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Gasnier, Nicolas. "Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT002.
Pełny tekst źródłaSpaceborne remote sensing provides hydrologists and decision-makers with data that are essential for understanding the water cycle and managing the associated resources and risks. The SWOT satellite, which is a collaboration between the French (CNES) and American (NASA, JPL) space agencies, is scheduled for launch in 2022 and will measure the height of lakes, rivers, and oceans with high spatial resolution. It will complement existing sensors, such as the SAR and optical constellations Sentinel-1 and 2, and in situ measurements. SWOT represents a technological breakthrough as it is the first satellite to carry a near-nadir swath altimeter. The estimation of water levels is done by interferometry on the SAR images acquired by SWOT. Detecting water in these images is therefore an essential step in processing SWOT data, but it can be very difficult, especially with low signal-to-noise ratios, or in the presence of unusual radiometries. In this thesis, we seek to develop new methods to make water detection more robust. To this end, we focus on the use of exogenous data to guide detection, the combination of multi-temporal and multi-sensor data and denoising approaches. The first proposed method exploits information from the river database used by SWOT (derived from GRWL) to detect narrow rivers in the image in a way that is robust to both noise in the image, potential errors in the database, and temporal changes. This method relies on a new linear structure detector, a least-cost path algorithm, and a new Conditional Random Field segmentation method that combines data attachment and regularization terms adapted to the problem. We also proposed a method derived from GrabCut that uses an a priori polygon containing a lake to detect it on a SAR image or a time series of SAR images. Within this framework, we also studied the use of a multi-temporal and multi-sensor combination between Sentinel-1 SAR and Sentinel-2 optical images. Finally, as part of a preliminary study on denoising methods applied to water detection, we studied the statistical properties of the geometric temporal mean and proposed an adaptation of the variational method MuLoG to denoise it
Narasimha, Ramya. "Méthodes dʼestimation de la profondeur par mise en correspondance stéréoscopique à lʼaide de champs aléatoires couplés". Grenoble, 2010. http://www.theses.fr/2010GRENM053.
Pełny tekst źródłaThe depth of objects in 3-D scene can be recovered from a stereo image-pair by finding correspondences between the two views. This stereo matching task involves identifying the corresponding points in the left and the right images, which are the projections of the same scene point. The difference between the locations of the two corresponding points is the disparity, which is inversely related to the 3-D depth. In this thesis, we focus on Bayesian techniques that constrain the disparity estimates. In particular, these constraints involve explicit smoothness assumptions. However, there are further constraints that should be included, for example, the disparities should not be smoothed across object boundaries, the disparities should be consistent with geometric properties of the surface, and regions with similar colour should have similar disparities. The goal of this thesis is to incorporate such constraints using monocular cues and differential geometric information about the surface. To this end, this thesis considers two important problems associated with stereo matching; the first is localizing disparity discontinuities and second aims at recovering binocular disparities in accordance with the surface properties of the scene under consideration. We present a possible solution for each these problems. In order to deal with disparity discontinuities, we propose to cooperatively estimating disparities and object boundaries. This is motivated by the fact that the disparity discontinuities occur near object boundaries. The second one deals with recovering surface consistent disparities and surface normals by estimating the two simultaneously
Narasimha, Ramya. "Méthodes dʼestimation de la profondeur par mise en correspondance stéréoscopique à lʼaide de champs aléatoires couplés". Phd thesis, Grenoble, 2010. http://www.theses.fr/2010GRENM056.
Pełny tekst źródłaThe depth of objects in 3-D scene can be recovered from a stereo image-pair by finding correspondences between the two views. This stereo matching task involves identifying the corresponding points in the left and the right images, which are the projections of the same scene point. The difference between the locations of the two corresponding points is the disparity, which is inversely related to the 3-D depth. In this thesis, we focus on Bayesian techniques that constrain the disparity estimates. In particular, these constraints involve explicit smoothness assumptions. However, there are further constraints that should be included, for example, the disparities should not be smoothed across object boundaries, the disparities should be consistent with geometric properties of the surface, and regions with similar colour should have similar disparities. The goal of this thesis is to incorporate such constraints using monocular cues and differential geometric information about the surface. To this end, this thesis considers two important problems associated with stereo matching; the first is localizing disparity discontinuities and second aims at recovering binocular disparities in accordance with the surface properties of the scene under consideration. We present a possible solution for each these problems. In order to deal with disparity discontinuities, we propose to cooperatively estimating disparities and object boundaries. This is motivated by the fact that the disparity discontinuities occur near object boundaries. The second one deals with recovering surface consistent disparities and surface normals by estimating the two simultaneously
Besbes, Ahmed. "Image segmentation using MRFs and statistical shape modeling". Phd thesis, Ecole Centrale Paris, 2010. http://tel.archives-ouvertes.fr/tel-00594246.
