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Titre : Context-aware image super-resolution using deep neural networks Type de document : Thèse/HDR Auteurs : Mohammad Saeed Rad, Auteur ; Jean-Philippe Thiran, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2021 Importance : 148 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée pour l'obtention du grade de Docteur ès SciencesLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image à basse résolution
[Termes IGN] image à haute résolution
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] reconstruction d'image
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation sémantique
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the performance of the single image super-resolution task significantly by benefiting from machine learning and AI-powered algorithms, and more specifically, with the advent of Deep Learning-based approaches. Although these advances allow a machine to learn and have better exploitation of an image and its content, recent methods are still unable to constrain the plausible solution space based on the available contextual information within an image. This limitation mostly results in poor reconstructions, even for well-known types of objects and textures easily recognizable for humans. In this thesis, we aim at proving that the categorical prior, which characterizes the semantic class of a region in an image (e.g., sky, building, plant), is crucial in super-resolution task for reaching a higher reconstruction quality. In particular, we propose several approaches to improve the perceived image quality and generalization capability of deep learning-based methods by exploiting the context and semantic meaning of images. To prove the effectiveness of this categorical information, we first propose a convolutional neural network-based framework that is able to extract and use semantic information to super-resolve a given image by using multitask learning, simultaneously for learning image super-resolution and semantic segmentation. The proposed decoder is forced to explore categorical information during training, as this setting employs only one shared deep network for both semantic segmentation and super-resolution tasks. We further investigate the possibility of using semantic information by a novel objective function to introduce additional spatial control over the training process. We propose penalizing images at different semantic levels using appropriate loss terms by benefiting from our new OBB (Object, Background, and Boundary) labels generated from segmentation labels. Then, we introduce a new test time adaptation-based technique to leverage high-resolution images with perceptually similar context to a given test image to improve the reconstruction quality. We further validate this approach's effectiveness by using a novel numerical experiment analyzing the correlation between filters learned by our network and what we define as `ideal' filters. Finally, we present a generic solution to enable adapting all our previous contributions in this thesis, as well as other recent super-resolution works trained on synthetic datasets, to real-world super-resolution problem. Real-world super-resolution refers to super-resolving images with real degradations caused by physical imaging systems, instead of low-resolution images from simulated datasets assuming a simple and uniform degradation model (i.e., bicubic downsampling). We study and develop an image-to-image translator to map the distribution of real low-resolution images to the well-understood distribution of bicubically downsampled images. This translator is used as a plug-in to integrate real inputs into any super-resolution framework trained on simulated datasets. We carry out extensive qualitative and quantitative experiments for each mentioned contribution, including user studies, to compare our proposed approaches to state-of-the-art method. Note de contenu : 1- Introduction
2- Brief image super-resolution review
3- Extracting image context by multi-task learning
4- Spatial control over image genertion process
5- Test-time adaptation based on perceptual similarity
6- Integrating into real-world SR
7- ConclusionNuméro de notice : 28652 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Sciences : EPFL, Lausanne : 2021 DOI : sans En ligne : https://infoscience.epfl.ch/record/286804?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99790 Contributions to graph-based hierarchical analysis for images and 3D point clouds / Leonardo Gigli (2021)
Titre : Contributions to graph-based hierarchical analysis for images and 3D point clouds Type de document : Thèse/HDR Auteurs : Leonardo Gigli, Auteur ; Beatriz Marcotegui, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2021 Importance : 177 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université PSL, Spécialité : Morphologie MathématiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] arbre aléatoire minimum
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] morphologie mathématique
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] réseau neuronal de graphes
