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Titre : Inferring the scale and content of a map using deep learning Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Florentin Brisebard, Auteur ; Félix Quinton , Auteur ; Azelle Courtial , 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-B4 Projets : ACTIVmap / Favreau, Jean-Marie Conférence : ISPRS 2020, Commission 4, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 4 Importance : pp 17 - 24 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] apprentissage profond
[Termes IGN] carte numérisée
[Termes IGN] carte scolaire
[Termes IGN] carte tactile
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] échelle cartographique
[Termes IGN] formation
[Termes IGN] généralisation
[Termes IGN] géographie physique
[Termes IGN] personne non-voyanteRésumé : (auteur) Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. "there are roads, cities and administrative boundaries"), and to infer the extent of the map (e.g. a map of France or of Europe). Numéro de notice : C2020-002 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B4-2020-17-2020 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-17-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95391
Titre : Intelligent Imaging and Analysis Type de document : Monographie Auteurs : DaeEun Kim, Éditeur scientifique ; Dosik Hwang, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 492 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03921-921-6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] estimation de pose
[Termes IGN] image 3D
[Termes IGN] image captée par drone
[Termes IGN] imagerie médicale
[Termes IGN] reconstruction d'image
[Termes IGN] segmentation d'image
[Termes IGN] texture d'image
[Termes IGN] vision par ordinateurRésumé : (éditeur) Imaging and analysis are widely involved in various research fields, including biomedical applications, medical imaging and diagnosis, computer vision, autonomous driving, and robot controls. Imaging and analysis are now facing big changes regarding intelligence, due to the breakthroughs of artificial intelligence techniques, including deep learning. Many difficulties in image generation, reconstruction, de-noising skills, artifact removal, segmentation, detection, and control tasks are being overcome with the help of advanced artificial intelligence approaches. This Special Issue focuses on the latest developments of learning-based intelligent imaging techniques and subsequent analyses, which include photographic imaging, medical imaging, detection, segmentation, medical diagnosis, computer vision, and vision-based robot control. These latest technological developments will be shared through this Special Issue for the various researchers who are involved with imaging itself, or are using image data and analysis for their own specific purposes. Note de contenu : 1- Special features on intelligent imaging and analysis
2- Intelligent evaluation of strabismus in videos based on an automated cover test
3- Application of a real-time visualization method of AUVs in underwater visual localization
4- Volumetric tooth wear measurement of scraper conveyor sprocket using shape from
focus-based method
5- A novel self-intersection penalty term for statistical body shape models and its applications in 3D pose estimation
6- A CNN model for human parsing based on capacity optimization
7- Fast 3D semantic mapping in road scenes †
8- Automated classification analysis of geological structures based on images data and deep learning model
9- Dark spot detection in SAR images of oil spill using segnet
10- A high-resolution texture mapping technique for 3D textured model
11- Image super-resolution algorithm based on dual-channel convolutional neural networks
12- No-reference automatic quality assessment for colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images using high-dynamic-range-derived features
13- A novel one-camera-five-mirror three-dimensional imaging method for reconstructing the cavitation bubble cluster in a water hydraulic valve
14- Deep residual network with sparse feedback for image restoration
15- An image segmentation method using an active contour model based on improved SPF
and LIF
16- Image segmentation approaches for weld pool monitoring during robotic arc welding
17- A novel discriminating and relative global spatial image representation with applications in CBIR
18- Double low-rank and sparse decomposition for surface defect segmentation of steel sheet
19- A UAV-based visual inspection method for rail surface defects
20- Feature-learning-based printed circuit board inspection via speeded-up robust features and random forest
21- Research progress of visual inspection technology of steel products
22- Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net
23- Semi-automatic segmentation of vertebral bodies in MR images of human lumbar spinesNuméro de notice : 28500 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03921-921-6 En ligne : https://doi.org/10.3390/books978-3-03921-921-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96897
Titre : Intelligent processing on image and optical information Type de document : Monographie Auteurs : Seakwon Yeom, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 324 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-945-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de lignes
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] détection de changement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] navigation autonome
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation d'imageRésumé : (éditeur) This book focuses on the intelligent processing of images and optical information acquired by various imaging methods. Intelligent image and optical information processing have paved the way for the recent epoch of new intelligence and information era. Certainly, information acquired by various imaging techniques is of tremendous value; thus, an intelligent analysis of them is necessary to make the best use of it. A broad range of research fields is included in this book. Many studies focus on object classification and detection. Registration, segmentation, and fusion are performed between a series of images. Many valuable and up-to-most recent technologies are provided to solve the real problems in selected papers. Note de contenu : 1- Special issue on intelligent processing on image and optical information
2- Change detection of water resources via remote sensing: An L-V-NSCT approach
3- A texture classification approach based on the integrated optimization for parameters and features of gabor filter via hybrid ant lion optimizer
