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Model based signal processing techniques for nonconventional optical imaging systems / Daniele Picone (2021)
Titre : Model based signal processing techniques for nonconventional optical imaging systems Type de document : Thèse/HDR Auteurs : Daniele Picone, Auteur ; Mauro Dalla Mura, Directeur de thèse Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2021 Importance : 364 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour obtenir le grade de Docteur de l'Université Grenoble Alpes, spécialité : Signal Image Parole TélécomsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] acquisition comprimée
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] inférence statistique
[Termes IGN] interférométrie
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] mosaïque d'images
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] problème inverse
[Termes IGN] reconstruction d'image
[Termes IGN] régression non linéaire
[Termes IGN] spectromètre imageur
[Termes IGN] traitement du signalIndex. décimale : THESE Thèses et HDR Résumé : (auteur) There is an increasing demand for images with higher spectral and spatial resolution for applications in several domains such as health, environment, quality checking and natural disasters monitoring. Hyperspectral imagery provides the necessary spectral diversity to recover the composition of materials on site for applications such as the detection of fires, anomalies, chemical agents, targets and changes in the scene.The requirement for cheaper and more compact devices (e.g. to be embarked on low cost satellites and airborne platform) which are capable of capturing this information has led to the development of nonconventional innovative design concepts to overcome the technological limitations of traditional cameras. Data acquired by such novel imaging devices following the computational imaging paradigm are typically not readily exploitable for the final application. A computational phase is hence needed for extracting useful information from the raw acquisitions.This thesis addresses this issue by setting up an inversion problem. The general approach is to characterize the data fidelity term with a physical model, describing the underlying optical transformations performed by the device. The challenge is then shifted on the regularization step to properly characterizes the features of the quantities of interest and improve the accuracy of the estimation, which can be tackled with variational techniques.The analysis is applied to two novel concepts for nonconventional optical devices. The first one is a novel compressed acquisition imaging system based on color filter arrays, which embeds information from sensors with different spatial and spectral characteristics into a single mosaiced product. As opposed to existing compressed sensing based devices, the goal is not to recover the original uncompressed multiresolution sources, but instead to directly recover a synthetic fused image with both high spatial and spectral resolution.The proposed solution relies on the total variation regularization and is the subject of a detailed analysis, comparing its compressive power with straightforward software alternatives, evaluating its performances as the amount of channels changes, and validating its efficiency in comparison to state of the art methods when applied to classical fusion or mosaicing algorithms separately.The second class of devices is based on the ImSPOC patent, a design concept for a low finesse snapshot imaging spectrometer based on the interferometry of Fabry-Pérot. Its ideal behaviour follows the principle of the Fourier Transform Spectroscopy, as its acquisition can be interpreted as a sampled version of an interferogram, arranged across different sub-images distributed on the same focal plane.After defining a physical model based on optical geometry, its validity is evaluated over real acquisitions by setting up a Bayesian inference problem to determine its parameters, with approaches based on maximum likelihood estimators, regular-grid searches and nonlinear regression.A variety of preliminary tests are then carried out on the inversion method, with approaches based on singular value decomposition and sparse-inducing regularizers, accompanied by a analysis of their robustness to model mismatches. Note de contenu : 1- Introduction
2- Inverse problems theory
3- Signal processing of multimodal data
4- Joint fusion and demosaicing of compressed multiresolution acquisitions
5- Optics foundations for the ImSPOC acquisition system
6- Data processing pipeline of ImSPOC acquisitions
7- ConclusionsNuméro de notice : 28691 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Signal Image Parole Télécoms : Grenoble : 2021 Organisme de stage : GIPSA-lab DOI : sans En ligne : https://hal.science/tel-03596486v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100170 Modélisation et reconstitution 3D de vestiges du Struthof en relation avec le PCR à partir d’éléments historiques / Yassine Seddik (2021)
