abstract: Video denoising has been an important task in many multimedia and computer vision applications. Recent developments in the matrix completion theory and emergence of new numerical methods which can efficiently solve the matrix completion problem have paved the way for exploration of new techniques for some classical image processing tasks. Recent literature shows that many computer vision and image processing problems can be solved by using the matrix completion theory. This thesis explores the application of matrix completion in video denoising. A state-of-the-art video denoising algorithm in which the denoising task is modeled as a matrix completion problem is chosen for detailed study. The contribution of this thesis lies in both providing extensive analysis to bridge the gap in existing literature on matrix completion frame work for video denoising and also in proposing some novel techniques to improve the performance of the chosen denoising algorithm. The chosen algorithm is implemented for thorough analysis. Experiments and discussions are presented to enable better understanding of the problem. Instability shown by the algorithm at some parameter values in a particular case of low levels of pure Gaussian noise is identified. Artifacts introduced in such cases are analyzed. A novel way of grouping structurally-relevant patches is proposed to improve the algorithm. Experiments show that this technique is useful, especially in videos containing high amounts of motion. Based on the observation that matrix completion is not suitable for denoising patches containing relatively low amount of image details, a framework is designed to separate patches corresponding to low structured regions from a noisy image. Experiments are conducted by not subjecting such patches to matrix completion, instead denoising such patches in a different way. The resulting improvement in performance suggests that denoising low structured patches does not require a complex method like matrix completion and in fact it is counter-productive to subject such patches to matrix completion. These results also indicate the inherent limitation of matrix completion to deal with cases in which noise dominates the structural properties of an image. A novel method for introducing priorities to the ranked patches in matrix completion is also presented. Results showed that this method yields improved performance in general. It is observed that the artifacts in presence of low levels of pure Gaussian noise appear differently after introducing priorities to the patches and the artifacts occur at a wider range of parameter values. Results and discussion suggesting future ways to explore this problem are also presented. / Dissertation/Thesis / M.S. Electrical Engineering 2013

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Registration means to bring two or more shapes into a suitable relative configuration (position and orientation). In its major applications like 3D scan alignment, the aim is to coalesce data and regions originating from the same physical region have similar local form. So, the correspondence between shapes is discoverable from the shapes themselves, and the registration makes corresponding regions coincide. This work concerns the registration of shapes to satisfy a purpose or intent, not involving data integration. Regions relevant to the purpose are marked as patches correspondingly on two input 3D meshes of objects. Then, a method of registration is used to obtain the suitable configuration. Three methods of registration are explored in the present work. The first method of registration is to align intrinsic co-ordinate frames defined on the shapes. This is used in a scenario of comparison of shapes with dissimilar local form, which are to be aligned as an expert requires, as in the comparison of dental casts and apple bitemarks in forensics. Regions recognized in dentistry are marked as patches on the cast and bitemark shapes by a dentist. From these, an intrinsic frame is defined and aligned to bring the shapes close. The alignment is used to calculate distortion of a deteriorated bitemark. Another application of frame alignment is the analysis of shape variation of contours in a population for wearable product design. A frame based on anthropometric landmarks is used to construct the contours of the product's interface with the body-part, analyze its spread through a 2D grid-statistics method, and construct the interface shape. The frame helps assess the fit of the constructed shape on an individual. The method is demonstrated with respirator masks. Frame-based alignment is seen to give unsatisfactory results with head shapes for motorcycle-helmet interior design, as it does not adequately describe the helmet-head interaction. This inspires the second method of registration. The second method of registration is the biased minimization of distance between corresponding patches on the shapes, by weighting patches to indicate their importance in the registration. The method is used to assess the small deviation of precisely-known quantities in shapes, such as in manufactured part inspection. Here, the patches marked are grouped, and the part and model shapes registered at patches in the combinations of groups, by giving a binary weighting of 1 to these patches and 0 to others. The deviation of every patch across the registrations at multiple datum systems is tabulated and analyzed to infer errors. The method is exemplified with welded bars and bent-pipes. In the analysis of head-shape variation in a population to create headforms for wearable products, the deviations are large and not precisely known. So, the head shapes are registered at patches on regions pertinent to the product's functioning, with a relatively higher weight for a reference patch. A 3D grid-statistics method is used to analyze the shapes' spread and arrive at the headform shapes. The selection of head form for a given head shape is also treated. The method is demonstrated with motorcycle helmets and respirator masks. Biased distance-minimization is applied to obtain the mechanical assembly of part meshes. Different schemes of marking patches are tested as cases. The method leads to both intended and unintended final configurations, prompting for a better objective in registration. Thus, the third method of registration, that of normals is proposed; this happens in a transformed space. By analyzing the nature of assembly in CAD systems, the face-normals of the mesh are used to obtain the intended orientation of parts. The normals of corresponding patches are registered using three methods of registration, namely on a unit-sphere, of unit-normals, and spherical co-ordinates of normals. In each method, the optimal transformation is suitably converted to be applied on the actual part shape in 3D. Unit-normal alignment gives sensible results, while the other two lead to skewed final orientations. This is attributed to the nature of the space of registration. The methods are applied to examples involving different assembly relations, such as alignment of holes. On the whole, it is shown that correspondence embodies the knowledge of importance of regions on shapes for a purpose. The registration method should lead to an apt shape placement, which need not always mean coincidence. In essence, correspondence denotes 'what' regions are of relevance, and registration, 'how' to get the relative configuration satisfying a purpose or intent.

