Reconhecimento de padrões em imagens é uma área de grande interesse no mundo científico. Os chamados métodos de extração de características, possuem as habilidades de extrair características das imagens e também de reduzir a dimensionalidade dos dados gerando assim o chamado vetor de características. Considerando uma imagem de consulta, o foco de um sistema de reconhecimento de imagens de faces humanas é pesquisar em um banco de imagens, a imagem mais similar à imagem de consulta, de acordo com um critério dado. Este trabalho de pesquisa foi direcionado para a geração de vetores de características para um sistema de reconhecimento de imagens, considerando bancos de imagens de faces humanas, para propiciar tal tipo de consulta. Um vetor de características é uma representação numérica de uma imagem ou parte dela, descrevendo seus detalhes mais representativos. O vetor de características é um vetor n-dimensional contendo esses valores. Essa nova representação da imagem propicia vantagens ao processo de reconhecimento de imagens, pela redução da dimensionalidade dos dados. Uma abordagem alternativa para caracterizar imagens para um sistema de reconhecimento de imagens de faces humanas é a transformação do domínio. A principal vantagem de uma transformação é a sua efetiva caracterização das propriedades locais da imagem. As wavelets diferenciam-se das tradicionais técnicas de Fourier pela forma de localizar a informação no plano tempo-freqüência; basicamente, têm a capacidade de mudar de uma resolução para outra, o que as fazem especialmente adequadas para análise, representando o sinal em diferentes bandas de freqüências, cada uma com resoluções distintas correspondentes a cada escala. As wavelets foram aplicadas com sucesso na compressão, melhoria, análise, classificação, caracterização e recuperação de imagens. Uma das áreas beneficiadas onde essas propriedades tem encontrado grande relevância é a área de visão computacional, através da representação e descrição de imagens. Este trabalho descreve uma abordagem para o reconhecimento de imagens de faces humanas com a extração de características baseado na decomposição multiresolução de wavelets utilizando os filtros de Haar, Daubechies, Biorthogonal, Reverse Biorthogonal, Symlet, e Coiflet. Foram testadas em conjunto as técnicas PCA (Principal Component Analysis) e IMPCA (Image Principal Component Analysis), sendo que os melhores resultados foram obtidos utilizando a wavelet Biorthogonal com a técnica IMPCA / Image pattern recognition is an interesting area in the scientific world. The features extraction method refers to the ability to extract features from images, reduce the dimensionality and generates the features vector. Given a query image, the goal of a features extraction system is to search the database and return the most similar to the query image according to a given criteria. Our research addresses the generation of features vectors of a recognition image system for human faces databases. A feature vector is a numeric representation of an image or part of it over its representative aspects. The feature vector is a n-dimensional vector organizing such values. This new image representation can be stored into a database and allow a fast image retrieval. An alternative for image characterization for a human face recognition system is the domain transform. The principal advantage of a transform is its effective characterization for their local image properties. In the past few years researches in applied mathematics and signal processing have developed practical wavelet methods for the multi scale representation and analysis of signals. These new tools differ from the traditional Fourier techniques by the way in which they localize the information in the time-frequency plane; in particular, they are capable of trading on type of resolution for the other, which makes them especially suitable for the analysis of non-stationary signals. The wavelet transform is a set basis function that represents signals in different frequency bands, each one with a resolution matching its scale. They have been successfully applied to image compression, enhancement, analysis, classification, characterization and retrieval. One privileged area of application where these properties have been found to be relevant is computer vision, especially human faces imaging. In this work we describe an approach to image recognition for human face databases focused on feature extraction based on multiresolution wavelets decomposition, taking advantage of Biorthogonal, Reverse Biorthogonal, Symlet, Coiflet, Daubechies and Haar. They were tried in joint the techniques together the PCA (Principal Component Analysis) and IMPCA (Image Principal Component Analysis)

