Tesis sobre el tema "Mélange de gaussiennes photométriques"
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Guerbas, Seif Eddine. "Modélisation adaptée des images omnidirectionnelles pour agrandir le domaine de convergence de l'asservissement visuel virtuel direct". Electronic Thesis or Diss., Amiens, 2022. http://www.theses.fr/2022AMIE0026.
Texto completoOmnidirectional vision captures a scene in real-time in all directions with a wider field of view than a conventional camera. Within the environment, linking the visual features contained in the camera images to its movements is a central issue for visual servoing. Direct approaches, however, are characterized by a limited range of convergence. The main objective of this dissertation is to significantly extend the area of convergence in the context of virtual visual servoing by representing the omnidirectional image by a Photometric Gaussian Mixtures (PGM). This approach is further extended in the second step to the registration and direct tracking based on 3D models in omnidirectional images. This proposed methodology allows for studying the localization of a mobile robot equipped with a panoramic camera in a 3D urban model. The results show a significant enlargement of the convergence domain for high robustness to large interframe movements, as evidenced by experiments in virtual environments and with real images captured with a mobile robot and a vehicle
Habibi, Zaynab. "Vers l'assistance à l'exploration pertinente et réaliste d'environnements 3D très denses". Thesis, Amiens, 2015. http://www.theses.fr/2015AMIE0028/document.
Texto completoIn this thesis, we address the issue of navigation in virtual 3D environment. In particular, environments made of hundreds of millions of points, which are difficult to bring under control by a novice. The complexity and the wealth of details of the 3D point cloud of the cathedral of Amiens can result in a disorientation and in an irrelevant visualization with existing tools (interfaces). The contributions of the thesis deal with automatic or assisted camera control exploiting 2D visual information from the image and other 3D information from the environment. To ensure the visual relevance, we propose two methods to pilot the camera, one based on the photometric entropy and the second representing the major contribution of this thesis, defines and exploits the saliency-based Gaussian mixture. The visual servoing formalism is used to link the image modelling to the camera degrees of freedom. The obstacle avoidance, the fluidity of motion and appropriate camera orientation are considered as additional constraints taken into account in two navigation modes: the local framing and the global exploration. The goal of visual framing is to move the camera by maximizing the saliency-based Gaussian mixture feature, in order to reach a relevant viewpoint to visualize an object. We test this approach in synthetic model, 3D points cloud model and in a real environment with a robot. Regarding exploration, we present first an automatic camera control exploiting the photometric entropy and some constraints to ensure realistic motion. The problem is solved using an hybrid and hierarchical optimization algorithm. Then, we present a navigation aid system helping the user to explore a part or the whole 3D environment. The system is built using the redundancy formalism taking into account several constraints. These approaches were tested on simple and complex dense 3D points cloud
Darwich, Ali. "Approche pixel de la soustraction d'arrière-plan en vidéo, basée sur un mélange de gaussiennes imprécises". Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0479/document.
Texto completoMoving objects detection is a very important step for many applications such as human behavior analysis surveillance, model-based action recognition, road traffic monitoring, etc. Background subtraction is a popular approach, but difficult given that it must overcome many obstacles, such as dynamic background changes, brightness variations, occlusions, and so on. In the presented works, we focused on this problem of objects/background segmentation, using a type-2 fuzzy modeling to manage the inaccuracy of the model and the data. The proposed method models the state of each pixel using an imprecise and scalable Gaussian mixture model, which is exploited by several fuzzy classifiers to ultimately estimate the pixel class at each image. More precisely, this decision takes into account the history of its evolution, but also its spatial neighborhood and its possible displacements in the preceding images. Then we compared the proposed method with other close methods, including methods based on a gaussian mixture model, fuzzy based methods, or ACP type methods. This comparison allowed us to assess its good performances, and to propose some perspectives to this work
Genin, Laure. "Détection d'objets de petite taille sur des séquences aériennes ou satellitaires". Paris 13, 2013. http://scbd-sto.univ-paris13.fr/secure/edgalilee_th_2013_genin.pdf.
Texto completoThe objective of this thesis is to improve the detection of point objects in optical imaging. They focus on the challenging detection of low velocity point objects on inhomogeneous background including areas of strong gradients of gray levels. In this context, we propose single-frame detection methods trying to take advantage at best of the spatial background correlation. Spatio-temporal extensions of the proposed methods are studied in a second time. Based on a formalism of the generalized likelihood ratio test (GLRT), the problem of detection boils down to a two-step process which consists in separating the first and second order estimation of the local background (i. E. Mean and covariance). To improve the performances of the detection methods by first order background modelling, we adapt patch-based denoising method to detection. Despite the improvement of detection performance brought by these patch-based methods, it appears that textures associated with background structures are still visible after the background suppression step. We seek to improve the detection performance by second order modeling. We are interested in matched filter adapted by area based on a Gaussian mixture model. A detailed performance analysis of the developed filters is made from real cloudy background on which point targets are embedded
Zaïdi, Abdelhamid. "Séparation aveugle d'un mélange instantané de sources autorégressives gaussiennes par la méthode du maximum de vraissemblance exact". Université Joseph Fourier (Grenoble), 2000. http://www.theses.fr/2000GRE10233.