La maladie d’Alzheimer est la première cause de démence chez les personnes âgées. Cette maladie est caractérisée par un déclin irréversible des fonctions cognitives. Les patients atteints par la maladie d’Alzheimer ont de sévères pertes de mémoire et ont de grandes difficultés à apprendre de nouvelles informations ce qui pose de gros problèmes dans leur vie quotidienne. À ce jour, cette maladie est diagnostiquée après que d’importantes altérations des structures du cerveaux apparaissent. De plus, aucune thérapie existe permettant de faire reculer ou de stopper la maladie. Le développement de nouvelles méthodes permettant la détection précoce de cette maladie est ainsi nécessaire. En effet, une détection précoce permettrait une meilleure prise en charge des patients atteints de cette maladie ainsi qu’une accélération de la recherche thérapeutique. Nos travaux de recherche portent sur l’utilisation de l’imagerie médicale, avec notamment l’imagerie par résonance magnétique (IRM) qui a démontrée ces dernières années son potentiel pour améliorer la détection et la prédiction de la maladie d’Alzheimer. Afin d’exploiter pleinement ce type d’imagerie, de nombreuses méthodes ont été proposées récemment. Au cours de nos recherches, nous nous sommes intéressés à un type de méthode en particulier qui est basé sur la correspondance de patchs dans de grandes bibliothèques d’images. Nous avons étudié ces méthodes à diverses échelles anatomiques c’est à dire, cerveaux entier, hippocampe, sous-champs de l’hippocampe) avec diverses modalités d’IRM (par exemple, IRM anatomique et imagerie de diffusion). Nous avons amélioré les performances de détection dans les stades les plus précoces avec l’imagerie par diffusion. Nous avons aussi proposé un nouveau schéma de fusion pour combiner IRM anatomique et imagerie de diffusion. De plus, nous avons montré que la correspondance de patchs était améliorée par l’utilisation de filtres dérivatifs. Enfin, nous avons proposé une méthode par graphe permettant de combiner les informations de similarité inter-sujet avec les informations apportées par la variabilité intra-sujet. Les résultats des expériences menées dans cette thèse ont montrées une amélioration des performances de diagnostique et de prognostique de la maladie d’Alzheimer comparé aux méthodes de l’état de l’art. / Alzheimer’s disease (AD) is the most common dementia leading to a neurodegenerative process and causing mental dysfunctions. According to the world health organization, the number of patients having AD will double in 20 years. Neuroimaging studies performed on AD patients revealed that structural brain alterations are advanced when the diagnosis is established. Indeed, the clinical symptoms of AD are preceded by brain changes. This stresses the need to develop new biomarkers to detect the first stages of the disease. The development of such biomarkers can make easier the design of clinical trials and therefore accelerate the development of new therapies. Over the past decades, the improvement of magnetic resonance imaging (MRI) has led to the development of new imaging biomarkers. Such biomarkers demonstrated their relevance for computer-aided diagnosis but have shown limited performances for AD prognosis. Recently, advanced biomarkers were proposed toimprove computer-aided prognosis. Among them, patch-based grading methods demonstrated competitive results to detect subtle modifications at the earliest stages of AD. Such methods have shown their ability to predict AD several years before the conversion to dementia. For these reasons, we have had a particular interest in patch-based grading methods. First, we studied patch-based grading methods for different anatomical scales (i.e., whole brain, hippocampus, and hippocampal subfields). We adapted patch-based grading method to different MRI modalities (i.e., anatomical MRI and diffusion-weighted MRI) and developed an adaptive fusion scheme. Then, we showed that patch comparisons are improved with the use of multi-directional derivative features. Finally, we proposed a new method based on a graph modeling that enables to combine information from inter-subjects’ similarities and intra-subjects’ variability. The conducted experiments demonstrate that our proposed method enable an improvement of AD detection and prediction.