Ce travail présente une nouvelle méthodologie pour la reconnaissance automatique des gestes de la bouche visant à l'élaboration d'IHM pour la commande d'endoscope. Cette méthodologie comprend des étapes communes à la plupart des systèmes de vision artificielle, comme le traitement d'image et la segmentation, ainsi qu'une méthode pour l'amélioration progressive de l'étiquetage obtenu grâce à la segmentation. Contrairement aux autres approches, la méthodologie est conçue pour fonctionner avec poses statiques, qui ne comprennent pas les mouvements de la tête. Beaucoup d'interêt est porté aux tâches de segmentation d'images, car cela s'est avéré être l'étape la plus importante dans la reconnaissance des gestes. En bref, les principales contributions de cette recherche sont les suivantes: La conception et la mise en oeuvre d'un algorithme de rafinement d'étiquettes qui dépend d'une première segmentation/pixel étiquetage et de deux paramétres corrélés. Le rafineur améliore la précision de la segmentation indiquée dans l'étiquetage de sortie pour les images de la bouche, il apporte également une amélioration acceptable lors de l'utilisation d'images naturelles. La définition de deux méthodes de segmentation pour les structures de la bouche dans les images; l'une fondée sur les propriétés de couleur des pixels, et l'autre sur des éléments de la texture locale, celles-ci se complétent pour obtenir une segmentation rapide et précise de la structure initiale. La palette de couleurs s'avére particuliérement importante dans la structure de séparation, tandis que la texture est excellente pour la séparation des couleurs de la bouche par rapport au fond. La dérivation d'une procédure basée sur la texture pour l'automatisation de la sélection des paramètres pour la technique de rafinement de segmentation discutée dans la première contribution. Une version améliorée de l'algorithme d'approximation bouche contour présentée dans l'ouvrage de Eveno et al. [1, 2], ce qui réduit le nombre d'itérations nécessaires pour la convergence et l'erreur d'approximation finale. La découverte de l'utilité de la composante de couleur CIE à statistiquement normalisée, dans la différenciation lévres et la langue de la peau, permettant l'utilisation des valeurs seuils constantes pour effectuer la comparaison. / This document presents a series of elements for approaching the task of segmenting mouth structures in facial images, particularly focused in frames from video sequences. Each stage is treated separately in different Chapters, starting from image pre-processing and going up to segmentation labeling post-processing, discussing the technique selection and development in every case. The methodological approach suggests the use of a color based pixel classification strategy as the basis of the mouth structure segmentation scheme, complemented by a smart pre-processing and a later label refinement. The main contribution of this work, along with the segmentation methodology itself, is based in the development of a color-independent label refinement technique. The technique, which is similar to a linear low pass filter in the segmentation labeling space followed by a nonlinear selection operation, improves the image labeling iteratively by filling small gaps and eliminating spurious regions resulting from a prior pixel classification stage. Results presented in this document suggest that the refiner is complementary to image pre-processing, hence achieving a cumulative effect in segmentation quality. At the end, the segmentation methodology comprised by input color transformation, preprocessing, pixel classification and label refinement, is put to test in the case of mouth gesture detection in images aimed to command three degrees of freedom of an endoscope holder.

This thesis represents Bayesian joint audio-visual tracking for the 3D locations of multiple people and a current speaker in a real conference environment. To achieve this objective, it focuses on several different research interests, such as acoustic-feature detection, visual-feature detection, a non-linear Bayesian framework, data association, and sensor fusion. As acoustic-feature detection, time-delay-of-arrival~(TDOA) estimation is used for multiple source detection. Localization performance using TDOAs is also analyzed according to different configurations of microphones. As a visual-feature detection, Viola-Jones face detection is used to initialize the locations of unknown multiple objects. Then, a corner feature, based on the results from the Viola-Jones face detection, is used for motion detection for robust objects. Simple point-to-line correspondences between multiple cameras using fundamental matrices are used to determine which features are more robust. As a method for data association and sensor fusion, Monte-Carlo JPDAF and a data association with IPPF~(DA-IPPF) are implemented in the framework of particle filtering. Three different tracking scenarios of acoustic source tracking, visual source tracking, and joint acoustic-visual source tracking are represented using the proposed algorithms. Finally the real-time implementation of this joint acoustic-visual tracking system using a PC, four cameras, and six microphones is addressed with two parts of system implementation and real-time processing.