Texto completoJarraya, Siala Aida. "Nouvelles paramétrisations de réseaux bayésiens et leur estimation implicite : famille exponentielle naturelle et mélange infini de Gaussiennes". Phd thesis, Nantes, 2013. https://archive.bu.univ-nantes.fr/pollux/show/show?id=aef89743-c009-457d-8c27-a888655a4e58.
Texto completoLearning a Bayesian network consists in estimating the graph (structure) and the parameters of conditional probability distributions associated with this graph. Bayesian networks learning algorithms rely on classical Bayesian estimation approach whose a priori parameters are often determined by an expert or defined uniformly The core of this work concerns the application of several advances in the field of statistics as implicit estimation, Natural exponential families or infinite mixtures of Gaussian in order to (1) provide new parametric forms for Bayesian networks, (2) estimate the parameters of such models and (3) learn their structure
Jarraya, Siala Aida. "Nouvelles paramétrisations de réseaux Bayésiens et leur estimation implicite - Famille exponentielle naturelle et mélange infini de Gaussiennes". Phd thesis, Université de Nantes, 2013. http://tel.archives-ouvertes.fr/tel-00932447.
Texto completoDumitru, Corneliu Octavian. "Noise sources in robust uncompressed video watermarking". Phd thesis, Institut National des Télécommunications, 2010. http://tel.archives-ouvertes.fr/tel-00541755.
Texto completoDevinck, Vincent. "Systèmes dynamiques linéaires : vitesse de mélange et spectre ponctuel unimodulaire". Thesis, Lille 1, 2012. http://www.theses.fr/2012LIL10006/document.
Texto completoIn this thesis, we study into two different parts the eigenvectors associated to unimodular eigenvalues of an operator on a separable Banach space. The first part of the thesis follows a work of F. Bayart and S. Grivaux where they give condition on the eigenvectors associated to unimodular eigenvalues of an operator on a complex separable Hilbert space to admit a Gaussian measure for which the operator defines a strongly mixing transformation. With this condition on the eigenvectors, we investigate the subject of speed of mixing of the strongly mixing operator. We prove that there is no way to obtain a uniform speed of mixing in general. Then we prove that if the eigenvectors associated to unimodular eigenvalues of the operator are parametrized by a countable family of regular eigenvector fields then we have a speed of mixing by considering regular classes of functions. In the second part of the thesis, we study the unimodular point spectrum of an operator on a separable Banach space. By using the results on Jamison sequences, we give a characterization of Jamison sequences for strongly continuous semigroups. We are also concerned in the problem of construction of Banach space and operator on this space when the sequences are not Jamison sequences. Then we generalize the notion of Jamison sequence by studying the unimodular point spectrum of a group representation which is bounded with respect to some sequence of this group. In particular, we characterize Jamison sequences of a finitely generated abelian group
Fernandes, maligo Artur otavio. "Unsupervised Gaussian mixture models for the classification of outdoor environments using 3D terrestrial lidar data". Thesis, Toulouse, INSA, 2016. http://www.theses.fr/2016ISAT0053/document.
Texto completoThe processing of 3D lidar point clouds enable terrestrial autonomous mobile robots to build semantic models of the outdoor environments in which they operate. Such models are interesting because they encode qualitative information, and thus provide to a robot the ability to reason at a higher level of abstraction. At the core of a semantic modelling system, lies the capacity to classify the sensor observations. We propose a two-layer classi- fication model which strongly relies on unsupervised learning. The first, intermediary layer consists of a Gaussian mixture model. This model is determined in a training step in an unsupervised manner, and defines a set of intermediary classes which is a fine-partitioned representation of the environment. The second, final layer consists of a grouping of the intermediary classes into final classes that are interpretable in a considered target task. This grouping is determined by an expert during the training step, in a process which is supervised, yet guided by the intermediary classes. The evaluation is done for two datasets acquired with different lidars and possessing different characteristics. It is done quantitatively using one of the datasets, and qualitatively using another. The system is designed following the standard learning procedure, based on a training, a validation and a test steps. The operation follows a standard classification pipeline. The system is simple, with no requirement of pre-processing or post-processing stages
Viandier, Nicolas. "Modélisation et utilisation des erreurs de pseudodistances GNSS en environnement transport pour l'amélioration des performances de localisation". Phd thesis, Ecole Centrale de Lille, 2011. http://tel.archives-ouvertes.fr/tel-00664264.