Volume measurements of timber loads is done in conjunction with timber trade. When dealing with goods of major economic values such as these, it is important to achieve an impartial and fair assessment when determining price-based volumes. With the help of Saab’s missile targeting technology, CIND AB develops products for digital volume measurement of timber loads. Currently there is a system in operation that automatically reconstructs timber trucks in motion to create measurable images of them. Future iterations of the system is expected to fully automate the scaling by generating a volumetric representation of the timber and calculate its external gross volume. The first challenge towards this development is to separate the timber load from the truck. This thesis aims to evaluate and implement appropriate method for semantic pixel-wise segmentation of timber loads in real time. Image segmentation is a classic but difficult problem in computer vision. To achieve greater robustness, it is therefore important to carefully study and make use of the conditions given by the existing system. Variations in timber type, truck type and packing together create unique combinations that the system must be able to handle. The system must work around the clock in different weather conditions while maintaining high precision and performance.

La tomographie SAR exploite plusieurs acquisitions d'une même zone acquises d'un point de vue légerement différent pour reconstruire la densité complexe de réflectivité au sol. Cette technique d'imagerie s'appuyant sur l'émission et la réception d'ondes électromagnétiques cohérentes, les données analysées sont complexes et l'information spatiale manquante (selon la verticale) est codée dans la phase. De nombreuse méthodes ont pu être proposées pour retrouver cette information. L'utilisation des redondances naturelles à certains milieux n'est toutefois généralement pas exploitée pour améliorer l'estimation tomographique. Cette thèse propose d'utiliser l'information structurelle propre aux structures urbaines pour régulariser les densités de réflecteurs obtenues par cette technique. / SAR tomography consists in exploiting multiple images from the same area acquired from a slightly different angle to retrieve the 3-D distribution of the complex reflectivity on the ground. As the transmitted waves are coherent, the desired spatial information (along with the vertical axis) is coded in the phase of the pixels. Many methods have been proposed to retrieve this information in the past years. However, the natural redundancies of the scene are generally not exploited to improve the tomographic estimation step. This Ph.D. presents new approaches to regularize the estimated reflectivity density obtained through SAR tomography by exploiting the urban geometrical structures.

Cette thèse s’intéresse à diverses composantes du traitement et de l’analyse d’images par méthodes non locales. Ces méthodes sont basées sur la redondance d’information présente dans d’autres images, et utilisent des algorithmes de recherche de correspondance, généralement basés sur l’utilisation patchs, pour extraire et transférer de l’information depuis ces images d’exemples. Ces approches, largement utilisées par la communauté de vision par ordinateur, sont souvent limitées par le temps de calcul de l’algorithme de recherche, appliqué à chaque pixel, et par la nécessité d’effectuer un prétraitement ou un apprentissage pour utiliser de grandes bases de données.Pour pallier ces limites, nous proposons plusieurs méthodes générales, sans apprentissage,rapides, et qui peuvent être facilement adaptées à diverses applications de traitement et d’analyse d’images naturelles ou médicales. Nous introduisons un algorithme de recherche de correspondances permettant d’extraire rapidement des patchs d’une grande bibliothèque d’images 3D, que nous appliquons à la segmentation d’images médicales. Pour utiliser de façon similaire aux patchs,des présegmentations en superpixels réduisant le nombre d’éléments de l’image,nous présentons une nouvelle structure de voisinage de superpixels. Ce nouveau descripteur permet d’utiliser efficacement les superpixels dans des approches non locales. Nous proposons également une méthode de décomposition régulière et précise en superpixels. Nous montrons comment évaluer cette régularité de façon robuste, et que celle-ci est nécessaire pour obtenir de bonnes performances de recherche de correspondances basées sur les superpixels. / This thesis focuses on several aspects of image analysis and processing with non local methods. These methods are based on the redundancy of information that occurs in other images, and use matching algorithms, that are usually patch-based, to extract and transfer information from the example data. These approaches are widely used by the computer vision community, and are generally limited by the computational time of the matching algorithm, applied at the pixel scale, and by the necessity to perform preprocessing or learning steps to use large databases. To address these issues, we propose several general methods, without learning, fast, and that can be easily applied to different image analysis and processing applications on natural and medical images. We introduce a matching algorithm that enables to quickly extract patches from a large library of 3D images, that we apply to medical image segmentation. To use a presegmentation into superpixels that reduces the number of image elements, in a way that is similar to patches, we present a new superpixel neighborhood structure. This novel descriptor enables to efficiently use superpixels in non local approaches. We also introduce an accurate and regular superpixel decomposition method. We show how to evaluate this regularity in a robust manner, and that this property is necessary to obtain good superpixel-based matching performances.

