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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 Interactions between hierarchical learning and visual system modeling : image classification on small datasets / Thalita Firmo Drumond (2020)
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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 stereo reconstruction with deep neural networks Type de document : Thèse/HDR Auteurs : Stepan Tulyakov, Auteur ; François Fleuret, Directeur de thèse ; Anton Ivanov, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2020 Importance : 139 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée à l'Ecole Polytechnique Fédérale de Lausanne pour l’obtention du grade de Docteur ès SciencesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification semi-dirigée
[Termes IGN] contrainte géométrique
[Termes IGN] couple stéréoscopique
[Termes IGN] entropie
[Termes IGN] estimateur
[Termes IGN] étalonnage géométrique
[Termes IGN] modèle stéréoscopique
[Termes IGN] profondeur
[Termes IGN] réalité de terrain
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'image
[Termes IGN] vision par ordinateur
[Termes IGN] vision stéréoscopiqueRésumé : (auteur) Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these methods, is that they typically utilize a single depth cue, such as parallax, defocus blur or shading, and thus are not as robust as a human visual system that simultaneously relies on a range of monocular and binocular cues. This is mainly because it is hard to manually design a model, accounting for multiple depth cues. In this work, we address this problem by focusing on deep learning-based stereo methods that can discover a model for multiple depth cues directly from training data with ground truth depth. The complexity of deep learning-based methods, however, requires very large training sets with ground truth depth, which is often hard or costly to collect. Furthermore, even when training data is available it is often contaminated with noise, which reduces the effectiveness of supervised learning. In this work, in Chapter 3 we show that it is possible to alleviate this problem by using weakly supervised learning, that utilizes geometric constraints of the problem instead of ground truth depth. Besides the large training set requirement, deep stereo methods are not as application-friendlyas traditional methods. They have a large memory footprint and their disparity range is fixed at training time. For some applications, such as satellite stereo i magery, these are serious problems since satellite images are very large, often reaching tens of megapixels, and have a variable baseline, depending on a time difference between stereo images acquisition. In this work, in Chapter 4 we address these problems by introducing a novel network architecture with a bottleneck, capable of processing large images and utilizing more context, and an estimator that makes the network less sensitive to stereo matching ambiguities and applicable to any disparity range without re-training. Because deep learning-based methods discover depth cues directly from training data, they can be adapted to new data modalities without large modifications. In this work, in Chapter 5 we show that our method, developed for a conventional frame-based camera, can be used with a novel event-based camera, that has a higher dynamic range, smaller latency, and low power consumption. Instead of sampling intensity of all pixels with a fixed frequency, this camera asynchronously reports events of significant pixel intensity changes. To adopt our method to this new data modality, we propose a novel event sequence embedding module, that firstly aggregates information locally, across time, using a novel fully-connected layer for an irregularly sampled continuous domain, and then across discrete spatial domain. One interesting application of stereo is a reconstruction of a planet’s surface topography from satellite stereo images. In this work, in Chapter 6 we describe a geometric calibration method, as well as mosaicing and stereo reconstruction tools that we developed in the framework of the doctoral project for Color and Stereo Surface Imaging System onboard of ESA’s Trace Gas Orbiter, orbiting Mars. For the calibration, we propose a novel method, relying on starfield images because large focal lengths and complex optical distortion of the instrument forbid using standard methods. Scientific and practical results of this work are widely used by a scientific community. Note de contenu : 1- Introduction
2- Background
3- Weakly supervised learning of deep patch-matching cost
4- Applications-friendly deep stereo
5- Dense deep event-based stereo
6- Calibration of a satellite stereo system
7- ConclusionsNuméro de notice : 25795 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Sciences : Lausanne : 2020 En ligne : https://infoscience.epfl.ch/record/275342?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95025 On the adjustment, calibration and orientation of drone photogrammetry and laser-scanning / Emmanuel Clédat (2020)
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Titre : On the adjustment, calibration and orientation of drone photogrammetry and laser-scanning Type de document : Thèse/HDR Auteurs : Emmanuel Clédat , Auteur ; Jan Skaloud, Directeur de thèse ; Davide Antonio Cucci, Directeur de thèse
Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] centrale inertielle
[Termes IGN] compensation par bloc
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage de chambre métrique
[Termes IGN] fusion de données
[Termes IGN] GPS-INS
[Termes IGN] image captée par drone
[Termes IGN] optimisation (mathématiques)
[Termes IGN] point d'appui
