Les gliomes sont des tumeurs cérébrales infiltrantes difficilement curables, notamment à cause de la difficulté à visualiser toutes les infiltrations au bloc opératoire. Dans cette thèse, nous réalisons une étude clinique de spectroscopie de fluorescence de la protoporphyrine IX (PpIX) dans les gliomes de 10 patients selon l’hypothèse que les spectres collectés proviennent de la contribution de 2 états de la PpIX dont les proportions varient suivant la densité en cellules tumorales. Après avoir présenté le développement du système interventionnel proposant une excitation multi-longueurs d’onde, nous présentons son utilisation sur fantômes de PpIX mimant les propriétés des gliomes. Ceci permet tout d’abord d’obtenir les spectres émis par les 2 états séparément puis de proposer un modèle d’ajustement des spectres comme une combinaison linéaire des 2 spectres de référence sur la bande spectrale 608-637 nm. Ensuite, nous présentons la mise en place de l’étude clinique, notamment l’analyse de risques, avant d’appliquer ce système in vivo. Les mesures in vivo détectent de la fluorescence dans des tissus où le microscope chirurgical n’en détecte pas, ce qui pourrait s’expliquer par un changement d’état de la PpIX entre le cœur des gliomes et leurs infiltrations. L’intérêt de l’excitation multi-longueurs d’onde est démontré par la décroissance de la corrélation des spectres acquis aux trois excitations suivant la densité en cellules tumorale. Enfin, nous soulevons des pistes d’étude de l’identification peropératoire des zones de fonctionnalité cérébrale à l’aide d’une caméra optique ainsi que l’étude du temps de vie de fluorescence et de la fluorescence deux photons de la PpIX sur fantômes / Gliomas are infiltrative tumors of the brain which are yet hardly curable, notably because of the difficulty to precisely delimitate their margins during surgery. Intraoperative 5-ALA induced protoporphyrin IX (PpIX) fluorescence microscopy has shown its relevance to assist neurosurgeons but lacks sensitivity. In this thesis, we perform a spectroscopic clinical trial on 10 patients with the assumption that collected fluorescence is a linear combination of the contribution of two states of PpIX which proportions vary with the density of tumor cells. This work starts with the development of the intraoperative, portable and real time fluorescence spectroscopic device that provides multi-wavelength excitation. Then, we show its use on PpIX phantoms with tissues mimicking properties. This first enables to obtain a reference emitted spectrum for each state apart and then permits the development of a fitting model to adjust any emitted spectrum as a linear combination of the references in the spectral band 608-637 nm. Next, we present the steps led to get approvals for the clinical trial, especially the risk analysis. In vivo data analysis is then presented, showing that we detect fluorescence where current microscopes cannot, which could exhibit a change in PpIX state from glioma center to its margins. Besides, the relevance of multi-wavelength excitation is highlighted as the correlation between the three measured spectra of a same sample decreases with the density of tumor cells. Finally, the complementary need to intraoperatively identify cerebral functional areas is tackled with optical measurements as a perspective and other properties of PpIX on phantoms are also raised

Content Based Image Retrieval (CBIR) is an important research area in the field of multimedia information retrieval. The application of CBIR in the medical domain has been attempted before, however the use of CBIR in medical diagnostics is a daunting task. The goal of diagnostic medical image retrieval is to provide diagnostic support by displaying relevant past cases, along with proven pathologies as ground truths. Moreover, medical image retrieval can be extremely useful as a training tool for medical students and residents, follow-up studies, and for research purposes. Despite the presence of an impressive amount of research in the area of CBIR, its acceptance for mainstream and practical applications is quite limited. The research in CBIR has mostly been conducted as an academic pursuit, rather than for providing the solution to a need. For example, many researchers proposed CBIR systems where the image database consists of images belonging to a heterogeneous mixture of man-made objects and natural scenes while ignoring the practical uses of such systems. Furthermore, the intended use of CBIR systems is important in addressing the problem of "Semantic Gap". Indeed, the requirements for the semantics in an image retrieval system for pathological applications are quite different from those intended for training and education. Moreover, many researchers have underestimated the level of accuracy required for a useful and practical image retrieval system. The human eye is extremely dexterous and efficient in visual information processing; consequently, CBIR systems should be highly precise in image retrieval so as to be useful to human users. Unsurprisingly, due to these and other reasons, most of the proposed systems have not found useful real world applications. In this dissertation, an attempt is made to address the challenging problem of developing a retrieval system for medical diagnostics applications. More specifically, a system for semantic retrieval of Magnetic Resonance (MR) images in 3D brain volumes is proposed. The proposed retrieval system has a potential to be useful for clinical experts where the human eye may fail. Previously proposed systems used imprecise segmentation and feature extraction techniques, which are not suitable for precise matching requirements of the image retrieval in this application domain. This dissertation uses multiscale representation for image retrieval, which is robust against noise and MR inhomogeneity. In order to achieve a higher