Pełny tekst źródłaXiang, Bo. "Knowledge-based image segmentation using sparse shape priors and high-order MRFs". Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2013. http://www.theses.fr/2013ECAP0066/document.
Pełny tekst źródłaIn this thesis, we propose a novel framework for knowledge-based segmentation using high-order Markov Random Fields (MRFs). We represent the shape model as a point distribution graphical model which encodes pose invariant shape priors through L1 sparse higher order cliques. Each triplet clique encodes the local shape variation statistics on the angle measurements which inherit invariance to global transformations (i.e. translation,rotation and scale). A sparse higher-order graph structure is learned through MRF training using dual decomposition, producing boosting efficiency while preserving its ability to represent the shape variation.We incorporate the prior knowledge in a novel framework for model-based segmentation.We address the segmentation problem as a maximum a posteriori (MAP) estimation in a probabilistic framework. A global MRF energy function is defined to jointly combine regional statistics, boundary support as well as shape prior knowledge for estimating the optimal model parameters (i.e. the positions of the control points). The pose-invariant priors are encoded in second-order MRF potentials, while regional statistics acting on a derived image feature space can be exactly factorized using Divergence theorem. Furthermore, we propose a novel framework for joint model-pixel segmentation towardsa more refined segmentation when exact boundary delineation is of interest. Aunified model-based and pixel-driven integrated graphical model is developed to combine both top-down and bottom-up modules simultaneously. The consistency between the model and the image space is introduced by a model decomposition which associates the model parts with pixels labeling. Both of the considered higher-order MRFs are optimized efficiently using state-of the-art MRF optimization algorithms. Promising results on computer vision and medical image applications demonstrate the potential of the proposed segmentation methods
Fongang, Léon. "Aspects prédictifs des interactions tissus-implant par analyses multi-échelles en imagerie : mise en évidence intraleucocytaires de microtextures décrivant un champ aléatoire markovien". Nice, 1993. http://www.theses.fr/1993NICE4707.
Pełny tekst źródłaHuman bone and hematologic celle textures are evaluated by the way of fittes biomedical image and signal processsing operators. In the present work, we report a set of algorithmic procedures leading to : 1) a mathematical modeling « cell memory ». That model i related to homogeneity variations associated to the metabolic states which characterize the cell life inside « energetic bands ». The second order statistical means of blood cell microscopic textures agree with those resulting form the markovian random field. 2) A morphological and textural cell classification based on the concept of « field ray-vectorrs » (CRV). Inside that CRV, the micro- and macroscopic aspects of the cell specificities are taken into account fot the contour determination. An indexing method of « Ray Vectors » (RV) leads to a « fictive » (not physically) reorientation of objects, the specific algorithms of rotations and tranlations being excluded. Matched RV give a « shape-signal » (SF) in relation to each object and leads to quantify the degree of similarity between objects. 3) A new concept found upon the morphological multi-scale analysis allowing the quantification of periimplant texture homogeneity and, the predictive evaluations of bone tissue evolutions during the regeneration r after normal or abnormal restructuration which can be more or less altered
Lê-Huu, Dien Khuê. "Nonconvex Alternating Direction Optimization for Graphs : Inference and Learning". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC005/document.