Texto completoHabibi, Zaynab. "Vers l'assistance à l'exploration pertinente et réaliste d'environnements 3D très denses". Electronic Thesis or Diss., Amiens, 2015. http://www.theses.fr/2015AMIE0028.
Texto completoIn this thesis, we address the issue of navigation in virtual 3D environment. In particular, environments made of hundreds of millions of points, which are difficult to bring under control by a novice. The complexity and the wealth of details of the 3D point cloud of the cathedral of Amiens can result in a disorientation and in an irrelevant visualization with existing tools (interfaces). The contributions of the thesis deal with automatic or assisted camera control exploiting 2D visual information from the image and other 3D information from the environment. To ensure the visual relevance, we propose two methods to pilot the camera, one based on the photometric entropy and the second representing the major contribution of this thesis, defines and exploits the saliency-based Gaussian mixture. The visual servoing formalism is used to link the image modelling to the camera degrees of freedom. The obstacle avoidance, the fluidity of motion and appropriate camera orientation are considered as additional constraints taken into account in two navigation modes: the local framing and the global exploration. The goal of visual framing is to move the camera by maximizing the saliency-based Gaussian mixture feature, in order to reach a relevant viewpoint to visualize an object. We test this approach in synthetic model, 3D points cloud model and in a real environment with a robot. Regarding exploration, we present first an automatic camera control exploiting the photometric entropy and some constraints to ensure realistic motion. The problem is solved using an hybrid and hierarchical optimization algorithm. Then, we present a navigation aid system helping the user to explore a part or the whole 3D environment. The system is built using the redundancy formalism taking into account several constraints. These approaches were tested on simple and complex dense 3D points cloud
Kamary, Kaniav. "Lois a priori non-informatives et la modélisation par mélange". Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED022/document.
Texto completoOne of the major applications of statistics is the validation and comparing probabilistic models given the data. This branch statistics has been developed since the formalization of the late 19th century by pioneers like Gosset, Pearson and Fisher. In the special case of the Bayesian approach, the comparison solution of models is the Bayes factor, ratio of marginal likelihoods, whatever the estimated model. This solution is obtained by a mathematical reasoning based on a loss function. Despite a frequent use of Bayes factor and its equivalent, the posterior probability of models, by the Bayesian community, it is however problematic in some cases. First, this rule is highly dependent on the prior modeling even with large datasets and as the selection of a prior density has a vital role in Bayesian statistics, one of difficulties with the traditional handling of Bayesian tests is a discontinuity in the use of improper priors since they are not justified in most testing situations. The first part of this thesis deals with a general review on non-informative priors, their features and demonstrating the overall stability of posterior distributions by reassessing examples of [Seaman III 2012].Beside that, Bayes factors are difficult to calculate except in the simplest cases (conjugate distributions). A branch of computational statistics has therefore emerged to resolve this problem with solutions borrowing from statistical physics as the path sampling method of [Gelman 1998] and from signal processing. The existing solutions are not, however, universal and a reassessment of the methods followed by alternative methods is a part of the thesis. We therefore consider a novel paradigm for Bayesian testing of hypotheses and Bayesian model comparison. The idea is to define an alternative to the traditional construction of posterior probabilities that a given hypothesis is true or that the data originates from a specific model which is based on considering the models under comparison as components of a mixture model. By replacing the original testing problem with an estimation version that focus on the probability weight of a given model within a mixture model, we analyze the sensitivity on the resulting posterior distribution of the weights for various prior modelings on the weights and stress that a major appeal in using this novel perspective is that generic improper priors are acceptable, while not putting convergence in jeopardy. MCMC methods like Metropolis-Hastings algorithm and the Gibbs sampler are used. From a computational viewpoint, another feature of this easily implemented alternative to the classical Bayesian solution is that the speeds of convergence of the posterior mean of the weight and of the corresponding posterior probability are quite similar.In the last part of the thesis we construct a reference Bayesian analysis of mixtures of Gaussian distributions by creating a new parameterization centered on the mean and variance of those models itself. This enables us to develop a genuine non-informative prior for Gaussian mixtures with an arbitrary number of components. We demonstrate that the posterior distribution associated with this prior is almost surely proper and provide MCMC implementations that exhibit the expected component exchangeability. The analyses are based on MCMC methods as the Metropolis-within-Gibbs algorithm, adaptive MCMC and the Parallel tempering algorithm. This part of the thesis is followed by the description of R package named Ultimixt which implements a generic reference Bayesian analysis of unidimensional mixtures of Gaussian distributions obtained by a location-scale parameterization of the model. This package can be applied to produce a Bayesian analysis of Gaussian mixtures with an arbitrary number of components, with no need to specify the prior distribution
Sebbar, Mehdi. "On unsupervised learning in high dimension". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLG003/document.
