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L'invariance d'échelle repose sur l'intuition que les dynamiques temporelles ne sont pas gouvernées par une (ou quelques) échelle(s) caratéristique(s). Cette propriété est massivement utilisée dans la modélisation et l'analyse de données univariées issues d'applications réelles. Son utilisation pratique se heurte pourtant à deux difficultés dans les applications modernes : les propriétés d'invariance d'échelle ne sont plus nécessairement homogènes en temps ou espace ; le caractère multivarié des données rend fortement non linéaires et non convexes les fonctionnelles à minimiser pour l'estimation des paramètres d'invariance d'échelle. La première originalité de ce travail est d'envisager l'étude de l'invariance d'échelle inhomogène comme un problème conjoint de détection/segmentation et estimation et d'en proposer une formulation par minimisation de fonctionnelles vectorielles, construites autour de pénalisation par variation totale, afin d'estimer à la fois les frontières délimitant les changements et les propriétés d'invariance d'échelle de chaque région. La construction d'un algorithme de débruitage par variation totale vectorielle à la volée est proposée. La seconde originalité réside dans la conception d'une procédure de minimisation de fonctionnelle non convexe type « branch and bound » pour l'identification complète de l'extension bivariée, du mouvement brownien fractionnaire, considéré comme référence pour la modélisation de l'invariance d'échelle univariée. Cette procédure est mise en œuvre en pratique sur des données de trafic Internet dans le contexte de la détection d'anomalies. Dans un troisième temps, nous proposons des contributions spécifiques au débruitage par variation totale : modèle poissonnien d'attache aux données en relation avec un problème de détection d'états pour la fluorescence intermittente ; sélection automatique du paramètre de régularisation. / Scale invariance relies on the intuition that temporal dynamics are not driven by one (or a few) characteristic scale(s). This property is massively used in the modeling and analysis of univariate data stemming from real-world applications. However, its use in practice encounters two difficulties when dealing with modern applications: scaling properties are not necessarily homogenous in time or space ; the multivariate nature of data leads to the minimization of highly non-linear and non-convex functionals in order to estimate the scaling parameters.The first originality of this work is to investigate the study of non-homogenous scale invariance as a joint problem of detection/segmentation and estimation, and to propose its formulation by the minimization of vectorial functionals constructed around a total variation penalization, in order to estimate both the boundaries delimiting the changes and the scaling properties within each region.The second originality lies in the design of a branch and bound minimization procedure of non-convex functional for the full identification of the bivariate extension of fractional Brownian motion, considered as the reference for modeling univariate scale invariance. Such procedure is applied in practice on Internet traffic data in the context of anomaly detection.Thirdly, we propose some contributions specific to total variation denoising: Poisson data-fidelity model related to a state detection problem in intermittent fluorescence ; automatic selection of the regularization parameter.

There are several types of disorders that affect our colon’s ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and colonic polyps. Automatic detection of these diseases would inform the endoscopist of possible sub-optimal inspection during the colonoscopy procedure as well as save time during post-procedure evaluation. But existing systems only detects few of those disorders like colonic polyps. In this dissertation, we address the automatic detection of another important disorder called ulcerative colitis. We propose a novel texture feature extraction technique to detect the severity of ulcerative colitis in block, image, and video levels. We also enhance the current informative frame filtering methods by detecting water and bubble frames using our proposed technique. Our feature extraction algorithm based on accumulation of pixel value difference provides better accuracy at faster speed than the existing methods making it highly suitable for real-time systems. We also propose a hybrid approach in which our feature method is combined with existing feature method(s) to provide even better accuracy. We extend the block and image level detection method to video level severity score calculation and shot segmentation. Also, the proposed novel feature extraction method can detect water and bubble frames in colonoscopy videos with very high accuracy in significantly less processing time even when clustering is used to reduce the training size by 10 times.

The interface with the real-world has proved to be extremely challenging throughout the past 70 years in which computer technology has been developing. The problem initially is assumed to be somewhat trivial, as humans are exceptionally skilled at interpreting real-world data, for example pictures and sounds. Traditional analytical methods have so far not provided the complete answer to what will be termed pattern recognition. Biological inspiration has motivated pattern recognition researchers since the early days of the subject, and the idea of a neural network which has self-evolving properties has always been seen to be a potential solution to this endeavour. Unlike the development of computer technology in which successive generations of improved devices have been developed, the neural network approach has been less successful, with major setbacks occurring in its development. However, the fact that natural processing in animals and humans is a voltage-based process, devoid of software, and self-evolving, provides an on-going motivation for pattern recognition in artificial neural networks. This thesis addresses the application of weightless neural networks using a ranking pre-processor to implement general pattern recognition with specific reference to face processing. The evaluation of the system will be carried out on open source databases in order to obtain a direct comparison of the efficacy of the method, in particular considerable use will be made of the MIT-CBCL face database. The methodology is cost effective in both software and hardware forms, offers real-time video processing, and can be implemented on all computer platforms. The results of this research show significant improvements over published results, and provide a viable commercial methodology for general pattern recognition.