degree of accuracy in the presence of misalignments, an image registration based retrieval framework is developed. Additionally, to speed-up the retrieval system, a fast discrete wavelet based feature space is proposed. Further improvement in speed is achieved by semantically classifying of the human brain into various "Semantic Regions", using an SVM based machine learning approach. A novel and fast identification system is proposed for identifying a 3D volume given a 2D image slice. To this end, we used SVM output probabilities for ranking and identification of patient volumes. The proposed retrieval systems are tested not only for noise conditions but also for healthy and abnormal cases, resulting in promising retrieval performance with respect to multi-modality, accuracy, speed and robustness. This dissertation furnishes medical practitioners with a valuable set of tools for semantic retrieval of 2D images, where the human eye may fail. Specifically, the proposed retrieval algorithms provide medical practitioners with the ability to retrieve 2D MR brain images accurately and monitor the disease progression in various lobes of the human brain, with the capability to monitor the disease progression in multiple patients simultaneously. Additionally, the proposed semantic classification scheme can be extremely useful for semantic based categorization, clustering and annotation of images in MR brain databases. This research framework may evolve in a natural progression towards developing more powerful and robust retrieval systems. It also provides a foundation to researchers in semantic based retrieval systems on how to expand existing toolsets for solving retrieval problems.

Content Based Image Retrieval (CBIR) is an important research area in the field of multimedia information retrieval. The application of CBIR in the medical domain has been attempted before, however the use of CBIR in medical diagnostics is a daunting task. The goal of diagnostic medical image retrieval is to provide diagnostic support by displaying relevant past cases, along with proven pathologies as ground truths. Moreover, medical image retrieval can be extremely useful as a training tool for medical students and residents, follow-up studies, and for research purposes. Despite the presence of an impressive amount of research in the area of CBIR, its acceptance for mainstream and practical applications is quite limited. The research in CBIR has mostly been conducted as an academic pursuit, rather than for providing the solution to a need. For example, many researchers proposed CBIR systems where the image database consists of images belonging to a heterogeneous mixture of man-made objects and natural scenes while ignoring the practical uses of such systems. Furthermore, the intended use of CBIR systems is important in addressing the problem of "Semantic Gap". Indeed, the requirements for the semantics in an image retrieval system for pathological applications are quite different from those intended for training and education. Moreover, many researchers have underestimated the level of accuracy required for a useful and practical image retrieval system. The human eye is extremely dexterous and efficient in visual information processing; consequently, CBIR systems should be highly precise in image retrieval so as to be useful to human users. Unsurprisingly, due to these and other reasons, most of the proposed systems have not found useful real world applications. In this dissertation, an attempt is made to address the challenging problem of developing a retrieval system for medical diagnostics applications. More specifically, a system for semantic retrieval of Magnetic Resonance (MR) images in 3D brain volumes is proposed. The proposed retrieval system has a potential to be useful for clinical experts where the human eye may fail. Previously proposed systems used imprecise segmentation and feature extraction techniques, which are not suitable for precise matching requirements of the image retrieval in this application domain. This dissertation uses multiscale representation for image retrieval, which is robust against noise and MR inhomogeneity. In order to achieve a higher degree of accuracy in the presence of misalignments, an image registration based retrieval framework is developed. Additionally, to speed-up the retrieval system, a fast discrete wavelet based feature space is proposed. Further improvement in speed is achieved by semantically classifying of the human brain into various "Semantic Regions", using an SVM based machine learning approach. A novel and fast identification system is proposed for identifying a 3D volume given a 2D image slice. To this end, we used SVM output probabilities for ranking and identification of patient volumes. The proposed retrieval systems are tested not only for noise conditions but also for healthy and abnormal cases, resulting in promising retrieval performance with respect to multi-modality, accuracy, speed and robustness. This dissertation furnishes medical practitioners with a valuable set of tools for semantic retrieval of 2D images, where the human eye may fail. Specifically, the proposed retrieval algorithms provide medical practitioners with the ability to retrieve 2D MR brain images accurately and monitor the disease progression in various lobes of the human brain, with the capability to monitor the disease progression in multiple patients simultaneously. Additionally, the proposed semantic classification scheme can be extremely useful for semantic based categorization, clustering and annotation of images in MR brain databases. This research framework may evolve in a natural progression towards developing more powerful and robust retrieval systems. It also provides a foundation to researchers in semantic based retrieval systems on how to expand existing toolsets for solving retrieval problems.

Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

Image registration is a fundamental problem that can be found in a diverse range of fields within the research community. It is used in areas such as engineering, science, medicine, robotics, computer vision and image processing, which often require the process of developing a spatial mapping between sets of data. Registration plays a crucial role in the medical imaging field where continual advances in imaging modalities, including MRI, CT and PET, allow the generation of 3D images that explicitly outline detailed in vivo information of not only human anatomy, but also human function. Mutual Information (MI) is a popular entropy-based similarity measure which has found use in a large number of image registration applications. Stemming from information theory, this measure generally outperforms most other intensity-based measures in multimodal applications as it does not assume the existence of any specific relationship between image intensities. It only assumes a statistical dependence. The basic concept behind any approach using MI is to find a transformation, which when applied to an image, will maximise the MI between two images. This thesis presents research using MI in three major topics encompassed by the computer vision and medical imaging field: rigid image registration, stereo vision, and non-rigid image registration. In the rigid domain, a novel gradient-based registration algorithm (MIGH) is proposed that uses Parzen windows to estimate image density functions and Gauss-Hermite quadrature to estimate the image entropies. The use of this quadrature technique provides an effective and efficient way of estimating entropy while bypassing the need to draw a second sample of image intensities (a procedure required in previous Parzen-based MI registration approaches). It is possible to achieve identical results with the MIGH algorithm when compared to current state of the art MI-based techniques. These results are achieved using half the previously required sample sizes, thus doubling the statistical power of the registration algorithm. Furthermore, the MIGH technique improves algorithm complexity by up to an order of N, where N represents the number of samples extracted from the images. In stereo vision, a popular passive method of depth perception, new extensions have been pro- posed in order to increase the robustness of MI-based stereo matching algorithms. Firstly, prior probabilities are incorporated into the MI measure to considerably increase the statistical power of the matching windows. The statistical power, directly related to the number of samples, can become too low when small matching windows are utilised. These priors, which are calculated from the global joint histogram, are tuned to a two level hierarchical approach. A 2D match surface, in which the match score is computed for every possible combination of template and matching windows, is also utilised to enforce left-right consistency and uniqueness constraints. These additions to MI-based stereo matching significantly enhance the algorithms ability to detect correct matches while decreasing computation time and improving the accuracy, particularly when matching across multi-spectra stereo pairs. MI has also recently found use in the non-rigid domain due to a need to compute multimodal non-rigid transformations. The viscous fluid algorithm is perhaps the best method for re- covering large local mis-registrations between two images. However, this model can only be used on images from the same modality as it assumes similar intensity values between images. Consequently, a hybrid MI-Fluid algorithm is proposed to compute a multimodal non-rigid registration technique. MI is incorporated via the use of a block matching procedure to generate a sparse deformation field which drives the viscous fluid algorithm, This algorithm is also compared to two other popular local registration techniques, namely Gaussian convolution and the thin-plate spline warp, and is shown to produce comparable results. An improved block matching procedure is also proposed whereby a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler is used to optimally locate grid points of interest. These grid points have a larger concentration in regions of high information and a lower concentration in regions of small information. Previous methods utilise only a uniform distribution of grid points throughout the image.

