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Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction / Torstein Fjeldstad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)
Titre : Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction Type de document : Article/Communication Auteurs : Torstein Fjeldstad, Auteur ; Henning Omre, Auteur Année de publication : 2020 Article en page(s) : pp 1957 - 1968 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] amplitude
[Termes descripteurs IGN] analyse mathématique
[Termes descripteurs IGN] approximation
[Termes descripteurs IGN] chaîne de Markov
[Termes descripteurs IGN] filtrage numérique d'image
[Termes descripteurs IGN] lithologie
[Termes descripteurs IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation)
[Termes descripteurs IGN] modèle d'inversion
[Termes descripteurs IGN] modèle mathématique
[Termes descripteurs IGN] processus gaussien
[Termes descripteurs IGN] sismicitéRésumé : (Auteur) The efficient assessment of convolved hidden Markov models is discussed. The bottom layer is defined as an unobservable categorical first-order Markov chain, whereas the middle layer is assumed to be a Gaussian spatial variable conditional on the bottom layer. Hence, this layer appears marginally as a Gaussian mixture spatial variable. We observe the top layer as a convolution of the middle layer with Gaussian errors. The focus is on assessing the categorical and Gaussian mixture variables given the observations, and we operate in a Bayesian inversion framework. The model is defined to perform the inversion of subsurface seismic amplitude-versus-offset data into lithology/fluid classes and to assess the associated seismic material properties. Due to the spatial coupling in the likelihood functions, evaluation of the posterior normalizing constant is computationally demanding, and brute-force, single-site updating Markov chain Monte Carlo (MCMC) algorithms converge far too slowly to be useful. We construct two classes of approximate posterior models, which we assess analytically and efficiently using the recursive forward–backward algorithm. These approximate posterior densities are used as proposal densities in an independent proposal MCMC algorithm to determine the correct posterior model. A set of synthetic realistic examples is presented. The proposed approximations provide efficient proposal densities, which results in acceptance probabilities in the range 0.10–0.50 in the MCMC algorithm. A case study of lithology/fluid seismic inversion is presented. The lithology/fluid classes and the seismic material properties can be reliably predicted. Numéro de notice : A2020-093 Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2951205 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2951205 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94667
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1957 - 1968[article]
Titre : Camera orientation, calibration and inverse perspective with uncertainties: a Bayesian method applied to area estimation from diverse photographs Type de document : Article/Communication Auteurs : Grégoire Guillet, Auteur ; Thomas Guillet, Auteur ; Ludovic Ravanel, Auteur Année de publication : 2020 Article en page(s) : pp 237 - 255 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] ajustement de paramètres
[Termes descripteurs IGN] appariement d'images
[Termes descripteurs IGN] autocorrélation spatiale
[Termes descripteurs IGN] distorsion d'image
[Termes descripteurs IGN] estimation bayesienne
[Termes descripteurs IGN] étalonnage de chambre métrique
[Termes descripteurs IGN] figuration de la densité
[Termes descripteurs IGN] fonction inverse
[Termes descripteurs IGN] image 2D
[Termes descripteurs IGN] image aérienne
[Termes descripteurs IGN] incertitude géométrique
[Termes descripteurs IGN] longueur focale
[Termes descripteurs IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes descripteurs IGN] modèle numérique de surface
[Termes descripteurs IGN] orientation externe
[Termes descripteurs IGN] photographie numérique
[Termes descripteurs IGN] vue 3D
[Termes descripteurs IGN] vue perspectiveRésumé : (Auteur) Large collections of images have become readily available through modern digital catalogs, from sources as diverse as historical photographs, aerial surveys, or user-contributed pictures. Exploiting the quantitative information present in such wide-ranging collections can greatly benefit studies that follow the evolution of landscape features over decades, such as measuring areas of glaciers to study their shrinking under climate change. However, many available images were taken with low-quality lenses and unknown camera parameters. Useful quantitative data may still be extracted, but it becomes important to both account for imperfect optics, and estimate the uncertainty of the derived quantities. In this paper, we present a method to address both these goals, and apply it to the estimation of the area of a landscape feature traced as a polygon on the image of interest. The technique is based on a Bayesian formulation of the camera calibration problem. First, the probability density function (PDF) of the unknown camera parameters is determined for the image, based on matches between 2D (image) and 3D (world) points together with any available prior information. In a second step, the posterior distribution of the feature area of interest is derived from the PDF of camera parameters. In this step, we also model systematic errors arising in the polygon tracing process, as well as uncertainties in the digital elevation model. The resulting area PDF therefore accounts for most sources of uncertainty. We present validation experiments, and show that the model produces accurate and consistent results. We also demonstrate that in some cases, accounting for optical lens distortions is crucial for accurate area determination with consumer-grade lenses. The technique can be applied to many other types of quantitative features to be extracted from photographs when careful error estimation is important. Numéro de notice : A2020-015 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.013 date de publication en ligne : 02/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.013 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94404
in ISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 237 - 255[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020011 SL Revue Centre de documentation Revues en salle Disponible 081-2020013 DEP-RECP Revue MATIS Dépôt en unité Exclu du prêt 081-2020012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Simultaneous characterization of objects temperature and radiative properties through multispectral infrared thermography / Thibaud Toullier (2019)
Titre : Simultaneous characterization of objects temperature and radiative properties through multispectral infrared thermography Type de document : Thèse/HDR Auteurs : Thibaud Toullier, Auteur ; Laurent Mevel, Directeur de thèse ; Jean Dumoulin, Directeur de thèse Editeur : Rennes : Université de Rennes 1 Année de publication : 2019 Importance : 233 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat Mathématique et Sciences et Technologies de l’Information et de la Communication, Spécialité Signal, Image, Vision, Université Signal, Image, Vision,Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes descripteurs IGN] construction
[Termes descripteurs IGN] contrôle thermique
[Termes descripteurs IGN] données météorologiques
[Termes descripteurs IGN] données multicapteurs
[Termes descripteurs IGN] estimation bayesienne
[Termes descripteurs IGN] filtre de Kalman
[Termes descripteurs IGN] image multibande
[Termes descripteurs IGN] image thermique
[Termes descripteurs IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes descripteurs IGN] Python (langage de programmation)
[Termes descripteurs IGN] rayonnement solaire
[Termes descripteurs IGN] scène 3D
[Termes descripteurs IGN] surveillance d'ouvrage
[Termes descripteurs IGN] thermographieRésumé : (auteur) The latest technological improvements in low-cost infrared cameras have brought new opportunities for long-term infrastructures monitoring. The accurate measurement of surfaces' temperatures is facing the lack of knowledge of radiatives properties of the scene. By using multi-sensors instrumentation, the measurement model can be refined to get a better estimate of the temperature. To overcome a lack of sensors instrumentation, it is shown that online and free available climatic data can be used. Then, Bayesian methods to estimate simultaneously the emissivity and temperature have been developed and compared to literature's methods. A radiative exchange simulator of 3D scenes have been developed to compare those different methods on numerical data. This software uses the hardware acceleration as well as a GPGPU approach to reduce the computation time. As a consequence, obtained numerical results emphasized an advanced use of multi-spectral infrared thermography for the monitoring of structures. This simultaneous estimation enables to have an estimate of the temperature by infrared thermography with a known uncertainty. Note de contenu : Introduction
1- Context and problem positioning
2- Bibliographical study
3- In-situ long-term thermal monitoring of structures: environmental measurements bias compensation
4- Study and development of an infrared multispectral images simulator
5- Proposed and studied methods for the simultaneous estimation of temperature and emissivity
Conclusion and future workNuméro de notice : 25705 Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématique et Sciences et Technologies de l’Information et de la Communication : Spécialité : Signal, Image, Vision : Signal, Image, Vision : 2019 En ligne : http://www.theses.fr/2019REN1S038 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94850
Titre : Robust hand pose recognition from stereoscopic capture Type de document : Thèse/HDR Auteurs : Rilwan Remilekun Basaru, Auteur Editeur : Londres : University of London Press Année de publication : 2018 Importance : 200 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Computer Science, City, University of LondonLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes descripteurs IGN] classification par forêts aléatoires
[Termes descripteurs IGN] estimation de pose
[Termes descripteurs IGN] image RVB
[Termes descripteurs IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes descripteurs IGN] régression
[Termes descripteurs IGN] réseau neuronal convolutifRésumé : (auteur) Hand pose is emerging as an important interface for human-computer interaction. The problem of hand pose estimation from passive stereo inputs has received less attention in the literature compared to active depth sensors. This thesis seeks to address this gap by presenting a data-driven method to estimate a hand pose from a stereoscopic camera input, with experimental results comparable to more expensive active depth sensors. The frameworks presented in this thesis are based on a two camera stereo rig capture as it yields a simpler and cheaper set-up and calibration. Three frameworks are presented, describing the sequential steps taken to solve the problem of depth and pose estimation of hands.
The first is a data-driven method to estimate a high quality depth map of a hand from a stereoscopic camera input by introducing a novel regression framework. The method first computes disparity using a robust stereo matching technique. Then, it applies a machine learning technique based on Random Forest to learn the mapping between the estimated disparity and depth given ground truth data. We introduce Eigen Leaf Node Features (ELNFs) that perform feature selection at the leaf nodes in each tree to identify features that are most discriminative for depth regression. The system provides a robust method for generating a depth image with an inexpensive stereo camera.