Texto completoIn this thesis, we discuss two topics, high-dimensional clustering on the one hand and estimation of mixing densities on the other. The first chapter is an introduction to clustering. We present various popular methods and we focus on one of the main models of our work which is the mixture of Gaussians. We also discuss the problems with high-dimensional estimation (Section 1.3) and the difficulty of estimating the number of clusters (Section 1.1.4). In what follows, we present briefly the concepts discussed in this manuscript. Consider a mixture of $K$ Gaussians in $RR^p$. One of the common approaches to estimate the parameters is to use the maximum likelihood estimator. Since this problem is not convex, we can not guarantee the convergence of classical methods such as gradient descent or Newton's algorithm. However, by exploiting the biconvexity of the negative log-likelihood, the iterative 'Expectation-Maximization' (EM) procedure described in Section 1.2.1 can be used. Unfortunately, this method is not well suited to meet the challenges posed by the high dimension. In addition, it is necessary to know the number of clusters in order to use it. Chapter 2 presents three methods that we have developed to try to solve the problems described above. The works presented there have not been thoroughly researched for various reasons. The first method that could be called 'graphical lasso on Gaussian mixtures' consists in estimating the inverse matrices of covariance matrices $Sigma$ (Section 2.1) in the hypothesis that they are parsimonious. We adapt the graphic lasso method of [Friedman et al., 2007] to a component in the case of a mixture and experimentally evaluate this method. The other two methods address the problem of estimating the number of clusters in the mixture. The first is a penalized estimate of the matrix of posterior probabilities $ Tau in RR ^ {n times K} $ whose component $ (i, j) $ is the probability that the $i$-th observation is in the $j$-th cluster. Unfortunately, this method proved to be too expensive in complexity (Section 2.2.1). Finally, the second method considered is to penalize the weight vector $ pi $ in order to make it parsimonious. This method shows promising results (Section 2.2.2). In Chapter 3, we study the maximum likelihood estimator of density of $n$ i.i.d observations, under the assumption that it is well approximated by a mixture with a large number of components. The main focus is on statistical properties with respect to the Kullback-Leibler loss. We establish risk bounds taking the form of sharp oracle inequalities both in deviation and in expectation. A simple consequence of these bounds is that the maximum likelihood estimator attains the optimal rate $((log K)/n)^{1/2}$, up to a possible logarithmic correction, in the problem of convex aggregation when the number $K$ of components is larger than $n^{1/2}$. More importantly, under the additional assumption that the Gram matrix of the components satisfies the compatibility condition, the obtained oracle inequalities yield the optimal rate in the sparsity scenario. That is, if the weight vector is (nearly) $D$-sparse, we get the rate $(Dlog K)/n$. As a natural complement to our oracle inequalities, we introduce the notion of nearly-$D$-sparse aggregation and establish matching lower bounds for this type of aggregation. Finally, in Chapter 4, we propose an algorithm that performs the Kullback-Leibler aggregation of components of a dictionary as discussed in Chapter 3. We compare its performance with different methods: the kernel density estimator , the 'Adaptive Danzig' estimator, the SPADES and EM estimator with the BIC criterion. We then propose a method to build the dictionary of densities and study it numerically. This thesis was carried out within the framework of a CIFRE agreement with the company ARTEFACT
Dumitru, Corneliu Octavian. "Noise sources in robust uncompressed video watermarking". Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2010. http://www.theses.fr/2010TELE0001.
Texto completoThe thesis is focus on natural video and attack modelling for uncompressed video watermarking purposes. By reconsidering a statistical investigation combining four types of statistical tests, the thesis starts by identifying with accuracy the drawbacks and limitations of the popular Gaussian model in watermarking applications. Further on, an advanced statistical approach is developed in order to establish with mathematical rigour: 1. That a mathematical model for the original video content and/or attacks exists; 2. The model parameters. From the theoretical point of view, this means to prove for the first time the stationarity of the random processes representing the natural video and/or the watermarking attacks. These general results have been already validated under applicative and theoretical frameworks. On the one hand, when integrating the attack models into the IProtect watermarking method patented by Institut Télécom/ARTEMIS and SFR, a speed-up by a factor of 100 of the insertion procedure has been obtained. On the other hand, accurate models for natural video and attacks allowed the increasing of the precision in the computation of some basic information theory entities (entropies and capacity)
Viandier, Nicolas. "Modélisation et utilisation des erreurs de pseudodistances GNSS en environnement transport pour l’amélioration des performances de localisation". Thesis, Ecole centrale de Lille, 2011. http://www.theses.fr/2011ECLI0006/document.