Cette thèse s'intéresse au développement de méthodes automatiques pour la segmentation et la synthèse d'images par résonance magnétique de tumeurs cérébrales. La principale perspective clinique de la segmentation des gliomes est le suivi de la vitesse d'expansion diamétrique dans le but d'adapter les solutions thérapeutiques. A cette fin, la thèse formalise au moyen de modèles graphiques probabilistes des approches de segmentation multi-atlas fondées sur l'appariement de blocs de voxels. Un premier modèle probabiliste prolonge à la segmentation automatique de régions cérébrales pathologiques les approches multi-atlas classiques de segmentation de structures anatomiques. Une approximation de l'étape de marginalisation remplace la notion de fenêtre de recherche locale par un tamisage par atlas et par étiquette. Un modèle de détection de gliomes fondé sur un a priori spatial et des critères de pré-sélection de blocs de voxels permettent d'obtenir des temps de calcul compétitifs malgré un appariement non local. Ce travail est validé et comparé à l'état de l'art sur des bases de données publiques. Un second modèle probabiliste, symétrique au modèle de segmentation, simule des images par résonance magnétique de cas pathologiques, à partir d'une unique segmentation. Une heuristique permet d'estimer le maximum a posteriori et l'incertitude du modèle de synthèse d'image. Un appariement itératif des blocs de voxels renforce la cohérence spatiale des images simulées. Le réalisme des images simulées est évalué avec de vraies IRM et des simulations de l'état de l'art. Le raccordement d'un modèle de croissance de tumeur permet de créer des bases d'images annotées synthétiques. / This thesis focuses on the development of automatic methods for the segmentation and synthesis of brain tumor Magnetic Resonance images. The main clinical perspective of glioma segmentation is growth velocity monitoring for patient therapy management. To this end, the thesis builds on the formalization of multi-atlas patch-based segmentation with probabilistic graphical models. A probabilistic model first extends classical multi-atlas approaches used for the segmentation of healthy brains structures to the automatic segmentation of pathological cerebral regions. An approximation of the marginalization step replaces the concept of local search windows with a stratification with respect to both atlases and labels. A glioma detection model based on a spatially-varying prior and patch pre-selection criteria are introduced to obtain competitive running times despite patch matching being non local. This work is validated and compared to state-of-the-art algorithms on publicly available datasets. A second probabilistic model mirrors the segmentation model in order to synthesize realistic MRI of pathological cases, based on a single label map. A heuristic method allows to solve for the maximum a posteriori and to estimate uncertainty of the image synthesis model. Iterating patch matching reinforces the spatial coherence of synthetic images. The realism of our synthetic images is assessed against real MRI, and against outputs of the state-of-the-art method. The junction of a tumor growth model to the proposed synthesis approach allows to generate databases of annotated synthetic cases.