Reconhecimento de padrões em imagens é uma área de grande interesse no mundo científico. Os chamados métodos de extração de características, possuem as habilidades de extrair características das imagens e também de reduzir a dimensionalidade dos dados gerando assim o chamado vetor de características. Considerando uma imagem de consulta, o foco de um sistema de reconhecimento de imagens de faces humanas é pesquisar em um banco de imagens, a imagem mais similar à imagem de consulta, de acordo com um critério dado. Este trabalho de pesquisa foi direcionado para a geração de vetores de características para um sistema de reconhecimento de imagens, considerando bancos de imagens de faces humanas, para propiciar tal tipo de consulta. Um vetor de características é uma representação numérica de uma imagem ou parte dela, descrevendo seus detalhes mais representativos. O vetor de características é um vetor n-dimensional contendo esses valores. Essa nova representação da imagem propicia vantagens ao processo de reconhecimento de imagens, pela redução da dimensionalidade dos dados. Uma abordagem alternativa para caracterizar imagens para um sistema de reconhecimento de imagens de faces humanas é a transformação do domínio. A principal vantagem de uma transformação é a sua efetiva caracterização das propriedades locais da imagem. As wavelets diferenciam-se das tradicionais técnicas de Fourier pela forma de localizar a informação no plano tempo-freqüência; basicamente, têm a capacidade de mudar de uma resolução para outra, o que as fazem especialmente adequadas para análise, representando o sinal em diferentes bandas de freqüências, cada uma com resoluções distintas correspondentes a cada escala. As wavelets foram aplicadas com sucesso na compressão, melhoria, análise, classificação, caracterização e recuperação de imagens. Uma das áreas beneficiadas onde essas propriedades tem encontrado grande relevância é a área de visão computacional, através da representação e descrição de imagens. Este trabalho descreve uma abordagem para o reconhecimento de imagens de faces humanas com a extração de características baseado na decomposição multiresolução de wavelets utilizando os filtros de Haar, Daubechies, Biorthogonal, Reverse Biorthogonal, Symlet, e Coiflet. Foram testadas em conjunto as técnicas PCA (Principal Component Analysis) e IMPCA (Image Principal Component Analysis), sendo que os melhores resultados foram obtidos utilizando a wavelet Biorthogonal com a técnica IMPCA / Image pattern recognition is an interesting area in the scientific world. The features extraction method refers to the ability to extract features from images, reduce the dimensionality and generates the features vector. Given a query image, the goal of a features extraction system is to search the database and return the most similar to the query image according to a given criteria. Our research addresses the generation of features vectors of a recognition image system for human faces databases. A feature vector is a numeric representation of an image or part of it over its representative aspects. The feature vector is a n-dimensional vector organizing such values. This new image representation can be stored into a database and allow a fast image retrieval. An alternative for image characterization for a human face recognition system is the domain transform. The principal advantage of a transform is its effective characterization for their local image properties. In the past few years researches in applied mathematics and signal processing have developed practical wavelet methods for the multi scale representation and analysis of signals. These new tools differ from the traditional Fourier techniques by the way in which they localize the information in the time-frequency plane; in particular, they are capable of trading on type of resolution for the other, which makes them especially suitable for the analysis of non-stationary signals. The wavelet transform is a set basis function that represents signals in different frequency bands, each one with a resolution matching its scale. They have been successfully applied to image compression, enhancement, analysis, classification, characterization and retrieval. One privileged area of application where these properties have been found to be relevant is computer vision, especially human faces imaging. In this work we describe an approach to image recognition for human face databases focused on feature extraction based on multiresolution wavelets decomposition, taking advantage of Biorthogonal, Reverse Biorthogonal, Symlet, Coiflet, Daubechies and Haar. They were tried in joint the techniques together the PCA (Principal Component Analysis) and IMPCA (Image Principal Component Analysis)