Texto completoToday, the GNSS are largely present in the transport field. Currently, the scientific community aims to develop transport applications with a high accuracy, availability and integrity. These systems offer a continuous positioning service. Performances are defined by the system parameters but also by signal environment propagation. The atmosphere propagation characteristics are well known. However, it is more difficult to anticipate and analyze the impact of the propagation environment close to the antenna which can be composed, for instance, of urban obstacles or vegetation.Since several years, the LEOST and the LAGIS research axes are driven by the understanding of the propagation environment and its use as supplementary information to help the GNSS receiver to be more pertinent. This approach aims to reduce the number of sensors in the localisation system, and consequently reduces its complexity and cost. The work performed in this thesis is devoted to provide more realistic pseudorange error models and reception channel model. After, a step of observation error characterization, several pseudorange error models have been proposed. These models are the finite gaussian mixture model and the Dirichlet process mixture. The model parameters are then estimated jointly with the state vector containing position by using adapted filtering solution like the Rao-Blackwellized particle filter. The noise model evolution allows adapting to an urban environment and consequently providing a position more accurate.Each step of this work has been tested and evaluated on simulation data and real data
Sebbar, Mehdi. "On unsupervised learning in high dimension". Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLG003.
Texto completoIn this thesis, we discuss two topics, high-dimensional clustering on the one hand and estimation of mixing densities on the other. The first chapter is an introduction to clustering. We present various popular methods and we focus on one of the main models of our work which is the mixture of Gaussians. We also discuss the problems with high-dimensional estimation (Section 1.3) and the difficulty of estimating the number of clusters (Section 1.1.4). In what follows, we present briefly the concepts discussed in this manuscript. Consider a mixture of K Gaussians in ℝ^p. One of the common approaches to estimate the parameters is to use the maximum likelihood estimator. Since this problem is not convex, we can not guarantee the convergence of classical methods such as gradient descent or Newton's algorithm. However, by exploiting the biconvexity of the negative log-likelihood, the iterative 'Expectation-Maximization' (EM) procedure described in Section 1.2.1 can be used. Unfortunately, this method is not well suited to meet the challenges posed by the high dimension. In addition, it is necessary to know the number of clusters in order to use it. Chapter 2 presents three methods that we have developed to try to solve the problems described above. The works presented there have not been thoroughly researched for various reasons. The first method that could be called 'graphical lasso on Gaussian mixtures' consists in estimating the inverse matrices of covariance matrices Σ (Section 2.1) in the hypothesis that they are parsimonious. We adapt the graphic lasso method of [Friedman et al., 2007] to a component in the case of a mixture and experimentally evaluate this method. The other two methods address the problem of estimating the number of clusters in the mixture. The first is a penalized estimate of the matrix of posterior probabilities T∈ℝ^{n x K} whose component (i, j) is the probability that the i-th observation is in the j-th cluster. Unfortunately, this method proved to be too expensive in complexity (Section 2.2.1). Finally, the second method considered is to penalize the weight vector π in order to make it parsimonious. This method shows promising results (Section 2.2.2). In Chapter 3, we study the maximum likelihood estimator of density of n i.i.d observations, under the assumption that it is well approximated by a mixture with a large number of components. The main focus is on statistical properties with respect to the Kullback-Leibler loss. We establish risk bounds taking the form of sharp oracle inequalities both in deviation and in expectation. A simple consequence of these bounds is that the maximum likelihood estimator attains the optimal rate ((log K)/n)^{1/2}, up to a possible logarithmic correction, in the problem of convex aggregation when the number K of components is larger than n^{1/2}. More importantly, under the additional assumption that the Gram matrix of the components satisfies the compatibility condition, the obtained oracle inequalities yield the optimal rate in the sparsity scenario. That is, if the weight vector is (nearly) D-sparse, we get the rate (Dlog K)/n. As a natural complement to our oracle inequalities, we introduce the notion of nearly-D-sparse aggregation and establish matching lower bounds for this type of aggregation. Finally, in Chapter 4, we propose an algorithm that performs the Kullback-Leibler aggregation of components of a dictionary as discussed in Chapter 3. We compare its performance with different methods: the kernel density estimator , the 'Adaptive Danzig' estimator, the SPADES and EM estimator with the BIC criterion. We then propose a method to build the dictionary of densities and study it numerically. This thesis was carried out within the framework of a CIFRE agreement with the company ARTEFACT
Othman, Nadia. "Fusion techniques for iris recognition in degraded sequences". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLL003/document.
