthode temps réel, innovante et simple / T.V. Lê in Revue internationale de géomatique, vol 22 n° 3 (septembre - novembre 2012)

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Fast and accurate visibility computation in urban scenes / Bruno Vallet (2011)    

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