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] texture d'image
[Termes IGN] théorie des graphesIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Graphs are powerful mathematical structures representing a set of objects and the underlying links between pairs of objects somehow related. They are becoming increasingly popular in data science in general and in particular in image or 3D point cloud analysis. Among the wide spectra of applications, they are involved in most of the hierarchical approaches.Hierarchies are particularly important because they allow us to efficiently organize the information required and to analyze the problems at different levels of detail. In this thesis, we address the following topics. Many morphological hierarchical approaches rely on the Minimum Spanning Tree (MST). We propose an algorithm for MST computation in streaming based on a graph decomposition strategy. Thanks to this decomposition, larger images can be processed or can benefit from partial reliable information while the whole image is not completely available.Recent LiDAR developments are able to acquire large-scale and precise 3D point clouds. Many applications, such as infrastructure monitoring, urban planning, autonomous driving, precision forestry, environmental assessment, archaeological discoveries, to cite a few, are under development nowadays. We introduce a ground detection algorithm and compare it with the state of the art. The impact of reducing the point cloud density with low-cost scanners is studied, in the context of an autonomous driving application. Finally, in many hierarchical methods similarities between points are given as input. However, the metric used to compute similarities influences the quality of the final results. We exploit metric learning as a complementary tool that helps to improve the quality of hierarchies. We demonstrate the capabilities of these methods in two contexts. The first one,a texture classification of 3D surfaces. Our approach ranked second in a task organized by SHREC’20 international challenge. The second one learning the similarity function together with the optimal hierarchical clustering, in a continuous feature-based hierarchical clustering formulation. Note de contenu : Introduction
1- Graph theory and clustering
2- Point clouds
3- Ground and road detection
4- Minimum spanning tree for data streams
5- Metric learning
6- Towards Morphological Convolutions on Graphs
ConclusionsNuméro de notice : 28623 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Morphologie Mathématique : Paris Sciences et Lettres : 2021 Organisme de stage : Centre de Morphologie Mathématique DOI : sans En ligne : https://pastel.hal.science/tel-03512298/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99543
Titre : Data mining : methods, applications and systems Type de document : Monographie Auteurs : Derya Birant, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 Importance : 200 p. ISBN/ISSN/EAN : 978-1-83968-319-0 Note générale : Print ISBN 978-1-83968-318-3
eBook (PDF) ISBN 978-1-83968-320-6Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] acquisition de données
[Termes IGN] apprentissage profond
[Termes IGN] arbre de décision
[Termes IGN] exploration de données
[Termes IGN] Python (langage de programmation)
[Termes IGN] série temporelleIndex. décimale : 26.40 Intelligence artificielle Résumé : (Editeur) Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment. Note de contenu :
1. Deep Learning: Exemplar Studies in Natural Language Processing and Computer Vision / Selma Tekir and Yalin Bastanlar
2. Contribution to Decision Tree Induction with Python: A Review / Bouchra Lamrini
3. Association Rule Mining on Big Data Sets / Oguz Celik, Muruvvet Hasanbasoglu, Mehmet S. Aktas and Oya Kalipsiz
4. Data Mining in Banking Sector Using Weighted Decision Jungle Method / Derya Birant
5. Analytical Statistics Techniques of Classification and Regression in Machine Learning / Pramod Kumar, Sameer Ambekar, Manish Kumar and Subarna Roy
6. Clustering of Time-Series Data / Esma Ergüner Özkoç
7. Weather Nowcasting Using Deep Learning Techniques / Makhamisa Senekane, Mhlambululi Mafu and Molibeli Benedict Taele
8. Data Mining and Machine Learning for Software Engineering / Elife Ozturk Kiyak
9. Data Mining for Student Performance Prediction in Education / Ferda Ünal
10. Tracer Transport in a Homogeneous Porous Medium: Experimental Study and Acquisition Data with LabVIEW / Sana Dardouri and Jalila Sghaier
11. Data Mining and Fuzzy Data Mining Using MapReduce Algorithms / Poli Venkata Subba ReddyNuméro de notice : 26539 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87784 En ligne : http://doi.org/10.5772/intechopen.87784 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97753 Deep convolutional neural networks for scene understanding and motion planning for self-driving vehicles / Abdelhak Loukkal (2021)