Titre : Modélisation et reconstitution 3D de vestiges du Struthof en relation avec le PCR à partir d’éléments historiques Type de document : Mémoire Auteurs : Yassine Seddik, Auteur Editeur : Strasbourg : Institut National des Sciences Appliquées INSA Strasbourg Année de publication : 2021 Importance : 80 p. Format : 21 x 30 cm Note générale : bibliographie
Mémoire de soutenance du Diplôme d’Ingénieur INSA Spécialité TopographieLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Alsace (France administrative)
[Termes IGN] fusion de données
[Termes IGN] image ancienne
[Termes IGN] lasergrammétrie
[Termes IGN] modèle 3D du site
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation du bâti
[Termes IGN] patrimoine archéologique
[Termes IGN] patrimoine culturel
[Termes IGN] photogrammétrie terrestre
[Termes IGN] reconstruction 3D
[Termes IGN] tunnelIndex. décimale : INSAS Mémoires d'ingénieur de l'INSA Strasbourg - Topographie, ex ENSAIS Résumé : (auteur) Le camp de Natzweiler-Struthof est le seul camp de concentration en Alsace, en France. En 1941, lorsque la construction de ce camp a commencé, le régime nazi avait déjà mis en place plusieurs camps de concentration dans les territoires annexés. Le but de ce camp était principalement d'interner la résistance jugée dangereuse pour le régime. D'un point de vue chronologique, le camp a intégré en mai 1941 ses premiers prisonniers. Ils furent condamnés à effectuer des travaux inhumains jusqu'à l'évacuation du camp le 2 septembre 1944. Le camp de Natzweiler-Struthof était associé à une carrière de granit où l'on trouve encore les fondations en béton d'anciens bâtiments ainsi que trois galeries creusées à l'explosif. Le travail de numérisation vise à archiver, analyser et comprendre l'organisation et le fonctionnement pour aboutir à une reconstitution 3D du site. En 2018, le site du Struthof a entamé un grand projet de restauration. Pour la première fois dans ce camp, un diagnostic archéologique a alors été réalisé dans le but de comprendre les aménagements encore existants et de supposer la présence d'autres éléments aujourd'hui détruits. Afin d'approfondir les connaissances sur ce camp, la Direction régionale des affaires culturelles a autorisé en 2020 la réalisation d'une prospection accompagnée d'une étude du bâti dans une zone encore vierge de toute recherche : la carrière. Actuellement, cette partie du camp présente les vestiges de trois bâtiments et de trois galeries. Pour en savoir plus sur ces éléments et indirectement sur la vie du camp et de ses prisonniers, cette étude montre la démarche adoptée pour préparer la reconstitution et modélisation 3D des bâtiments et des galeries. Note de contenu : 1- Etat de l'Art
2- Acquisition de données
3- Modélisation des galeries souterraines
4- Reconstruction des bâtiments sur MAYA
Conclusion et perspectivesNuméro de notice : 28604 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire ingénieur INSAS Organisme de stage : ICube En ligne : http://eprints2.insa-strasbourg.fr/4521/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99445 Multi-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot (2021)
Titre : Multi-modal temporal attention models for crop mapping from satellite time series Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu , Auteur ; Nesrine Chehata , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2021 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] base de données d'images
[Termes IGN] carte agricole
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] Pastis
[Termes IGN] segmentation d'imageRésumé : (auteur) Optical and radar satellite time series are synergetic: optical images contain rich spectral information, while C-band radar captures useful geometrical information and is immune to cloud cover. Motivated by the recent success of temporal attention-based methods across multiple crop mapping tasks, we propose to investigate how these models can be adapted to operate on several modalities. We implement and evaluate multiple fusion schemes, including a novel approach and simple adjustments to the training procedure, significantly improving performance and efficiency with little added complexity. We show that most fusion schemes have advantages and drawbacks, making them relevant for specific settings. We then evaluate the benefit of multimodality across several tasks: parcel classification, pixel-based segmentation, and panoptic parcel segmentation. We show that by leveraging both optical and radar time series, multimodal temporal attention-based models can outmatch single-modality models in terms of performance and resilience to cloud cover. To conduct these experiments, we augment the PASTIS dataset with spatially aligned radar image time series. The resulting dataset, PASTIS-R, constitutes the first large-scale, multimodal, and open-access satellite time series dataset with semantic and instance annotations. Numéro de notice : P2021-005 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : sans Date de publication en ligne : 14/12/2021 En ligne : https://arxiv.org/abs/2112.07558v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99392