Diploma thesis "Prague Spring from the point of view of the transition theory" deals with the time period of "Prague Spring" and briefly also about the following year. The application of the methods of transition tries to show the next development of Czechoslovakia in case if the development hasn't had stopped by the intervention of troops of Warsaw Pact. There is a brief development of transitology there and then; three transition theories from significant authors discussing this topic are mentioned there. Further, the attitudes of reformers to the society in the year 1968 in Czechoslovakia, changes in the society itself and in the Communist Party are investigated there. A very important fact is the international situation and influence of states of the Soviet Block to reviving process and it representatives. The main chapter offers the possible future development of Czechoslovakia after the implementation transition theories. In the last part events from the year 1969 are mentioned. Two public polls bring closer the perception of Prague Spring after 25 and 30 years and its importance for the transition to democracy in 1989.

This thesis considers the problematic of the object recognition in a digital picture, particularly about the human face recognition and its components. There are described the basics of the computer vision, the object detector Viola-Jones, its computer realization with help of the OpenCV libraries and the test results. This thesis also describes the accurate system of the facial features detection per the algorithm of the Active Shape Models and also related mechanism of the classifier training, including the software implementation.

As the amount of computer mediated information (e.g., emails, documents, multi-media) we need to process grows, our need to rapidly sort, organize and store electronic information likewise increases. In order to store information effectively, we must find ways to sort through it and organize it in a manner that facilitates efficient retrieval. The instantaneous and emergent nature of communications across networks like Twitter makes them suitable for discussing events (e.g., natural disasters) that are amorphous and prone to rapid changes. It can be difficult for an individual human to filter through and organize the large amounts of information that can pass through these types of social networks when events are unfolding rapidly. A common feature of social networks is the images (e.g., human faces, inanimate objects) that are often used by those who send messages across these networks. Humans have a particularly strong ability to recognize and differentiate between human Faces. This effect may also extend to recalling information associated with each human Face. This study investigated the difference between human Face images, non-human Face images and alphanumeric labels as retrieval cues under different levels of Task Load. Participants were required to recall key pieces of event information as they emerged from a Twitter-style message feed during a simulated natural disaster. A counter-balanced within-subjects design was used for this experiment. Participants were exposed to low, medium and high Task Load while responding to five different types of recall cues: (1) Nickname, (2) Non-Face, (3) Non-Face & Nickname, (4) Face and (5) Face & Nickname. The task required participants to organize information regarding emergencies (e.g., car accidents) from a Twitter-style message feed. The messages reported various events such as fires occurring around a fictional city. Each message was associated with a different recall cue type, depending on the experimental condition. Following the task, participants were asked to recall the information associated with one of the cues they worked with during the task. Results indicate that under medium and high Task Load, both Non-Face and Face retrieval cues increased recall performance over Nickname alone with Non-Faces resulting in the highest mean recall scores. When comparing medium to high Task Load: Face & Nickname and Non-Face significantly outperformed the Face condition. The performance in Non-Face & Nickname was significantly better than Face & Nickname. No significant difference was found between Non-Faces and Non-Faces & Nickname. Subjective Task Load scores indicate that participants experienced lower mental workload when using Non-Face cues than using Nickname or Face cues. Generally, these results indicate that under medium and high Task Load levels, images outperformed alphanumeric nicknames, Non-Face images outperformed Face images, and combining alphanumeric nicknames with images may have offered a significant performance advantage only when the image is that of a Face. Both theoretical and practical design implications are provided from these findings.