Σκοπός της παρούσας διδακτορικής διατριβής είναι η ανάπτυξη αλγορίθμων επεξεργασίας ιατρικής εικόνας για την ενσωμάτωση τους σε ιατρικές εφαρμογές ακτινοθεραπευτικού ενδιαφέροντος. Οι αλγόριθμοι αυτοί στηρίζονται στην αρχή λειτουργίας των χαρτών αυτο-οργάνωσης Kohonen και αξιοποιούν την πληροφορία που περιέχεται σε περιοχές των εικόνων γύρω από σημεία ενδιαφέροντος, ώστε να εντοπίσουν αυτόματα, με ακρίβεια και αξιοπιστία, αντιστοιχίες μεταξύ των εικόνων. Πιο συγκεκριμένα, ένας επαναληπτικός αλγόριθμος προτείνεται για την αυτόματη εύρεση αντίστοιχων σημείων σε ιατρικές εικόνες δύο διαστάσεων. Ο προτεινόμενος αλγόριθμος προϋποθέτει την εύρεση σημείων ενδιαφέροντος μόνο στη μια από τις δύο εικόνες και εντοπίζει τα αντίστοιχα σημεία στη δεύτερη εικόνα μέσα από μια επαναληπτική διαδικασία, η οποία προσομοιάζει τη φάση εκπαίδευσης του νευρωνικού δικτύου. Με βάση τα ζεύγη των αντίστοιχων σημείων, υπολογίζονται στη συνέχεια οι παράμετροι ενός μετασχηματισμού, κατάλληλου για να περιγράψει τη σχέση μεταξύ των δεδομένων εικόνων. Ο αλγόριθμος ευθυγράμμισης εφαρμόζεται σε δεδομένες εικόνες ηλεκτρονικής πυλαίας απεικόνισης (Electronic Portal Images), που λαμβάνονται πριν από κάθε συνεδρία της ακτινοθεραπείας, για τον υπολογισμό του σφάλματος τοποθέτησης του ασθενούς. Το ζήτημα της επαλήθευσης της θέσης του ασθενούς στην ακτινοθεραπεία αντιμετωπίζεται επίσης με τη βοήθεια μιας αυτόματης μεθόδου εύρεσης αντίστοιχων σημείων σε τρισδιάστατα δεδομένα, η οποία εφαρμόζεται για την ευθυγράμμιση της αξονικής τομογραφίας του σχεδιασμού της ακτινοθεραπείας και μιας αξονικής τομογραφίας επαλήθευσης, που λαμβάνεται πριν την πρώτη συνεδρία της ακτινοθεραπείας. Ο προτεινόμενος αλγόριθμος εντοπίζει αντίστοιχα σημεία ενδιαφέροντος στις δεδομένες τομογραφικές εικόνες και υπολογίζει τις παραμέτρους ενός μη γραμμικού μετασχηματισμού ευθυγράμμισης. Μετά την ευθυγράμμιση των δύο τομογραφιών, υπολογίζεται η μετατόπιση του ισοκέντρου στην τομογραφία επαλήθευσης σε σχέση με τη θέση του ισοκέντρου που προβλέπεται στην αρχική τομογραφία του σχεδιασμού. Με την ενσωμάτωση αυτής της μεθόδου ευθυγράμμισης στη διαδικασία της ακτινοθεραπείας, ικανοποιούνται δύο ανάγκες της κλινικής πρακτικής. Αφενός, η μετατόπιση του ισοκέντρου, όπως υπολογίζεται από την προτεινόμενη μέθοδο, παρέχει μια αξιόπιστη ένδειξη για τη μετατόπιση του ασθενούς που απαιτείται πριν τη χορήγηση της ακτινοβολίας. Αφετέρου, επιχειρείται η καλύτερη αξιοποίηση των πόρων του τμήματος της ακτινοθεραπείας με τη διαδικασία της εύρεσης του ισοκέντρου της ακτινοθεραπείας να λαμβάνει χώρα στην αίθουσα του αξονικού τομογράφου και να μειώνεται συνεπώς ο χρόνος που απαιτείται για την προετοιμασία του ασθενούς στον γραμμικό επιταχυντή κατά την πρώτη συνεδρία της ακτινοθεραπείας. / Aim of the present thesis is the development of image processing algorithms for radiotherapy applications. These algorithms are based on the principles of Kohonen Self Organizing Maps and exploit the information contained in image regions around distinctive points of interest, in order to determine image correspondences in an automatic, accurate and robust way. In particular, an iterative algorithm is proposed for automatic detection of point correspondences in two-dimensional medical images. The proposed algorithm requires the extraction of interest points only in one image and detects the homologous points in the second image through an iterative procedure, respective to the training phase of a neural network. Subsequently, the parameters of an appropriate registration transformation are computed to describe the mapping between the two images. The computation is based on the detected point correspondence. The proposed registration algorithm is applied to Electronic Portal Images, acquired prior to the radiotherapy treatment delivery, in order to estimate the setup error of the patient. The issue of patient position verification in radiotherapy is also addressed in the present thesis by developing an algorithm for automatic detection of point correspondences in three-dimensional medical data. The algorithm is used to register the CT data of radiotherapy planning to an additional verification CT, acquired prior to the first treatment fraction. The proposed algorithm detects corresponding points in the two CT images and computes the parameters of a non-rigid registration transformation. After the registration of the two CT images, the isocenter displacement of the verification CT is calculated with respect to the ideal isocenter position, defined in the planning CT. By integrating the proposed registration procedure in the clinical practice, two needs are met. Firstly, the isocenter displacement, calculated by the proposed method, provides a reliable indication of the patient shift, needed before the treatment delivery, for optimization of the dose delivery. Secondly, an improvement of the radiotherapy department efficiency is attempted by performing the procedure of isocenter marking in the CT scanner room and, consequently, reducing the time expenditure of the patient in the LINAC during the first radiotherapy fraction.