4- Real-time automated segmentation and classification of calcaneal fractures in CT images
5- Automatic zebrafish egg phenotype recognition from bright-field microscopic images using deep convolutional neural network
6- Zebrafish larvae phenotype classification from bright-field microscopic images using a two-tier deep-learning pipeline
7- Unsupervised generation and synthesis of facial images via an auto-encoder-based deep generative adversarial network
8- Detecting green mold pathogens on lemons using hyperspectral images
9- Review on computer aided weld defect detection from radiography images
10- Feature extraction with discrete non-separable shearlet transform and its application to surface inspection of continuous casting slabs
11- A novel extraction method for wildlife monitoring images with wireless multimedia sensor
networks (WMSNs)
12- IMU-aided high-frequency Lidar odometry for autonomous driving
13- Determination of the optimal state of dough fermentation in bread production by using optical sensors and deep learning
14- Multi-sensor face registration based on global and local structures
15- Multifocus image fusion using a sparse and low-rank matrix decomposition for aviator’s night vision Goggle
16- Error resilience for block compressed sensing with multiple-channel transmission
17- Image completion with hybrid interpolation in tensor representation
18- A correction method for heat wave distortion in digital image correlation measurements
based on background-oriented schlieren
19- An effective optimization method for machine learning based on ADAM
20- Boundary matching and interior connectivity-based cluster validity analysisNuméro de notice : 28438 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-945-4 En ligne : https://doi.org/10.3390/books978-3-03936-945-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98875 Interactions between hierarchical learning and visual system modeling : image classification on small datasets / Thalita Firmo Drumond (2020)
Titre : Interactions between hierarchical learning and visual system modeling : image classification on small datasets Type de document : Thèse/HDR Auteurs : Thalita Firmo Drumond, Auteur ; Frédéric Alexandre, Directeur de thèse ; Thierry Viéville, Directeur de thèse Editeur : Bordeaux : Université de Bordeaux Année de publication : 2020 Importance : 195 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée pour obtenir le grade de Docteur de l'Université de Bordeaux, Spécialité InformatiqueLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] classification semi-dirigée
[Termes IGN] corpus
[Termes IGN] échantillonnage de données
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] reconnaissance d'objets
[Termes IGN] taille du jeu de données
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Deep convolutional neural networks (DCNN) have recently protagonized a revolution in large-scale object recognition. They have changed the usual computer vision practices of hand-engineered features, with their ability to hierarchically learn representative features from data with a pertinent classifier. Together with hardware advances, they have made it possible to effectively exploit the ever-growing amounts of image data gathered online. However, in specific domains like healthcare and industrial applications, data is much less abundant, and expert labeling costs higher than those of general purpose image datasets. This scarcity scenario leads to this thesis' core question: can these limited-data domains profit from the advantages of DCNNs for image classification? This question has been addressed throughout this work, based on an extensive study of literature, divided in two main parts, followed by proposal of original models and mechanisms.The first part reviews object recognition from an interdisciplinary double-viewpoint. First, it resorts to understanding the function of vision from a biological stance, comparing and contrasting to DCNN models in terms of structure, function and capabilities. Second, a state-of-the-art review is established aiming to identify the main architectural categories and innovations in modern day DCNNs. This interdisciplinary basis fosters the identification of potential mechanisms - inspired both from biological and artificial structures — that could improve image recognition under difficult situations. Recurrent processing is a clear example: while not completely absent from the "deep vision" literature, it has mostly been applied to videos — due to their inherently sequential nature. From biology however it is clear such processing plays a role in refining our perception of a still scene. This theme is further explored through a dedicated literature review focused on recurrent convolutional architectures used in image classification.The second part carries on in the spirit of improving DCNNs, this time focusing more specifically on our central question: deep learning over small datasets. First, the work proposes a more detailed and precise discussion of the small sample problem and its relation to learning hierarchical features with deep models. This discussion is followed up by a structured view of the field, organizing and discussing the different possible paths towards adapting deep models to limited data settings. Rather than a raw listing, this review work aims to make sense out of the myriad of approaches in the field, grouping methods with similar intent or mechanism of action, in order to guide the development of custom solutions for small-data applications. Second, this study is complemented by an experimental analysis, exploring small data learning with the proposition of original models and mechanisms (previously published as a journal paper).In conclusion, it is possible to apply deep learning to small datasets and obtain good results, if done in a thoughtful fashion. On the data path, one shall try gather more information from additional related data sources if available. On the complexity path, architecture and training methods can be calibrated in order to profit the most from any available domain-specific side-information. Proposals concerning both of these paths get discussed in detail throughout this document. Overall, while there are multiple ways of reducing the complexity of deep learning with small data samples, there is no universal solution. Each method has its own drawbacks and practical difficulties and needs to be tailored specifically to the target perceptual task at hand. Note de contenu : 1- Introduction
I- Object recognition with deep convolutional neural networks
2- Convolutional neural networks and visual system modeling