Texto completoAmong the large number of biometric modalities, iris is considered as a very reliable biometrics with a remarkably low error rate. The excellent performance of iris recognition systems are obtained by controlling the quality of the captured images and by imposing certain constraints on users, such as standing at a close fixed distance from the camera. However, in many real-world applications such as control access and airport boarding these constraints are no longer suitable. In such non ideal conditions, the resulting iris images suffer from diverse degradations which have a negative impact on the recognition rate. One way to try to circumvent this bad situation is to use some redundancy arising from the availability of several images of the same eye in the recorded sequence. Therefore, this thesis focuses on how to fuse the information available in the sequence in order to improve the performance. In the literature, diverse schemes of fusion have been proposed. However, they agree on the fact that the quality of the used images in the fusion process is an important factor for its success in increasing the recognition rate. Therefore, researchers concentrated their efforts in the estimation of image quality to weight each image in the fusion process according to its quality. There are various iris quality factors to be considered and diverse methods have been proposed for quantifying these criteria. These quality measures are generally combined to one unique value: a global quality. However, there is no universal combination scheme to do so and some a priori knowledge has to be inserted, which is not a trivial task. To deal with these drawbacks, in this thesis we propose of a novel way of measuring and integrating quality measures in a super-resolution approach, aiming at improving the performance. This strategy can handle two types of issues for iris recognition: the lack of resolution and the presence of various artifacts in the captured iris images. The first part of the doctoral work consists in elaborating a relevant quality metric able to quantify locally the quality of the iris images. Our measure relies on a Gaussian Mixture Model estimation of clean iris texture distribution. The interest of our quality measure is 1) its simplicity, 2) its computation does not require identifying in advance the type of degradations that can occur in the iris image, 3) its uniqueness, avoiding thus the computation of several quality metrics and associated combination rule and 4) its ability to measure the intrinsic quality and to specially detect segmentation errors. In the second part of the thesis, we propose two novel quality-based fusion schemes. Firstly, we suggest using our quality metric as a global measure in the fusion process in two ways: as a selection tool for detecting the best images and as a weighting factor at the pixel-level in the super-resolution scheme. In the last case, the contribution of each image of the sequence in final fused image will only depend on its overall quality. Secondly, taking advantage of the localness of our quality measure, we propose an original fusion scheme based on a local weighting at the pixel-level, allowing us to take into account the fact that degradations can be different in diverse parts of the iris image. This means that regions free from occlusions will contribute more in the image reconstruction than regions with artefacts. Thus, the quality of the fused image will be optimized in order to improve the performance. The effectiveness of the proposed approaches is shown on several databases commonly used: MBGC, Casia-Iris-Thousand and QFIRE at three different distances: 5, 7 and 11 feet. We separately investigate the improvement brought by the super-resolution, the global quality and the local quality in the fusion process. In particular, the results show the important improvement brought by the use of the global quality, improvement that is even increased using the local quality
Ota, Kenko. "Traitement du signal pour la reconnaissance de la parole robuste dans des environnements bruités et réverbérants". Phd thesis, Ecole Centrale de Lille, 2008. http://tel.archives-ouvertes.fr/tel-00260343.
Texto completoJourani, Reda. "Reconnaissance automatique du locuteur par des GMM à grande marge". Phd thesis, Université Paul Sabatier - Toulouse III, 2012. http://tel.archives-ouvertes.fr/tel-00807563.
Texto completoPoulard, Hervé. "Statistiques et réseaux de neurones pour un système de diagnostic : application au diagnostic de pannes automobiles". Phd thesis, Université Paul Sabatier - Toulouse III, 1996. http://tel.archives-ouvertes.fr/tel-00459051.
Texto completoTran, Viet Anh. "Silent communication : whispered speech-to-clear speech conversion". Grenoble INPG, 2010. http://www.theses.fr/2010INPG0006.
Texto completoIn recent years, advances in wireless communication technology have led to the widespread use of cellular phones. Because of noisy environmental conditions and competing surrounding conversations, users tend to speak loudly. As a consequence, private policies and public legislation tend to restrain the use of cellular phone in public places. Silent speech which can only be heard by a limited set of listeners close to the speaker is an attractive solution to this problem if it can effectively be used for quiet and private communication. The motivation of this research thesis was to investigate ways of improving the naturalness and the intelligibility of synthetic speech obtained from the conversion of silent or whispered speech. A Non-audible murmur (NAM) condenser microphone, together with signal-based Gaussian Mixture Model (GMM) mapping, were chosen because promising results were already obtained with this sensor and this approach, and because the size of the NAM sensor is well adapted to mobile communication technology. Several improvements to the speech conversion obtained with this sensor were considered. A first set of improvement concerns characteristics of the voiced source. One of the features missing in whispered or silent speech with respect to loud or modal speech is F0, which is crucial in conveying linguistic (question vs. Statement, syntactic grouping, etc. ) as well as paralinguistic (attitudes, emotions) information. The proposed estimation of voicing and F0 for converted speech by separate predictors improves both predictions. The naturalness of the converted speech was then further improved by extending the context window of the input feature from phoneme size to syllable size and using a Linear Discriminant Analysis (LDA) instead of a Principal Component Analysis (PCA) for the dimension reduction of input feature vector. The objective positive influence of this new approach of the quality of the output converted speech was confirmed by perceptual tests. Another approach investigated in this thesis consisted in integrating visual information as a complement to the acoustic information in both input and output data. Lip movements which significantly contribute to the intelligibility of visual speech in face-to-face human interaction were explored by using an accurate lip motion capture system from 3D positions of coloured beads glued on the speaker's face. The visual parameters are represented by 5 components related to the rotation of the jaw, to lip rounding, upper and lower lip vertical movements and movements of the throat which is associated with the underlying movements of the larynx and hyoid bone. Including these visual features in the input data significantly improved the quality of the output converted speech, in terms of F0 and spectral features. In addition, the audio output was replaced by an audio-visual output. Subjective perceptual tests confirmed that the investigation of the visual modality in either the input or output data or both, improves the intelligibility of the whispered speech conversion. Both of these improvements are confirmed by subjective tests. Finally, we investigated the technique using a phonetic pivot by combining Hidden Markov Model (HMM)-based speech recognition and HMM-based speech synthesis techniques to convert whispered speech data to audible one in order to compare the performance of the two state-of-the-art approaches. Audiovisual features were used in the input data and audiovisual speech was produced as an output. The objective performance of the HMM-based system was inferior to the direct signal-to-signal system based on a GMM. A few interpretations of this result were proposed together with future lines of research
Ozerov, Alexey. "Adaptation de modèles statistiques pour la séparation de sources mono-capteur : application à la séparation voix / musique dans les chansons". Phd thesis, Rennes 1, 2006. https://tel.archives-ouvertes.fr/tel-00564866.