Titre : Multispectral object detection Type de document : Thèse/HDR Auteurs : Heng Zhang, Auteur ; Elisa Fromont, Directeur de thèse ; Sébastien Lefèvre, Directeur de thèse Editeur : Rennes : Université de Rennes 1 Année de publication : 2021 Importance : 114 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse présentée en vue de l’obtention du grade de docteur en Informatique de l'Université de Rennes 1Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] chambre de prise de vue thermique
[Termes IGN] détection d'objet
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] efficacité
[Termes IGN] fusion de données multisource
[Termes IGN] image multibande
[Termes IGN] précision de la classification
[Termes IGN] qualité du modèle
[Termes IGN] segmentation sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Only using RGB cameras for automatic outdoor scene analysis is challenging when, for example, facing insufficient illumination or adverse weather. To improve the recognition reliability, multispectral systems add additional cameras (e.g. infra-red) and perform object detection from multispectral data. Although multispectral scene analysis with deep learning has been shown to have a great potential, there are still many open research questions and it has not been widely deployed in industrial contexts. In this thesis, we investigated three main challenges about multispectral object detection: (1) the fast and accurate detection of objects of interest from images; (2) the dynamic and adaptive fusion of information from different modalities;(3) low-cost and low-energy multispectral object detection and the reduction of its manual annotation efforts. In terms of the first challenge, we first optimize the label assignment of the object detection training with a mutual guidance strategy between the classification and localization tasks; we then realize an efficient compression of object detection models including the teacher-student prediction disagreements in a feature-based knowledge distillation framework. With regard to the second challenge, three different multispectral feature fusion schemes are proposed to deal with the most difficult fusion cases where different cameras provide contradictory information. For the third challenge, a novel modality distillation framework is firstly presented to tackle the hardware and software constraints of current multispectral systems; then a multi-sensor-based active learning strategy is designed to reduce the labeling costs when constructing multispectral datasets. Note de contenu : 1. Introduction
1.1 Context and motivations
1.2 Thesis outline
2. Deep learning background
2.1 General object detection
2.2 Multispectral object detection
2.3 Knowledge distillation
2.4 Active learning
2.5 Datasets
3. Efficient object detection on embedded devices
3.1 Best practices for training object detection models
3.2 Mutual Guidance for Anchor Matching
3.3 Prediction Disagreement aware Feature Distillation
3.4 Experimental results
4. Information fusion from multispectral data
4.1 Multispectral Fusion with Cyclic Fuse-and-Refine
4.2 Progressive Spectral Fusion
4.3 Experimental results for CFR and PS-Fuse
4.4 Guided Attentive Feature Fusion
4.5 Experimental results for GAFF
5. Sensors and annotations: low cost multispectral data processing
5.1 Deep Active Learning from Multispectral Data
5.2 Low-cost Multispectral Scene Analysis with Modality Distillation
6. Conclusions and future works
6.1 Conclusions
6.2 Application to remote sensing data
6.3 PerspectivesNuméro de notice : 26765 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Rennes 1 : 2021 Organisme de stage : (IRISA) INRIA nature-HAL : Thèse DOI : sans Date de publication en ligne : 17/01/2022 En ligne : https://hal.science/tel-03530257/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99855 Object detection using component-graphs and ConvNets with application to astronomical images / Thanh Xuan Nguyen (2021)
Titre : Object detection using component-graphs and ConvNets with application to astronomical images Type de document : Thèse/HDR Auteurs : Thanh Xuan Nguyen, Auteur ; Laurent Najman, Directeur de thèse ; Hugues Talbot, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2021 Importance : 175 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée en vue de l'obtention du Doctorat de l'Université Gustave Eiffel, Discipline InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de filtrage
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] image multibande
[Termes IGN] lissage de données
[Termes IGN] morphologie mathématique