Titre : Deep convolutional neural networks for scene understanding and motion planning for self-driving vehicles Type de document : Thèse/HDR Auteurs : Abdelhak Loukkal, Auteur ; Yves Grandvalet, Directeur de thèse Editeur : Compiègne : Université de Technologie de Compiègne UTC Année de publication : 2021 Importance : 129 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse présentée pour l’obtention du grade de Docteur de l’UTC, spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] compréhension de l'image
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion de données multisource
[Termes IGN] navigation autonome
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau neuronal profond
[Termes IGN] segmentation sémantique
[Termes IGN] système de navigation
[Termes IGN] véhicule automobile
[Termes IGN] vision monoculaire
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) During this thesis, some perception approaches for self-driving vehicles were developed using de convolutional neural networks applied to monocular camera images and High-Definition map (HD-ma rasterized images. We focused on camera-only solutions instead of leveraging sensor fusion with rang sensors because cameras are the most cost-effective and discrete sensors. The objective was also to show th camera-based approaches can perform at par with LiDAR-based solutions on certain 3D vision tasks. Rea world data was used for training and evaluation of the developed approaches but simulation was als leveraged when annotated data was lacking or for safety reasons when evaluating driving capabilities. Cameras provide visual information in a projective space where the perspective effect does not preserve th distances homogeneity. Scene understanding tasks such as semantic segmentation are then often operated i the camera-view space and then projected to 3D using a precise depth sensor such as a LiDAR. Having thi scene understanding in the 3D space is useful because the vehicles evolve in the 3D world and the navigatio algorithms reason in this space. Our focus was then to leverage the geometric knowledge about the camer parameters and its position in the 3D world to develop an approach that allows scene understanding in the 3D space using only a monocular image as input. Neural networks have also proven to be useful for more than just perception and are more and more used fo the navigation and planning tasks that build on the perception outputs. Being able to output 3D scen understanding information from a monocular camera has also allowed us to explore the possibility of havin an end-to-end holistic neural network that takes a camera image as input, extracts intermediate semantic information in the 3D space and then lans the vehicle's trajectory. Note de contenu : 1. Introduction
1.1 General context
1.2 Framework and objectives
1.3 Organization and contributions of the thesis
2. Background and related work
2.1 Introduction
2.2 Autonomous driving perception datasets
2.3 Autonomous driving simulators
2.4 Semantic segmentation with CNNs
2.5 Monocular depth estimation with CNNs
2.6 Driving with imitation learning
2.7 Conclusion
3. Semantic segmentation using cartographic and depth maps
3.1 Introduction
3.2 Synthetic dataset
3.3 Proposed methods
3.4 Experiments
3.5 Conclusion
4. Disparity weighted loss for semantic segmentation
4.1 Introduction
4.2 Disparity weighting for semantic segmentation
4.3 Experiments
4.4 Conclusion
5. FlatMobileNet: Bird-Eye-View semantic masks from a monoc?ular camera
5.1 Introduction
5.2 Theoretical framework
5.3 FlatMobile network: footprint segmentation
5.4 Conclusion
6. Driving among flatmobiles
6.1 Introduction
6.2 Encoder-decoder LSTM for trajectory planning
6.3 Experimental evaluation
6.4 Conclusion
7. Conclusion
7.1 Contributions
7.2 PerspectivesNuméro de notice : 26769 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Compiègne : 2021 Organisme de stage : Heuristique et Diagnostic des Systèmes Complexes HeuDiaSyC nature-HAL : Thèse DOI : sans Date de publication en ligne : 25/10/2021 En ligne : https://tel.hal.science/tel-03402541/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99871
Titre : Deep-learning for 3D reconstruction Type de document : Thèse/HDR Auteurs : Fabio Tosi, Auteur Editeur : Bologne [Italie] : Université de Bologne Année de publication : 2021 Format : 21 x 30 cm Note générale : bibliographie
PhD Thesis in Computer Science and EngineeringLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] carte de confiance
[Termes IGN] compréhension de l'image
[Termes IGN] profondeur
[Termes IGN] reconstruction 3D
[Termes IGN] réseau antagoniste génératif