[Termes IGN] vision par ordinateurRésumé : (auteur) Centimetre level precision mapping is essential for many applications such as land-use, infrastructure inspection, cultural heritage preservation, and construction site monitoring. However, the acquisition and its preparation (in particular the setting of a ground control point network (GCPs)) are still expensive or even impossible in cluttered or dangerous areas. The recent development of UAVs together with the miniaturization of the sensors is a promising evolution for reducing costs and expand opportunities. The sensors embedded on the drone: GNSS antenna, IMU, camera and (optional) LIDAR are light and often low-cost. The low quality of their raw measurements must be counterbalanced by their rigorous modeling in order to obtain accurate final results: if we cannot expect the sensors to be error-free, one must model these in order to correct them. This is achieved by in-situ calibration or on a dedicated calibration field, together with a rigorous fusion of the raw data acquired by the different sensors with the so-called bundle-adjustment method. This thesis proposes several models to describe the behavior of the sensors, in order to hybridize them rigorously in the bundle-adjustment. Consistent datasets have been acquired on the field specifically to assess the relevance of both the sensor models and their hybridizing in complex photogrammetric processing. The contribution of this thesis could be divided into two mains categories. On one hand, this thesis suggests tools and recommendation to improve directly the procedures achieved by end-users using current UAV-mapping commercial solutions (in particular for the GCPs placement, for the choice of the camera calibration and model and for the flight-plan). On the other hand, this thesis put forward exotic methods (methods considered as exotic at the time of the writing of the thesis) such as Photo-LIDAR hybridizing and collaborative mapping achieved by a terrestrial-aerial tandem (a terrestrial vehicle holding a LIDAR, GNSS, imaging and inertial sensors followed by a drone conceived to proceed to airborne photogrammetry) or an aerial-aerial tandem (two drones flying in formation to proceed to airborne photogrammetry). The contribution of this thesis will permit to reduce costs, to improve the quality of mapping products and to enlarge the possibilities of mapping: in particular, map cluttered or inaccessible zones which are nowadays considered as difficult or even impossible to map. Numéro de notice : 17737 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Organisme de stage : Laboratoire de topométrie (EPFL) DOI : 10.5075/epfl-thesis-7826 En ligne : https://doi.org/10.5075/epfl-thesis-7826 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100752 Smoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors / Paul Chauchat (2020)
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Titre : Smoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors Type de document : Thèse/HDR Auteurs : Paul Chauchat, Auteur ; Silvère Bonnabel, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2020 Importance : 135 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Paris Sciences et Lettres, Informatique temps réel, robotique, automatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] cadre conceptuel
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] centrale inertielle
[Termes IGN] filtre de Kalman
[Termes IGN] itération
[Termes IGN] lissage de données
[Termes IGN] navigation inertielle
[Termes IGN] robotiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Mobile systems need to locate themselves ever more accurately, and in ever more complex situations. This is in particular true for autonomous systems, for which controlling the position error is a critical safety issue. To this end, they are endowed with various sensors, the data of which are fused to obtain an estimate of the vehicle’s location, either globally (with the GPS for instance), or locally, with respect to its surroundings (with cameras for instance). This thesis investigates algorithms for localisation by sensor fusion, namely filtering and especially smoothing, when the mobile is equipped with high-grade inertial sensors. The first part deals with the nonlinear consequences of the use of high-grade inertial sensors, and demonstrates how the nonlinear structure of both filtering and smoothing algorithms may be improved by leveraging the invariant filtering framework. The second part deals with the problems incurred by the linear solvers that are used at each step of nonlinear smoothing algorithms as a result of having highly precise sensors. It introduces a novel least-squares linear solver that solves the issues. Note de contenu : Introduction
I- From Invariant filtering to invariant smoothing
II- Navigation with highly precise sensors
ConclusionNuméro de notice : 28576 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/POSITIONNEMENT Nature : Thèse française Note de thèse : thèse de Doctorat : Informatique temps réel, robotique, automatique : Paris Sciences et Lettres : 2020 Organisme de stage : Centre de robotique (Paris) En ligne : https://pastel.archives-ouvertes.fr/tel-02887295/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97843 PermalinkInnovative techniques of photogrammetry for 3D modeling / Vicenzo Barrile in Applied geomatics, Vol 11 n° 4 (December 2019)
PermalinkContext pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)
PermalinkDetecting and mapping traffic signs from Google Street View images using deep learning and GIS / Andrew Campbell in Computers, Environment and Urban Systems, vol 77 (september 2019)
PermalinkEnhanced 3D mapping with an RGB-D sensor via integration of depth measurements and image sequences / Bo Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)
PermalinkAnalyse d’images par méthode de Deep Learning appliquée au contexte routier en conditions météorologiques dégradées / Khouloud Dahmane (2019)
PermalinkEstimation de profondeur à partir d'images monoculaires par apprentissage profond / Michel Moukari (2019)
PermalinkPermalinkSeeing the past with computers: Experiments with augmented reality and computer vision for history / Kevin Kee (2019)
PermalinkStructure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation / Xin Wang in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)
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