[Termes IGN] théorie des graphesIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This work investigates object detection algorithms with application to astronomical images. We specifically target to detect faint astronomical sources which value near the image background level. Our main directions include Mathematical Morphology (MM) and Convolutional Neural Network (ConvNet). The contributions of this study are presented in two parts:The first part proposes a novel morphological-based approach based on component-graphs and statistical hypothesis tests. The component-graphs can efficiently handle multi-band images while the statistical hypothesis tests can identify components that are significantly different from the background level. Beyond the classical component-trees and their multivariate extensions, the component-graph holds the complete structural information of multi-band images as directed acyclic graphs (DAGs). Such DAGs are more general and more powerful at the cost of non-trivial object filtering algorithms. Then, we introduce two algorithms to filter duplicated and partial components in the component-graphs. Experiments demonstrate that our proposed approach significantly improves object detection on both multi-band simulated and real astronomical images.The second part turns our attention to ConvNet direction.We introduce a real dataset of annotated astronomical objects.Based on this dataset, we propose two models: a ConvNet-based model and a hybrid model. The ConvNet-based model tailors astronomical contexts with three novel components, including a normalization layer, an object differentiation module, and a smoothness regularizer. Besides, the hybrid model uses both Morphology and ConvNet. In the hybrid method, morphological modules select region proposals while ConvNet extracts relevant information from the selected proposals. Ablation studies show that the two proposed models outperform the state of the art on both synthetic and real datasets. Note de contenu : Introduction
1- Object Detection in Astronomy
I- Mathematical morphology
2- Morphological Connected Operators
3- Object Detection with Component-graphs
II- ConvNet and morphology
4- ConvNet Object Detection Literature
5- ConvNet and Morphology
conclusions and perspectivesNuméro de notice : 15766 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Université Gustave Eiffel : 2021 Organisme de stage : Laboratoire d'Informatique Gaspard-Monge DOI : sans En ligne : https://hal.science/tel-03622555v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100960 PermalinkPlanimetric simplification and lexicographic optimal chains for 3D urban scene reconstruction / Julien Vuillamy (2021)PermalinkReal-time multimodal semantic scene understanding for autonomous UGV navigation / Yifei Zhang (2021)PermalinkReconnaissance spécifique et cartographie des arbres de la canopée en forêt tropicale en Guyane française par fusion de données lidar et hyperspectrales appliquées aux besoins de la gestion forestière / Anthony Laybros (2021)PermalinkRemote sensing and GIS / Basudeb Bhatta (2021)PermalinkRemotely-sensed rip current dynamics and morphological control in high-energy beach environments / Isaac Rodriguez Padilla (2021)PermalinkRendu basé image d'images historiques / Maria Scarlleth Gomes de Castro (2021)PermalinkRetrieving surface soil water content using a soil texture adjusted vegetation index and unmanned aerial system images / Haibin Gu in Remote sensing, vol 13 n° 1 (January-1 2021)PermalinkA review of image fusion techniques for pan-sharpening of high-resolution satellite imagery / Farzaneh Dadrass Javan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkPermalinkPermalinkSpectral variability in hyperspectral unmixing : Multiscale, tensor, and neural network-based approaches / Ricardo Augusto Borsoi (2021)PermalinkStructure-from-motion-derived digital surface models from historical aerial photographs: A new 3D application for coastal dune monitoring / Edoardo Grottoli in Remote sensing, vol 13 n° 1 (January-1 2021)PermalinkPermalinkSUMAC'21: Proceedings of the 3rd Workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2021)PermalinkSuper-resolution of VIIRS-measured ocean color products using deep convolutional neural network / Xiaoming Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkTélédétection hyperspectrale pour l’identification et la caractérisation de minéraux industriels / Ronan Rialland (2021)PermalinkTélédétection synchronisée hyperspectrale et LiDAR à retour d’onde complet : application au suivi des littoraux sableux de la région Pays-de-la-Loire / Giovanni Frati (2021)PermalinkThe challenge of robust trait estimates with deep learning on high resolution RGB images / Etienne David (2021)PermalinkVisual exploration of historical image collections: An interactive approach through space and time / Evelyn Paiz-Reyes (2021)PermalinkCNN-based tree species classification using high resolution RGB image data from automated UAV observations / Sebastian Egli in Remote sensing, vol 12 n° 23 (December-2 2020)PermalinkAutomatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - 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