Cette thèse s'inscrit dans le contexte des méthodes non locales pour le traitement d'images et a pour application principale le débruitage, bien que les méthodes étudiées soient suffisamment génériques pour être applicables à d'autres problèmes inverses en imagerie. Les images naturelles sont constituées de structures redondantes, et cette redondance peut être exploitée à des fins de restauration. Une manière classique d’exploiter cette auto-similarité est de découper l'image en patchs. Ces derniers peuvent ensuite être regroupés, comparés et filtrés ensemble.Dans le premier chapitre, le principe du "global denoising" est reformulé avec le formalisme classique de l'estimation diagonale et son comportement asymptotique est étudié dans le cas oracle. Des conditions précises à la fois sur l'image et sur le filtre global sont introduites pour assurer et quantifier la convergence.Le deuxième chapitre est consacré à l'étude d’a priori gaussiens ou de type mélange de gaussiennes pour le débruitage d'images par patches. Ces a priori sont largement utilisés pour la restauration d'image. Nous proposons ici quelques indices pour répondre aux questions suivantes : Pourquoi ces a priori sont-ils si largement utilisés ? Quelles informations encodent-ils ?Le troisième chapitre propose un modèle probabiliste de mélange pour les patchs bruités, adapté à la grande dimension. Il en résulte un algorithme de débruitage qui atteint les performance de l'état-de-l'art.Le dernier chapitre explore des pistes d'agrégation différentes et propose une écriture de l’étape d'agrégation sous la forme d'un problème de moindre carrés. / This thesis studies non-local methods for image processing, and their application to various tasks such as denoising. Natural images contain redundant structures, and this property can be used for restoration purposes. A common way to consider this self-similarity is to separate the image into "patches". These patches can then be grouped, compared and filtered together.In the first chapter, "global denoising" is reframed in the classical formalism of diagonal estimation and its asymptotic behaviour is studied in the oracle case. Precise conditions on both the image and the global filter are introduced to ensure and quantify convergence.The second chapter is dedicated to the study of Gaussian priors for patch-based image denoising. Such priors are widely used for image restoration. We propose some ideas to answer the following questions: Why are Gaussian priors so widely used? What information do they encode about the image?The third chapter proposes a probabilistic high-dimensional mixture model on the noisy patches. This model adopts a sparse modeling which assumes that the data lie on group-specific subspaces of low dimensionalities. This yields a denoising algorithm that demonstrates state-of-the-art performance.The last chapter explores different way of aggregating the patches together. A framework that expresses the patch aggregation in the form of a least squares problem is proposed.

La tâche la plus délicate d'un système de reconnaissance faciale est la phase d'extraction de caractéristiques significatives et discriminantes. Dans le cadre de cette thèse, nous nous sommes focalisés sur cette tâche avec comme objectif l'élaboration d'une représentation de visage robuste aux variations majeures suivantes: variations d'éclairage, de pose, de temps, images de qualité différentes (vidéosurveillance). Par ailleurs, nous avons travaillé également dans une optique de traitement temps réel. Tout d'abord, en tenant compte des caractéristiques d'orientation des traits principaux du visages (yeux, bouche), une nouvelle variante nommée ELBP de célèbre descripteur LBP a été proposée. Elle s'appuie sur les informations de micro-texture contenues dans une ellipse horizontale. Ensuite, le descripteur EPOEM est construit afin de tenir compte des informations d'orientation des contours. Puis un descripteur nommée PLPQMC qui intégre des informations obtenues par filtrage monogénique dans le descripteur LPQ est proposé. Enfin le descripteur LPOG intégrant des informations de gradient est présenté. Chacun des descripteurs proposés est testé sur les 3 bases d'images AR, FERET et SCface. Il en résulte que les descripteurs PLPQMC et LPOG sont les plus performants et conduisent à des taux de reconnaissance comparables voire supérieur à ceux des meilleurs méthodes de l'état de l'art. / Centered around feature extraction, the core task of any Face recognition system, our objective is devising a robust facial representation against major challenges, such as variations of illumination, pose and time-lapse and low resolution probe images, to name a few. Besides, fast processing speed is another crucial criterion. Towards these ends, several methods have been proposed through out this thesis. Firstly, based on the orientation characteristics of the facial information and important features, like the eyes and mouth, a novel variant of LBP, referred as ELBP, is designed for encoding micro patterns with the usage of an horizontal ellipse sample. Secondly, ELBP is exploited to extract local features from oriented edge magnitudes images. By this, the Elliptical Patterns of Oriented Edge Magnitudes (EPOEM) description is built. Thirdly, we propose a novel feature extraction method so called Patch based Local Phase Quantization of Monogenic components (PLPQMC). Lastly, a robust facial representation namely Local Patterns of Gradients (LPOG) is developed to capture meaningful features directly from gradient images. Chiefs among these methods are PLPQMC and LPOG as they are per se illumination invariant and blur tolerant. Impressively, our methods, while offering comparable or almost higher results than that of existing systems, have low computational cost and are thus feasible to deploy in real life applications.