Pełny tekst źródłaThis thesis presents our contributions toinference and learning of graph-based models in computervision. First, we propose a novel class of decompositionalgorithms for solving graph and hypergraphmatching based on the nonconvex alternating directionmethod of multipliers (ADMM). These algorithms arecomputationally efficient and highly parallelizable. Furthermore,they are also very general and can be appliedto arbitrary energy functions as well as arbitraryassignment constraints. Experiments show that theyoutperform existing state-of-the-art methods on popularbenchmarks. Second, we propose a nonconvex continuousrelaxation of maximum a posteriori (MAP) inferencein discrete Markov random fields (MRFs). Weshow that this relaxation is tight for arbitrary MRFs.This allows us to apply continuous optimization techniquesto solve the original discrete problem withoutloss in accuracy after rounding. We study two populargradient-based methods, and further propose a more effectivesolution using nonconvex ADMM. Experimentson different real-world problems demonstrate that theproposed ADMM compares favorably with state-of-theartalgorithms in different settings. Finally, we proposea method for learning the parameters of these graphbasedmodels from training data, based on nonconvexADMM. This method consists of viewing ADMM iterationsas a sequence of differentiable operations, whichallows efficient computation of the gradient of the trainingloss with respect to the model parameters, enablingefficient training using stochastic gradient descent. Atthe end we obtain a unified framework for inference andlearning with nonconvex ADMM. Thanks to its flexibility,this framework also allows training jointly endto-end a graph-based model with another model suchas a neural network, thus combining the strengths ofboth. We present experiments on a popular semanticsegmentation dataset, demonstrating the effectivenessof our method
Pereyra, Marcelo. "Statistical modeling and processing of high frequency ultrasound images : application to dermatologic oncology". Thesis, Toulouse, INPT, 2012. http://www.theses.fr/2012INPT0044/document.
Pełny tekst źródłaThis thesis studies statistical image processing of high frequency ultrasound imaging, with application to in-vivo exploration of human skin and noninvasive lesion assessment. More precisely, Bayesian methods are considered in order to perform tissue segmentation in ultrasound images of skin. It is established that ultrasound signals backscattered from skin tissues converge to a complex Levy Flight random process with non-Gaussian _-stable statistics. The envelope signal follows a generalized (heavy-tailed) Rayleigh distribution. Based on these results, it is proposed to model the distribution of multiple-tissue ultrasound images as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by a Potts Markov random field. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. The proposed method is successfully applied to the segmentation of in-vivo skin tumors in high frequency 2D and 3D ultrasound images. This method is subsequently extended by including the estimation of the Potts regularization parameter B within the Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because the likelihood of B is intractable. This difficulty is addressed by using a likelihood-free Metropolis-Hastings algorithm based on the sufficient statistic of the Potts model. The resulting unsupervised segmentation method is successfully applied to tridimensional ultrasound images. Finally, the problem of computing the Cramer-Rao bound (CRB) of B is studied. The CRB depends on the derivatives of the intractable normalizing constant of the Potts model. This is resolved by proposing an original Monte Carlo algorithm, which is successfully applied to compute the CRB of the Ising and Potts models
Kornaropoulos, Evgenios. "Enregistrement d'Image Déformable en Groupe pour l'Estimation de Mouvement en Imagerie Médicale en 4D". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLC042/document.
Pełny tekst źródłaThis doctoral thesis develops methods to estimate patient's motion, voluntary and involuntary (organs'motion), in order to correct for motion in spatiotemporal tomographic medical images. As an experimentalparadigm we consider the problem of motion estimation in Diffusion-Weighted Magnetic Resonance Imaging (DWI),an imaging modality sensitive to the diffusion of water molecules in the body. DWI is used for the evaluation oflymphoma patients, since water diffuses differently in healthy tissues and in lesions. The effect of water diffusioncan be better depicted through a parametric map, the so-called apparent diffusion coefficient (ADC map), createdbased on a series of DW images of the same patient (3D image sequence), acquired in time during scanning. Such aparametric map has the potentiality to become an imaging biomarker in DWI and provide physicians withcomplementary information to current state-of-the-art FDG-PET imaging reflecting quantitatively glycosemetaboslism.Our contributions are three fold. First, we propose a group-wise deformable image registration methodespecially designed for motion correction in DWI, as it is guided by a physiological model describing the diffusionprocess taking place during image acquisition. Our method derives an ADC map of higher accuracy in terms ofdepicting the gradient of the water molecules' diffusion in comparison to the corresponding map derived bycommon practice or by other model-free group-wise image registration methods. Second, we show that by imposingspatial constraints on the computation of the ADC map, the tumours in the image can be even better characterized interms of classifying them into the different types of the disease. Third, we show that a correlation between DWI andFDG-PET should exist by examining the correlation between statistical features extracted by the smooth ADC mapderived by our deformable registration method, and recommendation scores on the malignancy of the lesions, givenby experts based on an evaluation of the corresponding FDG-PET images of the patient
Le, bars Batiste. "Event detection and structure inference for graph vectors". Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASM003.