Fúze obrazu je dnes jednou z nejběžnějších avšak stále velmi diskutovanou oblastí v lékařském zobrazování a hraje důležitou roli ve všech oblastech lékařské péče jako je diagnóza, léčba a chirurgie. V této dizertační práci jsou představeny tři projekty, které jsou velmi úzce spojeny s oblastí fúze medicínských dat. První projekt pojednává o 3D CT subtrakční angiografii dolních končetin. V práci je využito kombinace kontrastních a nekontrastních dat pro získání kompletního cévního stromu. Druhý projekt se zabývá fúzí DTI a T1 váhovaných MRI dat mozku. Cílem tohoto projektu je zkombinovat stukturální a funkční informace, které umožňují zlepšit znalosti konektivity v mozkové tkáni. Třetí projekt se zabývá metastázemi v CT časových datech páteře. Tento projekt je zaměřen na studium vývoje metastáz uvnitř obratlů ve fúzované časové řadě snímků. Tato dizertační práce představuje novou metodologii pro klasifikaci těchto metastáz. Všechny projekty zmíněné v této dizertační práci byly řešeny v rámci pracovní skupiny zabývající se analýzou lékařských dat, kterou vedl pan Prof. Jiří Jan. Tato dizertační práce obsahuje registrační část prvního a klasifikační část třetího projektu. Druhý projekt je představen kompletně. Další část prvního a třetího projektu, obsahující specifické předzpracování dat, jsou obsaženy v disertační práci mého kolegy Ing. Romana Petera.

Nous décrivons une procédure numérique pour le recalage d'IRM cérébrales 3D. Le problème d'appariement est abordé à travers la distinction usuelle entre le modèle de déformation et le critère d'appariement. Le modèle de déformation est celui de l'anatomie computationnelle, fondé sur un groupe de difféomorphismes engendrés en intégrant des champs de vecteurs. Le décalage entre les images est évalué en comparant les lignes de niveau de ces images, représentées par un courant différentiel dans le dual d'un espace de champs de vecteurs. Le critère d'appariement obtenu est non local et rapide à calculer. On se place dans l'ensemble des difféomorphismes pour rechercher une déformation reliant les deux images. Pour cela, on minimise le critère en suivant le principe de l'algorithme sous-optimal. L'efficacité de l'algorithme est renforcée par une description eulérienne et périodique du mouvement. L'algorithme est appliqué pour le recalage d'images IRM cérébrale 3d, la procédure numérique menant à ces résultats est intégralement décrite. Nos travaux concernent aussi l'analyse des propriétés de l'algorithme. Pour cela, nous avons simplifié l'équation représentant l'évolution de l'image et étudié l'équation simplifiée en utilisant la théorie des solutions de viscosité. Nous étudions aussi le problème de détection de rupture dans la variance d'un signal aléatoire gaussien. La spécificité de notre modèle vient du cadre infill, ce qui signifie que la distribution des données dépend de la taille de l'échantillon. L'estimateur de l'instant de rupture est défini comme le point maximisant une fonction de contraste. Nous étudions la convergence de cette fonction et ensuite la convergence de l'estimateur associé. L'application la plus directe concerne l'estimation de changement dans le paramètre de Hurst d'un mouvement brownien fractionnaire. L'estimateur dépend d'un paramètre p > 0 et nos résultats montrent qu'il peut être intéressant de choisir p < 2. / We present a numerical procedure for the matching of 3D MRI. The problem of image matching is addressed through the usual distinction between the deformation model and the matching criterion. The deformation model is based on the theory of computational anatomy and the set of deformations is a group of diffeomorphisms generated by integrating vector fields. The discrepancy between the two images is evaluated through comparisons of level lines represented by a differential current in the dual of a space of vector fields. This representation leads to a quickly computable non-local criterion. Then, the optimisation method is based on the minimization of the criterion following the idea of the so-called sub-optimal algorithm. We take advantage of the eulerian and periodical description of the algorithm to get an efficient numerical procedure. This algorithm can be used to deal with 3d MR images and numerical experiences are presented. In an other part, we focus on