Loopy belief propagation and probabilistic image processing

Estimation of hyperparameters by maximization of the marginal likelihood in probabilistic image processing is investigated by using the cluster variation method. The algorithms are substantially equivalent to generalized loopy belief propagation

Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T

PURPOSE: Dynamic lung oxygen-enhanced MRI (OE-MRI) is challenging due to the presence of confounding signals and poor signal-to-noise ratio, particularly at 3 T. We have created a robust pipeline utilizing independent component analysis (ICA) to automatically extract the oxygen-induced signal change from confounding factors to improve the accuracy and sensitivity of lung OE-MRI. METHODS: Dynamic OE-MRI was performed on healthy participants using a dual-echo multi-slice spoiled gradient echo sequence at 3 T and cyclical gas delivery. ICA was applied to each echo within a thoracic mask. The ICA component relating to the oxygen-enhancement signal was automatically identified using correlation analysis. The oxygen-enhancement component was reconstructed, and the percentage signal enhancement (PSE) was calculated. The lung PSE of current smokers was compared with nonsmokers; scan-rescan repeatability, ICA pipeline repeatability, and reproducibility between two vendors were assessed. RESULTS: ICA successfully extracted a consistent oxygen-enhancement component for all participants. Lung tissue and oxygenated blood displayed the opposite oxygen-induced signal enhancements. A significant difference in PSE was observed between the lungs of current smokers and nonsmokers. The scan-rescan repeatability and the ICA pipeline repeatability were good. CONCLUSION: The developed pipeline demonstrated sensitivity to the signal enhancements of the lung tissue and oxygenated blood at 3 T. The difference in lung PSE between current smokers and nonsmokers indicates a likely sensitivity to lung function alterations that may be seen in mild pathology, supporting future use of our methods in patient studies

Méthodes de séparation aveugle de sources non linéaires, étude du modèle quadratique 2X2

Cette thèse présente des méthodes de séparation aveugle de sources pour un modèle de mélange non-linéaire particulier, le cas quadratique avec auto-termes et termes croisés. Dans la première partie, nous présentons la structure de séparation étudiée qui se décline sous deux formes : étendue ou basique. Les propriétés de ce réseau récurrent sont ensuite analysées (points d'équilibre, stabilité locale). Nous proposons alors deux méthodes de séparation aveugle de sources. La première exploite les cumulants des observations en un bloc placé en amont de la structure récurrente. La deuxième méthode est basée sur une approche par maximum de vraisemblance. Le tout est validé par des simulations numériques.This thesis presents blind source separation (BSS) methods for a particular model of mixture, the quadratic one. The first part presents the separating structure (basic and extended versions).The equilibrium points of the structure and their local stability are then studied. We propose two methods of BSS. The first method uses the cumulants and the second is based on a maximum likelihood approach. We validate our results by numerical tests

Multi-system Biometric Authentication: Optimal Fusion and User-Specific Information

Verifying a person's identity claim by combining multiple biometric systems (fusion) is a promising solution to identity theft and automatic access control. This thesis contributes to the state-of-the-art of multimodal biometric fusion by improving the understanding of fusion and by enhancing fusion performance using information specific to a user. One problem to deal with at the score level fusion is to combine system outputs of different types. Two statistically sound representations of scores are probability and log-likelihood ratio (LLR). While they are equivalent in theory, LLR is much more useful in practice because its distribution can be approximated by a Gaussian distribution, which makes it useful to analyze the problem of fusion. Furthermore, its score statistics (mean and covariance) conditioned on the claimed user identity can be better exploited. Our first contribution is to estimate the fusion performance given the class-conditional score statistics and given a particular fusion operator/classifier. Thanks to the score statistics, we can predict fusion performance with reasonable accuracy, identify conditions which favor a particular fusion operator, study the joint phenomenon of combining system outputs with different degrees of strength and correlation and possibly correct the adverse effect of bias (due to the score-level mismatch between training and test sets) on fusion. While in practice the class-conditional Gaussian assumption is not always true, the estimated performance is found to be acceptable. Our second contribution is to exploit the user-specific prior knowledge by limiting the class-conditional Gaussian assumption to each user. We exploit this hypothesis in two strategies. In the first strategy, we combine a user-specific fusion classifier with a user-independent fusion classifier by means of two LLR scores, which are then weighted to obtain a single output. We show that combining both user-specific and user-independent LLR outputs always results in improved performance than using the better of the two. In the second strategy, we propose a statistic called the user-specific F-ratio, which measures the discriminative power of a given user based on the Gaussian assumption. Although similar class separability measures exist, e.g., the Fisher-ratio for a two-class problem and the d-prime statistic, F-ratio is more suitable because it is related to Equal Error Rate in a closed form. F-ratio is used in the following applications: a user-specific score normalization procedure, a user-specific criterion to rank users and a user-specific fusion operator that selectively considers a subset of systems for fusion. The resultant fusion operator leads to a statistically significantly increased performance with respect to the state-of-the-art fusion approaches. Even though the applications are different, the proposed methods share the following common advantages. Firstly, they are robust to deviation from the Gaussian assumption. Secondly, they are robust to few training data samples thanks to Bayesian adaptation. Finally, they consider both the client and impostor information simultaneously