Pełny tekst źródłaThis thesis addresses different problems around the analysis and the modeling of graph signals i.e. vector data that are observed over graphs. In particular, we are interested in two tasks. The rst one is the problem of event detection, i.e. anomaly or changepoint detection, in a set of graph vectors. The second task concerns the inference of the graph structure underlying the observed graph vectors contained in a data set. At first, our work takes an application oriented aspect in which we propose a method for detecting antenna failures or breakdowns in a telecommunication network. The proposed approach is designed to be eective for communication networks in a broad sense and it implicitly takes into account the underlying graph structure of the data. In a second time, a new method for graph structure inference within the framework of Graph Signal Processing is investigated. In this problem, notions of both local and globalsmoothness, with respect to the underlying graph, are imposed to the vectors.Finally, we propose to combine the graph learning task with the change-point detection problem. This time, a probabilistic framework is considered to model the vectors, assumed to be distributed from a specifc Markov Random Field. In the considered modeling, the graph underlying the data is allowed to evolve in time and a change-point is actually detected whenever this graph changes significantly
El-Mawass, Nour. "Protection Anti-Abus de Réseaux Sociaux Numériques par Apprentissage Statistique". Thesis, Normandie, 2020. http://www.theses.fr/2020NORMR094.
Pełny tekst źródłaOver the last decade, the growing popularity of Online Social Networks has attracted a pervasive presence of social spammers. While this presence has started with spam advertising and common scams, the recent years have seen this escalate to the far more concerning mass manipulation attempts. This targeted and largely automated abuse of social platforms is risking the credibility and usefulness of the information disseminated on these platforms. The social spam detection problem has been traditionally modeled as a supervised problem where the goal is to classify individual social accounts. This common choice is problematic for two reasons. First, the dynamic and adversarial nature of social spam makes the performance achieved by features-based supervised systems hard to maintain. Second, features-based modeling of individual social accounts discards the collusive context in which social attacks are increasingly undertaken. Acting synchronously allows spammers to gain greater exposure and efficiently disseminate their content. Thus, even when spammers change their characteristics, they continue to act collusively, inevitably creating links between collusive spammingaccounts. This constitutes an unsupervised signal that is relatively easy to maintain and hard to evade. It is therefore beneficial to find a suitable similarity measure that captures this collusive behavior. Accordingly, we propose in this work to cast the social spam detection problem in probabilistic terms using the undirected graphical models framework. Instead of the individual detection paradigm that is commonly used in the literature, we aim to model the classi_cation task as one of joint inference. In this context, accounts are represented as random variables and the dependency between these variables is encoded in a graphical structure. This probabilistic setting allows to model theuncertainty that is inherent to classification systems while simultaneously leveraging the dependency that _ows from the similarity induced by the spammers collusive behavior. We propose two graphical models: the Markov Random Field with inference performed via Loopy Belief Propagation, and the Conditional Random Field with a setting that is more adapted to the classification problem, namely by adopting the Tree Reweighted message passing algorithm for inference and a loss that minimizes theempirical risk. Both models, evaluated on Twitter, demonstrate an increase in classification performance compared to state-of-the-art supervised classifiers. Compared to the Markov Random Field, the proposed Conditional Random Field framework offers a better classification performance and a higher robustness to changes in spammers input distribution
Forbes, Florence. "Modèles markoviens de ressources partagées". Phd thesis, Université Joseph Fourier (Grenoble), 1996. http://tel.archives-ouvertes.fr/tel-00004991.
Pełny tekst źródłaThai, Anh-Thi Marie Noémie. "Processus de Fleming-Viot, distributions quasi-stationnaires et marches aléatoires en interaction de type champ moyen". Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1124/document.