The second framework improves on the task of hand depth estimation from stereo capture by introducing a novel superpixel-based regression framework that takes advantage of the smoothness of the depth surface of the hand. To this end, it introduces Conditional Regressive Random Forest (CRRF), a method that combines a Conditional Random Field (CRF) and a Regressive Random Forest (RRF) to model the mapping from a stereo RGB image pair to a depth image. The RRF provides a unary term that adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. While the RRF makes depth prediction for each super-pixel independently, the CRF unifies the prediction of depth by modeling pair-wise interactions between adjacent superpixels.
The final framework introduces a stochastic approach to propose potential depth solutions to the observed stereo capture and evaluate these proposals using two convolutional neural networks (CNNs). The first CNN, configured in a Siamese network architecture, evaluates how consistent the proposed depth solution is to the observed stereo capture. The second CNN estimates a hand pose given the proposed depth. Unlike sequential approaches that reconstruct pose from a known depth, this method jointly optimizes the hand pose and depth estimation through Markov-chain Monte Carlo (MCMC) sampling. This way, pose estimation can correct for errors in depth estimation, and vice versa.
Experimental results using an inexpensive stereo camera show that the proposed system measures pose more accurately than competing methods. More importantly, it presents the possibility of pose recovery from stereo capture that is on par with depth based pose recovery.Numéro de notice : 17505 Thématique : IMAGERIE Nature : Thèse étrangère En ligne : https://openaccess.city.ac.uk/19938/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90396
Titre : Inverting Glacial Isostatic Adjustment signal using Bayesian framework and two linearly relaxing rheologies Type de document : Article/Communication Auteurs : Lambert Caron, Auteur ; Laurent Métivier Année de publication : 2017 Article en page(s) : pp 1126 - 1147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes descripteurs IGN] ajustement glacio-isostatique
[Termes descripteurs IGN] anomalie de pesanteur
[Termes descripteurs IGN] calotte glaciaire
[Termes descripteurs IGN] élasticité
[Termes descripteurs IGN] gravimétrie spatiale
[Termes descripteurs IGN] manteau terrestre
[Termes descripteurs IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes descripteurs IGN] rhéologieRésumé : (Auteur) Glacial Isostatic Adjustment (GIA) models commonly assume a mantle with a viscoelastic Maxwell rheology and a fixed ice history model. Here, we use a Bayesian Monte Carlo approach with a Markov chain formalism to invert the global GIA signal simultaneously for the mechanical properties of the mantle and the volumes of the ice sheets, using as starting ice models two previously published ice histories. Two stress relaxing rheologies are considered: Burgers and Maxwell linear viscoelasticities. A total of 5720 global palaeo sea level records are used, covering the last 35 kyr. Our goal is not only to seek the model best fitting this data set, but also to determine and display the range of possible solutions with their respective probability of explaining the data. In all cases, our a posteriori probability maps exhibit the classic character of solutions for GIA-determined mantle viscosity with two distinct peaks. What is new in our treatment is the presence of the bi-viscous Burgers rheology and the fact that we invert rheology jointly with ice history, in combination with the greatly expanded palaeo sea level records. The solutions tend to be characterized by an upper-mantle viscosity of around 5 × 1020 Pa s with one preferred lower-mantle viscosities at 3 × 1021 Pa s and the other more than 2 × 1022 Pa s, a rather classical pairing. Best-fitting models depend upon the starting ice history and the stress relaxing law. A first peak (P1) has the highest probability only in the case with a Maxwell rheology and ice history based on ICE-5G, while the second peak (P2) is favoured for ANU-based ice history or Burgers stress relaxation. The latter solution also may satisfy lower-mantle viscosity inferences from long-term geodynamics and gravity gradient anomalies over Laurentia. P2 is also consistent with large Laurentian and Fennoscandian ice-sheet volumes at the Last Glacial Maximum (LGM) and smaller LGM Antarctic ice volume than in either ICE-5G or ANU. Exploration of a bi-viscous linear relaxing rheology in GIA now seems logical due to a new set of requirements to satisfy observations of transient post-seismic flow seen so ubiquitously in space gravimetry and other global geodetic data. Numéro de notice : A2017-402 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/gji/ggx083 date de publication en ligne : 27/02/2017 En ligne : https://doi.org/10.1093/gji/ggx083 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86108
in Geophysical journal international > vol 209 n° 2 (May 2017) . - pp 1126 - 1147[article]PermalinkPermalinkPermalinkPermalinkPermalink
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