BIOMETRIC TECHNOLOGIES FOR AMBIENT INTELLIGENCE

Il termine Ambient Intelligence (AmI) si riferisce a un ambiente in grado di riconoscere e rispondere alla presenza di diversi individui in modo trasparente, non intrusivo e spesso invisibile. In questo tipo di ambiente, le persone sono circondate da interfacce uomo macchina intuitive e integrate in oggetti di ogni tipo. Gli scopi dell\u2019AmI sono quelli di fornire un supporto ai servizi efficiente e di facile utilizzo per accrescere le potenzialit\ue0 degli individui e migliorare l\u2019interazioni uomo-macchina. Le tecnologie di AmI possono essere impiegate in contesti come uffici (smart offices), case (smart homes), ospedali (smart hospitals) e citt\ue0 (smart cities). Negli scenari di AmI, i sistemi biometrici rappresentano tecnologie abilitanti al fine di progettare servizi personalizzati per individui e gruppi di persone. La biometria \ue8 la scienza che si occupa di stabilire l\u2019identit\ue0 di una persona o di una classe di persone in base agli attributi fisici o comportamentali dell\u2019individuo. Le applicazioni tipiche dei sistemi biometrici includono: controlli di sicurezza, controllo delle frontiere, controllo fisico dell\u2019accesso e autenticazione per dispositivi elettronici. Negli scenari basati su AmI, le tecnologie biometriche devono funzionare in condizioni non controllate e meno vincolate rispetto ai sistemi biometrici comunemente impiegati. Inoltre, in numerosi scenari applicativi, potrebbe essere necessario utilizzare tecniche in grado di funzionare in modo nascosto e non cooperativo. In questo tipo di applicazioni, i campioni biometrici spesso presentano una bassa qualit\ue0 e i metodi di riconoscimento biometrici allo stato dell\u2019arte potrebbero ottenere prestazioni non soddisfacenti. \uc8 possibile distinguere due modi per migliorare l\u2019applicabilit\ue0 e la diffusione delle tecnologie biometriche negli scenari basati su AmI. Il primo modo consiste nel progettare tecnologie biometriche innovative che siano in grado di funzionare in modo robusto con campioni acquisiti in condizioni non ideali e in presenza di rumore. Il secondo modo consiste nel progettare approcci biometrici multimodali innovativi, in grado di sfruttare a proprio vantaggi tutti i sensori posizionati in un ambiente generico, al fine di ottenere un\u2019elevata accuratezza del riconoscimento ed effettuare autenticazioni continue o periodiche in modo non intrusivo. Il primo obiettivo di questa tesi \ue8 la progettazione di sistemi biometrici innovativi e scarsamente vincolati in grado di migliorare, rispetto allo stato dell\u2019arte attuale, la qualit\ue0 delle tecniche di interazione uomo-macchine in diversi scenari applicativi basati su AmI. Il secondo obiettivo riguarda la progettazione di approcci innovativi per migliorare l\u2019applicabilit\ue0 e l\u2019integrazione di tecnologie biometriche eterogenee negli scenari che utilizzano AmI. In particolare, questa tesi considera le tecnologie biometriche basate su impronte digitali, volto, voce e sistemi multimodali. Questa tesi presenta le seguenti ricerche innovative: \u2022 un metodo per il riconoscimento del parlatore tramite la voce in applicazioni che usano AmI; \u2022 un metodo per la stima dell\u2019et\ue0 dell\u2019individuo da campioni acquisiti in condizioni non-ideali nell\u2019ambito di scenari basati su AmI; \u2022 un metodo per accrescere l\u2019accuratezza del riconoscimento biometrico in modo protettivo della privacy e basato sulla normalizzazione degli score biometrici tramite l\u2019analisi di gruppi di campioni simili tra loro; \u2022 un approccio per la fusione biometrica multimodale indipendente dalla tecnologia utilizzata, in grado di combinare tratti biometrici eterogenei in scenari basati su AmI; \u2022 un approccio per l\u2019autenticazione continua multimodale in applicazioni che usano