[Termes IGN] vision stéréoscopiqueRésumé : (auteur) Depth perception is paramount for many computer vision applications such as autonomous driving and augmented reality. Despite active sensors (e.g., LiDAR, Time-of-Flight, struc- tured light) are quite diffused, they have severe shortcomings that could be potentially addressed by image-based sensors. Concerning this latter category, deep learning has enabled ground-breaking results in tackling well-known issues affecting the accuracy of systems inferring depth from a single or multiple images in specific circumstances (e.g., low textured regions, depth discontinuities, etc.), but also introduced additional concerns about the domain shift occurring between training and target environments and the need of proper ground truth depth labels to be used as the training signals in network learning. Moreover, despite the copious literature concerning confidence estimation for depth from a stereo setup, inferring depth uncertainty when dealing with deep networks is still a major challenge and almost unexplored research area, especially when dealing with a monocular setup. Finally, computational complexity is another crucial aspect to be considered when targeting most practical applications and hence is desirable not only to infer reliable depth data but do so in real-time and with low power requirements even on standard embedded devices or smartphones. Therefore, focusing on stereo and monocular setups, this thesis tackles major issues affecting methodologies to infer depth from images and aims at developing accurate and efficient frameworks for accurate 3D reconstruction on challenging environments. Note de contenu : Introduction
1- Related work
2- Datasets
3- Evaluation protocols
4- Confidence measures in a machine learning world
5- Efficient confidence measures for embedded stereo
6- Even more confident predictions with deep machine-learning
7- Beyond local reasoning for stereo confidence estimation with deep learning
8- Good cues to learn from scratch a confidence measure for passive depth sensors
9- Confidence estimation for ToF and stereo sensors and its application to depth data fusion
10- Learning confidence measures in the wild
11- Self-adapting confidence estimation for stereo
12- Leveraging confident points for accurate depth refinement on embedded systems
13- SMD-Nets: Stereo Mixture Density Networks
14- Real-time self-adaptive deep stereo
15- Guided stereo matching
16- Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation
17- Learning end-to-end scene flow by distilling single tasks knowledge
18- Learning monocular depth estimation with unsupervised trinocular assumptions
19- Geometry meets semantics for semi-supervised monocular depth estimation
20- Generative Adversarial Networks for unsupervised monocular depth prediction
21- Learning monocular depth estimation infusing traditional stereo knowled
22- Towards real-time unsupervised monocular depth estimation on CPU
23- Enabling energy-efficient unsupervised monocular depth estimation on ARMv7-based platforms
24- Distilled semantics for comprehensive scene understanding from videos
25- On the uncertainty of self-supervised monocular depth estimation
ConclusionNuméro de notice : 28596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Computer Science and Engineering : Bologne : 2021 DOI : 10.48676/unibo/amsdottorato/9816 En ligne : http://amsdottorato.unibo.it/9816/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99325 Deep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)PermalinkDescription et recherche d’image généralisables pour l’interconnexion et l’analyse multi-source / Dimitri Gominski (2021)PermalinkDétection d’ouvertures par segmentation sémantique de nuages de points 3D : apport de l’apprentissage profond / Camille Lhenry (2021)PermalinkDétection/reconnaissance d'objets urbains à partir de données 3D multicapteurs prises au niveau du sol, en continu / Younes Zegaoui (2021)PermalinkDétection et reconstruction 3D d’arbres urbains par segmentation de nuages de points : apport de l’apprentissage profond / Victor Alteirac (2021)PermalinkEvaluation of a neural network with uncertainty for detection of ice and water in SAR imagery / Nazanin Asadi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkExploration of reinforcement learning algorithms for autonomous vehicle visual perception and control / Florence Carton (2021)PermalinkExtracting event-related information from a corpus regarding soil industrial pollution / Chuanming Dong (2021)PermalinkFrom point clouds to high-fidelity models - advanced methods for image-based 3D reconstruction / Audrey Richard (2021)PermalinkFuNet: A novel road extraction network with fusion of location data and remote sensing imagery / Kai Zhou in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)PermalinkGenerative adversarial networks to generalise urban areas in topographic maps / Azelle Courtial (2021)PermalinkImage matching from handcrafted to deep features: A survey / Jiayi Ma in International journal of computer vision, vol 29 n° 1 (January 2021)PermalinkImproving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / Roholah Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkInitialization methods of convolutional neural networks for detection of image manipulations / Ivan Castillo Camacho (2021)PermalinkPermalinkLANet: Local attention embedding to improve the semantic segmentation of remote sensing images / Lei Ding in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkLearning-based representations and methods for 3D shape analysis, manipulation and reconstruction / Marie-Julie Rakotosaona (2021)PermalinkPermalinkLearning embeddings for cross-time geographic areas represented as graphs / Margarita Khokhlova (2021)PermalinkPermalink