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A biometria é a ciência que estuda a mensuração dos seres vivos. Muitos trabalhos exploram as características dos seres humanos tais como, impressão digital, íris e face, a fim de desenvolver sistemas biométricos, utilizados em diversas aplicações (monitoramento de segurança, computação ubíqua, robótica). O reconhecimento de faces é uma das técnicas biométricas mais investigadas, por ser bastante intuitiva e menos invasiva que as demais. Alguns trabalhos envolvendo essa técnica se preocupam apenas em localizar a face de um indivíduo (fazer a contagem de pessoas), enquanto outros tentam identificá-lo a partir de uma imagem. Este trabalho propõe uma abordagem capaz de identificar faces a partir de quadros de vídeo e, posteriormente, reconhecê-las por meio de técnicas de análise de imagens. Pode-se dividir o trabalho em dois módulos principais: (1) - Localização e rastreamento de faces em uma seqüência de imagens ( frames), além de separar a região rastreada da imagem; (2) - Reconhecimento de faces, identificando a qual pessoa pertence. Para a primeira etapa foi implementado um sistema de análise de movimento (baseado em subtração de quadros) que possibilitou localizar, rastrear e captar imagens da face de um indivíduo usando uma câmera de vídeo. Para a segunda etapa foram implementados os módulos de redução de informações (técnica Principal Component Analysis - PCA), de extração de características (transformada wavelet de Gabor), e o de classificação e identificação de face (distância Euclidiana e Support Vector Machine - SVM). Utilizando-se duas bases de dados de faces (FERET e uma própria - Própria), foram realizados testes para avaliar o sistema de reconhecimento implementado. Os resultados encontrados foram satisfatórios, atingindo 91,92% e 100,00% de taxa de acertos para as bases FERET e Própria, respectivamente. / Biometry is the science of measuring and analyzing biomedical data. Many works in this field have explored the characteristics of human beings, such as digital fingerprints, iris, and face to develop biometric systems, employed in various aplications (security monitoring, ubiquitous computation, robotic). Face identification and recognition are very apealing biometric techniques, as it it intuitive and less invasive than others. Many works in this field are only concerned with locating the face of an individual (for counting purposes), while others try to identify people from faces. The objective of this work is to develop a biometric system that could identify and recognize faces. The work can be divided into two major stages: (1) Locate and track in a sequence of images (frames), as well as separating the tracked region from the image; (2) Recognize a face as belonging to a certain individual. In the former, faces are captured from frames of a video camera by a motion analysis system (based on substraction of frames), capable of finding, tracking and croping faces from images of individuals. The later, consists of elements for data reductions (Principal Component Analysis - PCA), feature extraction (Gabor wavelets) and face classification (Euclidean distance and Support Vector Machine - SVM). Two faces databases have been used: FERET and a \"home-made\" one. Tests have been undertaken so as to assess the system\'s recognition capabilities. The experiments have shown that the technique exhibited a satisfactory performance, with success rates of 91.97% and 100% for the FERET and the \"home-made\" databases, respectively.

A biometria é a ciência que estuda a mensuração dos seres vivos. Muitos trabalhos exploram as características dos seres humanos tais como, impressão digital, íris e face, a fim de desenvolver sistemas biométricos, utilizados em diversas aplicações (monitoramento de segurança, computação ubíqua, robótica). O reconhecimento de faces é uma das técnicas biométricas mais investigadas, por ser bastante intuitiva e menos invasiva que as demais. Alguns trabalhos envolvendo essa técnica se preocupam apenas em localizar a face de um indivíduo (fazer a contagem de pessoas), enquanto outros tentam identificá-lo a partir de uma imagem. Este trabalho propõe uma abordagem capaz de identificar faces a partir de quadros de vídeo e, posteriormente, reconhecê-las por meio de técnicas de análise de imagens. Pode-se dividir o trabalho em dois módulos principais: (1) - Localização e rastreamento de faces em uma seqüência de imagens ( frames), além de separar a região rastreada da imagem; (2) - Reconhecimento de faces, identificando a qual pessoa pertence. Para a primeira etapa foi implementado um sistema de análise de movimento (baseado em subtração de quadros) que possibilitou localizar, rastrear e captar imagens da face de um indivíduo usando uma câmera de vídeo. Para a segunda etapa foram implementados os módulos de redução de informações (técnica Principal Component Analysis - PCA), de extração de características (transformada wavelet de Gabor), e o de classificação e identificação de face (distância Euclidiana e Support Vector Machine - SVM). Utilizando-se duas bases de dados de faces (FERET e uma própria - Própria), foram realizados testes para avaliar o sistema de reconhecimento implementado. Os resultados encontrados foram satisfatórios, atingindo 91,92% e 100,00% de taxa de acertos para as bases FERET e Própria, respectivamente. / Biometry is the science of measuring and analyzing biomedical data. Many works in this field have explored the characteristics of human beings, such as digital fingerprints, iris, and face to develop biometric systems, employed in various aplications (security monitoring, ubiquitous computation, robotic). Face identification and recognition are very apealing biometric techniques, as it it intuitive and less invasive than others. Many works in this field are only concerned with locating the face of an individual (for counting purposes), while others try to identify people from faces. The objective of this work is to develop a biometric system that could identify and recognize faces. The work can be divided into two major stages: (1) Locate and track in a sequence of images (frames), as well as separating the tracked region from the image; (2) Recognize a face as belonging to a certain individual. In the former, faces are captured from frames of a video camera by a motion analysis system (based on substraction of frames), capable of finding, tracking and croping faces from images of individuals. The later, consists of elements for data reductions (Principal Component Analysis - PCA), feature extraction (Gabor wavelets) and face classification (Euclidean distance and Support Vector Machine - SVM). Two faces databases have been used: FERET and a \"home-made\" one. Tests have been undertaken so as to assess the system\'s recognition capabilities. The experiments have shown that the technique exhibited a satisfactory performance, with success rates of 91.97% and 100% for the FERET and the \"home-made\" databases, respectively.