AmI. Le tecnologie biometriche innovative progettate e descritte in questa tesi sono state validate utilizzando diversi dataset biometrici (sia pubblici che acquisiti in laboratorio), i quali simulano le condizioni che si possono verificare in applicazioni di AmI. I risultati ottenuti hanno dimostrato la realizzabilit\ue0 degli approcci studiati e hanno mostrato che i metodi progettati aumentano l\u2019accuratezza, l\u2019applicabilit\ue0 e l\u2019usabilit\ue0 delle tecnologie biometriche rispetto allo stato dell\u2019arte negli scenari basati su AmI.Ambient Intelligence (AmI) refers to an environment capable of recognizing and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way. In this environment, people are surrounded by intelligent intuitive interfaces that are embedded in all kinds of objects. The goals of AmI are to provide greater user-friendliness, more efficient services support, user-empowerment, and support for human interactions. Examples of AmI scenarios are smart cities, smart homes, smart offices, and smart hospitals. In AmI, biometric technologies represent enabling technologies to design personalized services for individuals or groups of people. Biometrics is the science of establishing the identity of an individual or a class of people based on the physical, or behavioral attributes of the person. Common applications include: security checks, border controls, access control to physical places, and authentication to electronic devices. In AmI, biometric technologies should work in uncontrolled and less-constrained conditions with respect to traditional biometric technologies. Furthermore, in many application scenarios, it could be required to adopt covert and non-cooperative technologies. In these non-ideal conditions, the biometric samples frequently present poor quality, and state-of-the-art biometric technologies can obtain unsatisfactory performance. There are two possible ways to improve the applicability and diffusion of biometric technologies in AmI. The first one consists in designing novel biometric technologies robust to samples acquire in noisy and non-ideal conditions. The second one consists in designing novel multimodal biometric approaches that are able to take advantage from all the sensors placed in a generic environment in order to achieve high recognition accuracy and to permit to perform continuous or periodic authentications in an unobtrusive manner. The first goal of this thesis is to design innovative less-constrained biometric systems, which are able to improve the quality of the human-machine interaction in different AmI environments with respect to the state-of-the-art technologies. The second goal is to design novel approaches to improve the applicability and integration of heterogeneous biometric systems in AmI. In particular, the thesis considers technologies based on fingerprint, face, voice, and multimodal biometrics. This thesis presents the following innovative research studies: \u2022 a method for text-independent speaker identification in AmI applications; \u2022 a method for age estimation from non-ideal samples acquired in AmI scenarios; \u2022 a privacy-compliant cohort normalization technique to increase the accuracy of already deployed biometric systems; \u2022 a technology-independent multimodal fusion approach to combine heterogeneous traits in AmI scenarios; \u2022 a multimodal continuous authentication approach for AmI applications. The designed novel biometric technologies have been tested on different biometric datasets (both public and collected in our laboratory) simulating the acquisitions performed in AmI applications. Results proved the feasibility of the studied approaches and shown that the studied methods effectively increased the accuracy, applicability, and usability of biometric technologies in AmI with respect to the state-of-the-art