3- Feedforward CNN architectures for object recognition
4- Recurrent and feedback CNN architectures for object recognition
II- Image classification on small datasets
5- A review of strategies to use deep learning under limited data
6- Analysis of DCNN applied to small sample learning using data prototypes
7 ConclusionNuméro de notice : 28312 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : thèse de Doctorat : Informatique : Bordeaux : 2020 Organisme de stage : Laboratoire bordelais de recherche en informatique DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03129189v2/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98233
Titre : Learning 3D generation and matching Type de document : Thèse/HDR Auteurs : Thibault Groueix, Auteur ; Mathieu Aubry, Directeur de thèse Editeur : Paris : Ecole Nationale des Ponts et Chaussées ENPC Année de publication : 2020 Importance : 169 p. Format : 21 x 30 cm Note générale : bibliographie
A doctoral thesis in the domain of automated signal and image processing submitted to École Doctorale Paris-Est
Mathématiques et Sciences et Technologies de l’Information et de la CommunicationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de formes
[Termes IGN] appariement dense
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déformation de surface
[Termes IGN] isométrie
[Termes IGN] maillage
[Termes IGN] modélisation 3D
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'image
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] voxelIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The goal of this thesis is to develop deep learning approaches to model and analyse 3D shapes. Progress in this field could democratize artistic creation of 3D assets which currently requires time and expert skills with technical software. We focus on the design of deep learning solutions for two particular tasks, key to many 3D modeling applications: single-view reconstruction and shape matching. A single-view reconstruction (SVR) method takes as input a single image and predicts the physical world which produced that image. SVR dates back to the early days of computer vision. In particular, in the 1960s, Lawrence G. Roberts proposed to align simple 3D primitives to the input image under the assumption that the physical world is made of cuboids. Another approach proposed by Berthold Horn in the 1970s is to decompose the input image in intrinsic images and use those to predict the depth of every input pixel. Since several configurations of shapes, texture and illumination can explain the same image, both approaches need to form assumptions on the distribution of images and 3D shapes to resolve the ambiguity. In this thesis, we learn these assumptions from large-scale datasets instead of manually designing them. Learning allows us to perform complete object reconstruction, including parts which are not visible in the input image. Shape matching aims at finding correspondences between 3D objects. Solving this task requires both a local and global understanding of 3D shapes which is hard to achieve explicitly. Instead we train neural networks on large-scale datasets to solve this task and capture this knowledge implicitly through their internal parameters.Shape matching supports many 3D modeling applications such as attribute transfer, automatic rigging for animation, or mesh editing.The first technical contribution of this thesis is a new parametric representation of 3D surfaces modeled by neural networks.The choice of data representation is a critical aspect of any 3D reconstruction algorithm. Until recently, most of the approaches in deep 3D model generation were predicting volumetric voxel grids or point clouds, which are discrete representations. Instead, we present an alternative approach that predicts a parametric surface deformation ie a mapping from a template to a target geometry. To demonstrate the benefits of such a representation, we train a deep encoder-decoder for single-view reconstruction using our new representation. Our approach, dubbed AtlasNet, is the first deep single-view reconstruction approach able to reconstruct meshes from images without relying on an independent post-processing, and can do it at arbitrary resolution without memory issues. A more detailed analysis of AtlasNet reveals it also generalizes better to categories it has not been trained on than other deep 3D generation approaches.Our second main contribution is a novel shape matching approach purely based on reconstruction via deformations. We show that the quality of the shape reconstructions is critical to obtain good correspondences, and therefore introduce a test-time optimization scheme to refine the learned deformations. For humans and other deformable shape categories deviating by a near-isometry, our approach can leverage a shape template and isometric regularization of the surface deformations. As category exhibiting non-isometric variations, such as chairs, do not have a clear template, we learn how to deform any shape into any other and leverage cycle-consistency constraints to learn meaningful correspondences. Our reconstruction-for-matching strategy operates directly on point clouds, is robust to many types of perturbations, and outperforms the state of the art by 15% on dense matching of real human scans. Note de contenu : 1- Introduction
2 Related Work
3 AtlasNet: A Papier-Mache Approach to Learning 3D Surface Generation
4 3D-CODED : 3D Correspondences by Deep Deformation
5 Unsupervised cycle-consistent deformation for shape matching
6 ConclusionNuméro de notice : 28310 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Automated signal and image processing : Paris-Est : 2020 Organisme de stage : LIGM DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03127055v2/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98201 Learning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)PermalinkLow-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network / Yuxing Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)PermalinkPermalinkOn the joint exploitation of optical and SAR satellite imagery for grassland monitoring / Anatol Garioud (2020)PermalinkProbabilistic pose estimation and 3D reconstruction of vehicles from stereo images / Maximilian Alexander Coenen (2020)PermalinkPermalinkRecherche multimodale d'images aériennes multi-date à l'aide d'un réseau siamois / Margarita Khokhlova (2020)PermalinkReconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne / Valentin Desbiolles (2020)PermalinkSatellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)PermalinkVery high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)Permalink