Texto completoDiop, Cheikh Abdoulahat. "La structure multimodale de la distribution de probabilité de la réflectivité radar des précipitations". Toulouse 3, 2012. http://thesesups.ups-tlse.fr/3089/.
Texto completoA set of radar data gathered over various sites of the US Nexrad (Next Generation Weather Radar) S band radar network is used to analyse the probability distribution function (pdf) of the radar reflectivity factor (Z) of precipitation, P(Z). Various storm types are studied and a comparison between them is made: 1) hailstorms at the continental site of Little Rock (Arkansas), 2) peninsular and coastal convection at Miami (Florida), 3) coastal convection and land/sea transition at Brownsville (Texas), 4) tropical maritime convection at Hawaii, 5) midlatitude maritime convection at Eureka (California), 6) snowstorms from winter frontal continental systems at New York City (New York), and 7) high latitude maritime snowstorms at Middleton Island (Alaska). Each storm type has a specific P(Z) signature with a complex shape. It is shown that P(Z) is a mixture of Gaussian components, each of them being attribuable to a precipitation type. Using the EM (Expectation Maximisation) algorithm of Dempster et al. 1977, based on the maximum likelihood method, four main components are categorized in hailstorms: 1) cloud and precipitation of very low intensity or drizzle, 2) stratiform precipitation, 3) convective precipitation, and 4) hail. Each component is described by the fraction of area occupied inside P(Z) and by the two Gaussian parameters, mean and variance. The absence of hail component in maritime and coastal storms is highlighted. For snowstorms, P(Z) has a more regular shape. The presence of several components in P(Z) is linked to some differences in the dynamics and microphysics of each precipitation type. The retrieval of the mixed distribution by a linear combination of the Gaussian components gives a very stisfactory P(Z) fitting. An application of the results of the split-up of P(Z) is then presented. Cloud, rain, and hail components have been isolated and each corresponding P(Z) is converted into a probability distribution of rain rate P(R) which parameters are µR and sR2 , respectively mean and variance. It is shown on the graph (µR ,sR2) that each precipitation type occupies a specific area. This suggests that the identified components are distinct. For example, the location of snowstorms representative points indicates that snow is statistically different from rain. The P(R) variation coefficient, CVR = sR/µR is constant for each precipitation type. This result implies that knowing CVR and measuring only one of the P(R) parameters enable to determine the other one and to define the rain rate probability distribution. The influence of the coefficients a and b of the relation Z = aRb on P(R) is also discussed
Ménétré, Sarah. "Analyse de signaux d'arrêts cardiaques en cas d'intervention d'urgence avec défibrillateur automatisé : optimisation des temps de pause péri-choc et prédiction d'efficacité de défibrillation". Thesis, Nancy 1, 2011. http://www.theses.fr/2011NAN10120/document.