Αυτή η εργασία μελετά την εφαρμογή προηγμένων τεχνικών επεξεργασίας εικόνας από υπολογιστές για την επίλυση του προβλήματος της ανίχνευσης ατελειών σε υφάσματα από τις βιομηχανίες παραγωγής υφασμάτων. Προτείνεται μία νέα μέθοδος ανίχνευσης ατελειών, η οποία αποτελείται από ένα περιττό συμμετρικό φίλτρο Gabor πραγματικών τιμών, ένα άρτιο συμμετρικό φίλτρο Gabor πραγματικών τιμών και ένα φίλτρο εξομάλυνσης. Κατά την ανάπτυξη της μεθόδου, τα φίλτρα Gabor σχεδιάζονται με βάση τα χαρακτηριστικά του texture που εξάγονται βέλτιστα από μία εικόνα ενός μη ελαττωματικού υφάσματος με τη χρήση ενός Gabor Wavelet Network (GWN). Η απόδοση της προτεινόμενης μεθόδου αξιολογείται με τη χρήση ενός σετ εικόνων υφασμάτων που προέρχονται από μία βάση δεδομένων που περιέχει μία μεγάλη ποικιλία εικόνων ομογενών υφασμάτων. Τα αποτελέσματα παρουσιάζουν ακρίβεια στην ανίχνευση ατελειών με πολύ λίγες λάθος ανιχνεύσεις, από όπου φαίνεται η αποτελεσματικότητα της προτεινόμενης μεθόδου. Τα πειραματικά αποτελέσματα επιβεβαίωσαν τις δυνατότητες της μεθόδου και ένας υπολογισμός του υπολογιστικού φορτίου που χρειάζεται για την υλοποίηση της έδειξε ότι μπορεί να χρησιμοποιηθεί ακόμα και σε συστήματα ανίχνευσης πραγματικού χρόνου. / -

Nano scale processing of IC chips has become the prime production technique as the microelectronic industry aims towards scaling down product dimensions while increasing accuracy and performance. Accurate control of temperature and a good monitoring mechanism for thickness of the deposition layers during epitaxial growth are critical parameters influencing a good yield. The two-fold objective of this thesis is to establish the feasibility of an alternative to the current pyrometric and ellipsometric techniques to simultaneously measure temperature and thickness during wafer processing. TAP-NDE is a non-contact, non-invasive, laser-based ultrasound technique that is employed in this study to contemporarily profile the thermal and spatial characteristics of the wafer. The Gabor wavelet transform allows the wave dispersion to be unraveled and the group velocity of individual frequency components to be extracted from the experimentally acquired time waveform. The thesis illustrates the formulation of a theoretical model that is used to identify the frequencies sensitive to temperature and thickness changes. The group velocity of the corresponding frequency components is determined and their corresponding changes with respect to temperature for different thickness are analytically modeled. TAP-NDE is then used to perform an experimental analysis on Silicon wafers of different thickness to determine the maximum possible resolution of TAP-NDE towards temperature sensitivity, and to demonstrate the ability to differentiate between wafers of different deposition layer thickness at temperatures up to 600?C. Temperature resolution is demonstrated for ?10?C resolution and for ?5?C resolution; while thickness differentiation is carried out with wafers carrying 4000? and 8000? of aluminum deposition layer. The experimental group velocities of a set of selected frequency components extracted using the Gabor Wavelet time-frequency analysis as compared to their corresponding theoretical group velocities show satisfactory agreement. As a result of this work, it is seen that TAP-NDE is a suitable tool to identify and characterize thickness and temperature changes simultaneously during thermal annealing that can replace the current need for separate characterization of these two important parameters in semiconductor manufacturing.