A displasia cortical focal (DCF) é uma das causas mais frequentes de epilepsia refratária. Na clínica, diferentes informações são usadas para localizar o foco epileptogênico, mas nenhum método é autossuficiente para evidenciar o local original das crises, associado com a presença da DCF. Embora haja relatos na literatura indicando alterações no padrão de distribuição de tons de cinza e morfologia dos voxels decorrentes da DCF, algumas limitações dos métodos desenvolvidos ainda impedem a utilização clínica. Nossa proposta foi investigar a capacidade de identificar DCF através de análises de espessura cortical e padrões de textura em imagens estruturais de Ressonância Magnética (RM), validando os métodos desenvolvidos a partir uma base de imagens retrospectiva, cujo tecido epileptogênico já havia sido ressecado e a DCF confirmada em análise histológica. A caracterização das DCF foi feita a partir da segmentação automática de tecido cortical saudável em conjunto com a segmentação manual da DCF feita por um especialista, e consiste na geração de mapas de característica e extração de valores de distribuições para comparação em análise estatística. Investigamos também a eficácia da detecção de DCF através do uso de algoritmos de aprendizado de máquina para classificação automática. Obtivemos precisão 0,81 e sensitividade 0,87, colocando o método desenvolvido em par com outros métodos presentes na literatura. Entretanto, foi identificada uma grande dependência do desempenho de métodos de pré-processamento, como corregistro e segmentação automática. / Focal Cortical Dysplasia (FCD) is one of the most frequent causes of refractory epilepsy. In clinical procedures, the information gathered from different techniques is used in order to locate the epileptogenic focus, associated with the presence of FCD. However, there is no self sufficient method to evidence the presence and location of such lesions and especially its extension. Although there are reports indicating change in gray scale intensity patterns and voxel morphology in the presence of DCF, limitations in developed methods still prevent their clinical use. Our proposal was to investigate the capability of identifying FCD through cortical thickness and texture patter analysis in structural MRI images, validating developed methods by utilizing a retrospective base of images from patients that were subjected to surgery, with the FCD being confirmed in histological analysis. Characterization of FCD was achieved from automatic segmentation of healthy cortex and manual segmentation of FCD tissue made by an specialist, and consists in the generation of texture or structural feature maps and comparison of distribution values in healthy or FCD tissue with statistical analysis. We also investigate the efficiency of FCD detection with Machine Learning automatic classification, obtaining precision of 0,81 and sensitivity of 0,87, placing our method on par with other methods in the literature. However, there is a major performance dependency of proposed method with pre-processing steps, like registration and automatic segmentation.

A displasia cortical focal (DCF) é uma das causas mais frequentes de epilepsia refratária. Na clínica, diferentes informações são usadas para localizar o foco epileptogênico, mas nenhum método é autossuficiente para evidenciar o local original das crises, associado com a presença da DCF. Embora haja relatos na literatura indicando alterações no padrão de distribuição de tons de cinza e morfologia dos voxels decorrentes da DCF, algumas limitações dos métodos desenvolvidos ainda impedem a utilização clínica. Nossa proposta foi investigar a capacidade de identificar DCF através de análises de espessura cortical e padrões de textura em imagens estruturais de Ressonância Magnética (RM), validando os métodos desenvolvidos a partir uma base de imagens retrospectiva, cujo tecido epileptogênico já havia sido ressecado e a DCF confirmada em análise histológica. A caracterização das DCF foi feita a partir da segmentação automática de tecido cortical saudável em conjunto com a segmentação manual da DCF feita por um especialista, e consiste na geração de mapas de característica e extração de valores de distribuições para comparação em análise estatística. Investigamos também a eficácia da detecção de DCF através do uso de algoritmos de aprendizado de máquina para classificação automática. Obtivemos precisão 0,81 e sensitividade 0,87, colocando o método desenvolvido em par com outros métodos presentes na literatura. Entretanto, foi identificada uma grande dependência do desempenho de métodos de pré-processamento, como corregistro e segmentação automática. / Focal Cortical Dysplasia (FCD) is one of the most frequent causes of refractory epilepsy. In clinical procedures, the information gathered from different techniques is used in order to locate the epileptogenic focus, associated with the presence of FCD. However, there is no self sufficient method to evidence the presence and location of such lesions and especially its extension. Although there are reports indicating change in gray scale intensity patterns and voxel morphology in the presence of DCF, limitations in developed methods still prevent their clinical use. Our proposal was to investigate the capability of identifying FCD through cortical thickness and texture patter analysis in structural MRI images, validating developed methods by utilizing a retrospective base of images from patients that were subjected to surgery, with the FCD being confirmed in histological analysis. Characterization of FCD was achieved from automatic segmentation of healthy cortex and manual segmentation of FCD tissue made by an specialist, and consists in the generation of texture or structural feature maps and comparison of distribution values in healthy or FCD tissue with statistical analysis. We also investigate the efficiency of FCD detection with Machine Learning automatic classification, obtaining precision of 0,81 and sensitivity of 0,87, placing our method on par with other methods in the literature. However, there is a major performance dependency of proposed method with pre-processing steps, like registration and automatic segmentation.