Comparaison des documents audiovisuels<br />par Matrice de Similarité

The work of this thesis relates to the comparison of video documents. The field of digital video is in full expansion. Videos are now present in large quantity even for personal use. The video comparison is a basic analysis operation in complement of classification, extraction and structuring of videos.Traditional approaches of comparison are primarily based on the low-level features of the videos to be compared, considered as multidimensional vectors. Other approaches are based on the similarity of frames without taking into account neither the temporal composition of the video nor the audiolayer. The main disadvantage of these methods is that they reduce the comparison role to a simple operator robust to noise effects. Such operators are generally used in order to identify the various specimens of a same document.The originality of our approach lies in the introduction of the of style similarity notion, taking as a starting point the human criteria into the comparison. These criteria are more flexible, and do not impose a strict similarity of all the studied features at the same time.We define an algorithm of extraction of the similarities between the series of values produced bythe analysis of the audiovisual low-level features. The algorithm is inspired by the dynamic programmingand the time series comparison methods.We propose a representation of the data resulting from these processings in the form of a matrixpattern suitable for the visual and immediate comparison of two videos. This matrix is then used topropose a generic similarity measure. The measure is applicable independently to videos of comparableor heterogeneous contents.We developed several applications to demonstrate the behavior of the comparison method and thesimilarity measure. The experiments concern primarily: - the identification of the structure in acollection/sub-collection of documents, - the description of stylistics elements in a movie, and - theanalysis of the grid of programs from a TV stream.Les travaux de cette thèse concernent la comparaison des documents vidéo. Dans le domaine en pleine expansion de la vidéo numérique, les documents disponibles sont maintenant présents en quantité importante même dans les foyers. Opération de base de tout type d'analyse de contenus, en complément de la classification, de l'extraction et de la structuration, la comparaison dans le domaine de l'audiovisuel est d'une utilité qui n'est pas à démontrer.Des approches classiques de comparaison se basent essentiellement sur l'ensemble des caractéristiquesbas niveaux des documents à comparer, en les considérant comme des vecteurs multidimensionnels. D'autres approches se basent sur la similarité des images composant la vidéo sans tenir compte de la composition temporelle du document ni de la bandeson. Le défaut que l'on peut reprocher à ces méthodes est qu'elles restreignent la comparaison à un simple opérateur binaire robuste au bruit. De tels opérateurs sont généralement utilisés afin d'identifier les différents exemplaires d'un même document. L'originalité de notre démarche réside dans le fait que nous introduisons la notion de la similarité de styleen s'inspirant des critères humains dans la comparaison des documents vidéo. Ces critèressont plus souples, et n'imposent pas une similarité stricte de toutes les caractéristiques étudiéesà la fois.En nous inspirant de la programmation dynamique et de la comparaison des séries chronologiques, nous définissons un algorithme d'extraction des similarités entre les séries de valeurs produites par l'analyse de caractéristiques audiovisuelles de bas-niveau. Ensuite, un second traitement générique approxime le résultat de l'algorithme de la longueur de la PlusLongue Sous-Séquence Commune (PLSC) plus rapidement que ce dernier. Nous proposons une représentation des données issues de ces traitements sous la forme d'un schéma matriciel propre à la comparaison visuelle et immédiate de deux contenus. Cette matrice peut être également utilisée pour définir une mesure de similarité générique, applicable à des documents de même genre ou de genres hétérogènes.Plusieurs applications ont été mises en place pour démontrer le comportement de la méthode de comparaison et de la mesure de similarité, ainsi que leur pertinence. Les expérimentations concernent essentiellement : - l'identification d'une structure organisationnelle en collection / sous-collection d'une base de documents, - la mise en évidence d'élémentsstylistiques dans un film de cinéma, - la mise en évidence de la grille de programmes d'unflux de télévision

Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen

El modelo cortical HTM y su aplicación al conocimiento lingüístico

El problema que aborda este trabajo de investigación es encontrar un modelo neurocomputacional de representación y comprensión del conocimiento léxico, utilizando para ello el algoritmo cortical HTM, que modela el mecanismo según el cual se procesa la información en el neocórtex humano. La comprensión automática del lenguaje natural implica que las máquinas tengan un conocimiento profundo del lenguaje natural, lo que, actualmente, está muy lejos de conseguirse. En general, los modelos computacionales para el Procesamiento del Lenguaje Natural (PLN), tanto en su vertiente de análisis y comprensión como en la de generación, utilizan algoritmos fundamentados en modelos matemáticos y lingüísticos que intentan emular la forma en la que tradicionalmente se ha procesado el lenguaje, por ejemplo, obteniendo la estructura jerárquica implícita de las frases o las desinencias de las palabras. Estos modelos son útiles porque sirven para construir aplicaciones concretas como la extracción de datos, la clasificación de textos o el análisis de opinión. Sin embargo, a pesar de su utilidad, las máquinas realmente no entienden lo que hacen con ninguno de estos modelos. Por tanto, la pregunta que se aborda en este trabajo es si, realmente, es posible modelar computacionalmente los procesos neocorticales humanos que regulan el tratamiento de la información de tipo semántico del léxico. Esta cuestión de investigación constituye el primer nivel para comprender el procesamiento del lenguaje natural a niveles lingüísticos superiores..