Nano scale processing of IC chips has become the prime production technique as the microelectronic industry aims towards scaling down product dimensions while increasing accuracy and performance. Accurate control of temperature and a good monitoring mechanism for thickness of the deposition layers during epitaxial growth are critical parameters influencing a good yield. The two-fold objective of this thesis is to establish the feasibility of an alternative to the current pyrometric and ellipsometric techniques to simultaneously measure temperature and thickness during wafer processing. TAP-NDE is a non-contact, non-invasive, laser-based ultrasound technique that is employed in this study to contemporarily profile the thermal and spatial characteristics of the wafer. The Gabor wavelet transform allows the wave dispersion to be unraveled and the group velocity of individual frequency components to be extracted from the experimentally acquired time waveform. The thesis illustrates the formulation of a theoretical model that is used to identify the frequencies sensitive to temperature and thickness changes. The group velocity of the corresponding frequency components is determined and their corresponding changes with respect to temperature for different thickness are analytically modeled. TAP-NDE is then used to perform an experimental analysis on Silicon wafers of different thickness to determine the maximum possible resolution of TAP-NDE towards temperature sensitivity, and to demonstrate the ability to differentiate between wafers of different deposition layer thickness at temperatures up to 600?C. Temperature resolution is demonstrated for ?10?C resolution and for ?5?C resolution; while thickness differentiation is carried out with wafers carrying 4000? and 8000? of aluminum deposition layer. The experimental group velocities of a set of selected frequency components extracted using the Gabor Wavelet time-frequency analysis as compared to their corresponding theoretical group velocities show satisfactory agreement. As a result of this work, it is seen that TAP-NDE is a suitable tool to identify and characterize thickness and temperature changes simultaneously during thermal annealing that can replace the current need for separate characterization of these two important parameters in semiconductor manufacturing.

Ce travail de thèse vise à mettre en place un système d’extraction de réseau routier en milieu urbain à partir d’image satellite à très haute résolution. Dans ce contexte, nous avons proposé deux méthodes de localisation de routes. La première approche est fondée sur la procédure de conversion de l’image vers un format vectoriel. L’originalité de cette approche réside dans l’utilisation d’une méthode géométrique pour assurer le passage vers une représentation vectorielle de l’image d’origine et la mise en place d’un formalisme logique fondé sur un ensemble de critères perceptifs permettant le filtrage de l’information inutile et l’extraction des structures linéaires. Dans la deuxième approche, nous avons proposé un algorithme fondé sur la théorie des ondelettes, il met particulièrement en évidence les deux volets multi-résolution et multi-direction. Nous proposons donc une approche de localisation des routes mettant en jeux l'information fréquentielle multi directionnelle issue de la transformée en ondelette Log-Gabor. Dans l’étape de localisation, nous avons présenté deux détecteurs de routes qui exploitent l’information radiométrique, géométrique et fréquentielle. Cependant, ces informations ne permettent pas un résultat exact et précis. Pour remédier à ce problème, un algorithme de suivi s’avère nécessaire. Nous proposons la reconstruction de réseaux routiers par des courbes NURBS. Cette approche est basée sur un ensemble de points de repères identifiés dans la phase de localisation. Elle propose un nouveau concept, que nous avons désigné par NURBSC, basé sur les contraintes géométriques des formes à approximer. Nous connectons les segments de route identifiés afin d’obtenir des tracés continus propres aux routes. / The aim of this thesis is to establish a road network extraction system in urban areas from very high resolution satellite images. In this context, we proposed two approaches to locate roads. The first one is based on the process of converting the image into a vector form. The originality of this approach lies in the use of a geometric method to ensure the shift into a vector representation of the original image and the establishment of a logical formalism based on a set of perceptual criteria. It allows the filtering of unnecessary information and extracting linear structures. In the second approach, we proposed an algorithm based on the wavelet theory, it particularly highlights the two axis multi-resolution and multi-direction. Thus, we introduce a road localization approach, which manage the frequency multidirectional data resulting from the transform using the Log-Gabor wavelet. In the localization step, we presented two road detectors, which are capable of exploiting the radiometric, geometric and frequency data. However, this data cannot allow accurate and precise results. To overcome this drawback, a tracking algorithm is needed. We propose the reconstruction of road networks by NURBS curves. This approach is based on a landmark set of points identified in the localization phase and presents a new concept, noted by NURBSC. NURBSC is based on the geometrical constraints of shapes to be approximated. We connect road segments identified in order to obtain continuous road network.

In my thesis, I focus on the task of recognizing human iris from an image.In the beginning, the work deals with a question of biometrics, its importance and basic concepts, which are necessary for use in following text. Subsequently process of human Iris detection is described together with theory of evolution algorithms. In the implementation part, is described the design of implemented solution, which uses evolution algorithms, where is emphasis on correct pupil and iris boundary detection.