Texto completoThe cardiac arrest is mainly of cardiovascular etiology. In the actual context of out-of-hospital cardiac arrests, 20 to 25% of the victims present a ventricular fibrillation. About 3 to 5% of the victims are saved without neurological damage. The chance of surviving a cardiac arrest outside an hospital depends on the early and fast support of the victim. The first active witnesses performing cardiopulmonary resuscitation combined with the use of a defibrillator are an important link to save the victim.Our main objective is to improve survival rate in out-of-hospital cardiac arrest cases. A first way of investigation is to propose an optimal functioning of defibrillator combining wisely the different processes of detection embedded (ventricular fibrillation detection, chest compressions detection, electromagnetic interferences detection), in order to reduce the peri-shock pauses during the resuscitation procedure. In fact, during these pauses, known as "hands-off" pauses, no emergency action is provided to the patient, what is correlated to a drop of the coronary pression, but also to a decrease of the chance of successful defibrillation. That is the reason why, a second way of investigation is based on the prediction of the efficiency of defibrillation. In this context, we propose to use a combination of parameters extracted from electrocardiogram in time, frequency and non-linear dynamics domains. A bayesian classifier using a gaussian mixture model was applied to the vectors of parameters, which are the most predictor of the defibrillation outcome and the algorithm Expectation-Maximization allowed to learn the parameters of the probabilistic model representing the class conditional distributions.All of the proposed methods allowed to reach promising results for both reducing the peri-shock pauses and predicting the efficiency of defibrillation in hope to improve the survival rate in cardiac arrest cases
Hueber, Thomas. "Reconstitution de la parole par imagerie ultrasonore et vidéo de l'appareil vocal : vers une communication parlée silencieuse". Phd thesis, Université Pierre et Marie Curie - Paris VI, 2009. http://pastel.archives-ouvertes.fr/pastel-00005707.
Texto completoDurrieu, Jean-Louis. "Transcription et séparation automatique de la mélodie principale dans les signaux de musique polyphoniques". Phd thesis, Paris, Télécom ParisTech, 2010. https://pastel.hal.science/pastel-00006123.
Texto completoWe propose to address the problem of melody extraction along with the monaural lead instrument and accompaniment separation problem. The first task is related to Music Information Retrieval (MIR), since it aims at indexing the audio music signals with their melody. The separation problem is related to Blind Audio Source Separation (BASS), as it aims at breaking an audio mixture into several source tracks. Leading instrument source separation and main melody extraction are addressed within a unified framework. The lead instrument is modelled thanks to a source/filter production model. Its signal is generated by two hidden states, the filter state and the source state. The proposed signal spectral model therefore explicitly uses pitches both to separate the lead instrument from the others and to transcribe the pitch sequence played by that instrument, the "main melody". This model gives rise to two alternative models, a Gaussian Scaled Mixture Model (GSMM) and the Instantaneous Mixture Model (IMM). The accompaniment is modelled with a more general spectral model. Five systems are proposed. Three systems detect the fundamental frequency sequence of the lead instrument, i. E. They estimate the main melody. A system returns a musical melody transcription and the last system separates the lead instrument from the accompaniment. The results in melody transcription and source separation are at the state of the art, as shown by our participations to international evaluation campaigns (MIREX'08, MIREX'09 and SiSEC'08). The proposed extension of previous source separation works using "MIR" knowledge is therefore a very successful combination
Durrieu, Jean-Louis. "Transcription et séparation automatique de la mélodie principale dans les signaux de musique polyphoniques". Phd thesis, Télécom ParisTech, 2010. http://pastel.archives-ouvertes.fr/pastel-00006123.
Texto completoJourani, Reda. "Reconnaissance automatique du locuteur par des GMM à grande marge". Phd thesis, Toulouse 3, 2012. http://thesesups.ups-tlse.fr/1668/.
Texto completoMost of state-of-the-art speaker recognition systems are based on Gaussian Mixture Models (GMM), trained using maximum likelihood estimation and maximum a posteriori (MAP) estimation. The generative training of the GMM does not however directly optimize the classification performance. For this reason, discriminative models, e. G. , Support Vector Machines (SVM), have been an interesting alternative since they address directly the classification problem, and they lead to good performances. Recently a new discriminative approach for multiway classification has been proposed, the Large Margin Gaussian mixture models (LM-GMM). As in SVM, the parameters of LM-GMM are trained by solving a convex optimization problem. However they differ from SVM by using ellipsoids to model the classes directly in the input space, instead of half-spaces in an extended high-dimensional space. While LM-GMM have been used in speech recognition, they have not been used in speaker recognition (to the best of our knowledge). In this thesis, we propose simplified, fast and more efficient versions of LM-GMM which exploit the properties and characteristics of speaker recognition applications and systems, the LM-dGMM models. In our LM-dGMM modeling, each class is initially modeled by a GMM trained by MAP adaptation of a Universal Background Model (UBM) or directly initialized by the UBM. The models mean vectors are then re-estimated under some Large Margin constraints. We carried out experiments on full speaker recognition tasks under the NIST-SRE 2006 core condition. The experimental results are very satisfactory and show that our Large Margin modeling approach is very promising
Tremblay, Pier-Emmanuel. "Étude photométrique des étoiles naines blanches dans le domaine infrarouge". Thèse, 2007. http://hdl.handle.net/1866/18100.
Texto completoTran, Viet-Anh. "Communication silencieuse: conversion de la parole chuchotée en parole claire". Phd thesis, 2010. http://tel.archives-ouvertes.fr/tel-00614289.
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