Neste trabalho é apresentado um método de extração de características biométricas da íris de seres humanos. O trabalho proposto baseia-se na análise e extração das características de textura da íris. Durante a identificação, a imagem de um olho de uma pessoa é processada e a posição da íris é determinada através da técnica da transformada de Hough para círculos. Na sequência, as informações de textura da íris são extraídas através de uma abordagem estatística de segunda ordem, utilizando as características de Haralick como parâmetros de classificação. As informações obtidas são armazenadas em um vetor de características, usando-se uma métrica, através da distância Euclidiana normalizada, para o reconhecimento. / This work presents a biometric method for features extraction from human iris. The proposed work is based on iris texture features analysis and extraction. The eye image is preprocessed and the iris localization is found using the Hough transform for circles. The iris features information is extracted by a second order statistical approach, using the Haralick\'s texture features as classification parameters. After that, the information obtained is saved in a features vector, and the recognition is done using a measure, through the normalized Euclidian distance.

Neste trabalho é apresentado um método de extração de características biométricas da íris de seres humanos. O trabalho proposto baseia-se na análise e extração das características de textura da íris. Durante a identificação, a imagem de um olho de uma pessoa é processada e a posição da íris é determinada através da técnica da transformada de Hough para círculos. Na sequência, as informações de textura da íris são extraídas através de uma abordagem estatística de segunda ordem, utilizando as características de Haralick como parâmetros de classificação. As informações obtidas são armazenadas em um vetor de características, usando-se uma métrica, através da distância Euclidiana normalizada, para o reconhecimento. / This work presents a biometric method for features extraction from human iris. The proposed work is based on iris texture features analysis and extraction. The eye image is preprocessed and the iris localization is found using the Hough transform for circles. The iris features information is extracted by a second order statistical approach, using the Haralick\'s texture features as classification parameters. After that, the information obtained is saved in a features vector, and the recognition is done using a measure, through the normalized Euclidian distance.

En fait, le concept de texture n’est pas facile à définir, mais il est clair qu’il est fortement lié au Système Visuel Humain. Sachant que le Modèle LIP est compatible avec la vision humaine, il nous a semblé intéressant de créer des outils logarithmiques dédiés à l’évaluation de la texture. Nous nous sommes concentrés sur la notion de covariogramme, qui peut être pilotée par diverses métriques logarithmiques. Ces métriques jouent le rôle d’outils de “corrélation”, avec l’avantage de prendre en compte la vision humaine. De plus, les outils LIP sont peu dépendants des conditions d’éclairement et fournissent donc des résultats robustes si celles-ci varient. Les deux derniers Chapitres proposent une nouvelle approche consistant à considérer les niveaux de gris d’une image comme les phases d’un milieu. Chaque phase permet de simuler la percolation d’un liquide dans le milieu, définissant ainsi des trajectoires de percolation. Chaque propagation d’un pixel à un autre est considérée comme facile ou non, en fonction des niveaux de gris traversés. Une « fonction de coût » est créée, qui modifie le « temps » de propagation d’un point à l’autre. De plus, la fonction de coût peut être calculée dans le contexte LIP, pour prendre en compte la vision humaine / This thesis looks at the evaluation of textures in two different perspectives using logarithmic image processing (LIP) framework. The first case after introducing the concept of textures and giving some classical approaches of textures evaluation, it gives an original approach of textures evaluation called covariogram which is derived from similarity metrics like distances or correlations etc. The classical covariogram which is derived from the classical similarity metrics and LIP covariogram are then applied over several images and the efficiency of the LIP one is clearly shown for darkened images. The last two chapters offer a new approach by considering the gray levels of an image as the phases of a medium. Each phase simulates like a percolation of a liquid in a medium defining the percolation trajectories. The propagation from one pixel to another is taken as easy or difficult determined by the difference of the gray level intensities. Finally different parameters like fractality from fractal dimensions, mean histogram etc associated to these trajectories are derived, based on which the primary experiment for the classification of random texture is carried out determining the relevance of this idea. Obviously, our study is only first approach and requires additional workout to obtain a reliable method of classification

Pattern recognition is an important area with potential applications in computer vision, Speech understanding, knowledge engineering, bio-medical data classification, earth sciences, life sciences, economics, psychology, linguistics, etc. Clustering is an unsupervised classification process corning under the area of pattern recognition. There are two types of clustering approaches: 1) Non-hierarchical methods 2) Hierarchical methods. Non-hierarchical algorithms are iterative in nature and. perform well in the context of isotropic clusters. Time-complexity of these algorithms is order of (0 (n) ) and above, Hierarchical agglomerative algorithms, on the other hand, are effective when clusters are non-isotropic. The single linkage method of hierarchical category produces a dendrogram which corresponds to the minimal spanning tree, conventional approaches are time consuming requiring O (n2 ) computational time. In this thesis we propose an intelligent partitioning scheme for generating the minimal spanning tree in the co-ordinate space. This is computationally elegant as it avoids the computation of similarity between many pairs of samples me minimal spanning tree generated can be used to produce C disjoint clusters by breaking the (C-1) longest edges in the tree. A systolic architecture has been proposed to increase the speed of the algorithm further. Simulation study has been conducted and the corresponding results are reported. The simulation package has been developed on DEC-1090 in Pascal. It is observed based on the simulation study that the parallel implementation reduces the time enormously. The number of processors required for the parallel implementation is a constant making the approach more attractive. Texture analysis and synthesis has been extensively studied in the context of computer vision, Two important approaches which have been studied extensively by researchers earlier are statistical and structural approaches, Texture is understood to be a periodic pattern with primitive sub patterns repeating in a particular fashion. This has been used to characterize texture with the help of the hierarchical data structure, tree. It is convenient to use a tree data structure as, along with the operations like merging, splitting, deleting a node, adding a node, etc, .it would be useful to handle a periodic pattern. Various functions like angular second moment, correlation etc, which are used to characterize texture have been translated into the new language of hierarchical data structure.

This thesis looks at the evaluation of textures in two different perspectives using logarithmic image processing (LIP) framework. The first case after introducing the concept of textures and giving some classical approaches of textures evaluation, it gives an original approach of textures evaluation called covariogram which is derived from similarity metrics like distances or correlations etc. The classical covariogram which is derived from the classical similarity metrics and LIP covariogram are then applied over several images and the efficiency of the LIP one is clearly shown for darkened images. The last two chapters offer a new approach by considering the gray levels of an image as the phases of a medium. Each phase simulates like a percolation of a liquid in a medium defining the percolation trajectories. The propagation from one pixel to another is taken as easy or difficult determined by the difference of the gray level intensities. Finally different parameters like fractality from fractal dimensions, mean histogram etc associated to these trajectories are derived, based on which the primary experiment for the classification of random texture is carried out determining the relevance of this idea. Obviously, our study is only first approach and requires additional workout to obtain a reliable method of classification