Pełny tekst źródłaIn this thesis we study the asymptotic behavior of particle systems in mean field type interaction in discrete space, where the system acts over one fixed particle through the empirical measure of the system. In the first part of this thesis, we are interested in Fleming-Viot particle systems: the particles move independently of each other until one of them reaches an absorbing state. At this time, the absorbed particle jumps instantly to the position of one of the other particles, chosen uniformly at random. The ergodicity of the process is established in the case of random walks on N with a dirft towards the origin and on complete graph dynamics. For the latter, we obtain a quantitative estimate of the convergence described by the Wasserstein curvature. Moreover, under the invariant measure, we show the convergence of the empirical measure towards the unique quasi-stationary distribution as the size of the system tends to infinity. In the second part of this thesis, we study the behavior in large time and when the number of particles is large of a system of birth and death processes where at each time a particle interacts with the others through the mean of theirs positions. We establish the existence of a macroscopic limit, solution of a non linear equation and the propagation of chaos phenomenon with quantitative and uniform in time estimate
Pereyra, Marcelo. "Modélisation et traitement statistiques d'images d'ultrasons de haute fréquence. Application à l'oncologie dermatologique". Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2012. http://tel.archives-ouvertes.fr/tel-00787930.
Pełny tekst źródłaMartin, Victorin. "Modélisation probabiliste et inférence par l'algorithme Belief Propagation". Phd thesis, Ecole Nationale Supérieure des Mines de Paris, 2013. http://tel.archives-ouvertes.fr/tel-00867693.
Pełny tekst źródłaDang, Van Mô. "Classification de donnees spatiales : modeles probabilistes et criteres de partitionnement". Compiègne, 1998. http://www.theses.fr/1998COMP1173.
Pełny tekst źródłaNguyen, Chu Duc. "Localization and quality enhancement for automatic recognition of vehicle license plates in video sequences". Thesis, Ecully, Ecole centrale de Lyon, 2011. http://www.theses.fr/2011ECDL0018.
Pełny tekst źródłaAutomatic reading of vehicle license plates is considered an approach to mass surveillance. It allows, through the detection / localization and optical recognition to identify a vehicle in the images or video sequences. Many applications such as traffic monitoring, detection of stolen vehicles, the toll or the management of entrance/ exit parking uses this method. Yet in spite of important progress made since the appearance of the first prototype sin 1979, with a recognition rate sometimes impressive thanks to advanced science and sensor technology, the constraints imposed for the operation of such systems limit laid. Indeed, the optimal use of techniques for localizing and recognizing license plates in operational scenarios requiring controlled lighting conditions and a limitation of the pose, velocity, or simply type plate. Automatic reading of vehicle license plates then remains an open research problem. The major contribution of this thesis is threefold. First, a new approach to robust license plate localization in images or image sequences is proposed. Then, improving the quality of the plates is treated with a localized adaptation of super-resolution technique. Finally, a unified model of location and super-resolution is proposed to reduce the time complexity of both approaches combined
Urs, Radu Dragos. "Non-parametric synthesis of volumetric textures from a 2D sample". Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00821886.
Pełny tekst źródłaTarabalka, Yuliya. "Approches spectro-spatiales pour la classification d'images hyperspectrales". Phd thesis, Grenoble INPG, 2010. http://www.theses.fr/2010INPG0049.
Pełny tekst źródłaLn this thesis, we propose and develop novel spectral-spatial methods and algorithms for classification of hyperspectral data. First, the integration of the Support Vector Machines technique within a Markov Random Fields framework is investigated. Ln. A second step, we propose classification methods using adaptive spatial neighborhoods derived from region segmentation results. Different segmentation techniques are investigated and extended to the case of hyperspectral images. We also propose approaches to reduce oversegmentation in an image, which is achieved by automatically marking the spatial structures before performing a marker-controlled segmentation. Our proposai is to analyze probabilistic classification results for selecting the most reliably classified pixels as markers of spatial regions. Then, different approaches for marker-controlled region growing are developed. The new techniques improve classification results when compared to previously proposed methods
Tarabalka, Yuliya. "Approches spectro-spatiales pour la classification d'images hyperspectrales". Phd thesis, Grenoble INPG, 2010. http://tel.archives-ouvertes.fr/tel-00557734.
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