theoretical properties of the algorithm. We begin by simplifying the equation representing the evolution of the deformed image and we use the theory of viscosity solutions to study the simplified equation. The second issue we are interested in is the change-point estimation for a gaussian sequence with change in the variance parameter. The main feature of our model is that we work with infill data and the nature of the data can evolve jointly with the size of the sample. The usual approach suggests to introduce a contrast function and using the point of its maximum as a change-point estimator. We first get an information about the asymptotic fluctuations of the contrast function around its mean function. Then, we focus on the change-point estimator and more precisely on the convergence of this estimator. The most direct application concerns the detection of change in the Hurst parameter of a fractional brownian motion. The estimator depends on a parameter p > 0, generalizing the usual choice p = 2. We present some results illustrating the advantage of a parameter p < 2.

Etude bibliographique sur le recalage d'images de visage et sur le recalage d'images et travail en collaboration avec Son VuS, pour définir la précision nécessaire du recalage en fonction des exigences des méthodes de reconnaissance de visages. / Face alignment is an important step in a typical automatic face recognition system.This thesis addresses the alignment of faces for face recognition applicationin video surveillance context. The main challenging factors of this research includethe low quality of images (e.g., low resolution, motion blur, and noise), uncontrolledillumination conditions, pose variations, expression changes, and occlusions. In orderto deal with these problems, we propose several face alignment methods using differentstrategies. The _rst part of our work is a three-stage method for facial pointlocalization which can be used for correcting mis-alignment errors. While existingalgorithms mostly rely on a priori knowledge of facial structure and on a trainingphase, our approach works in an online mode without requirements of pre-de_nedconstraints on feature distributions. The proposed method works well on images underexpression and lighting variations. The key contributions of this thesis are aboutjoint image alignment algorithms where a set of images is simultaneously alignedwithout a biased template selection. We respectively propose two unsupervised jointalignment algorithms : \Lucas-Kanade entropy congealing" (LKC) and \gradient correlationcongealing" (GCC). In LKC, an image ensemble is aligned by minimizing asum-of-entropy function de_ned over all images. GCC uses gradient correlation coef-_cient as similarity measure. The proposed algorithms perform well on images underdi_erent conditions. To further improve the robustness to mis-alignments and thecomputational speed, we apply a multi-resolution framework to joint face alignmentalgorithms. Moreover, our work is not limited in the face alignment stage. Since facealignment and face acquisition are interrelated, we develop an adaptive appearanceface tracking method with alignment feedbacks. This closed-loop framework showsits robustness to large variations in target's state, and it signi_cantly decreases themis-alignment errors in tracked faces.

Orientador: João Antonio Pereira / Resumo: O objetivo específico deste trabalho é estender as capacidades da técnica de rastreamento de pontos em 3 dimensões (three-dimensional point tracking – 3DPT) para identificar as características dinâmicas de estruturas grandes e complexas, tais como pás de turbina eólica. Um sistema multi-camera (composto de múltiplos sistemas de estéreo visão calibrados independentemente) é desenvolvido para obter alta resolução espacial de pontos discretos a partir de medidas de deslocamento sobre grandes áreas. Uma proposta de técnica de costura é apresentada e empregada para executar o alinhamento de duas nuvens de pontos, obtidas com a técnica 3DPT, de uma estrutura sob excitação dinâmica. Três diferentes algoritmos de registro de nuvens de pontos são propostos para executar a junção das nuvens de pontos de cada sistema estéreo, análise de componentes principais (Principal Component Analysis - PCA), decomposição de valores singulares (Singular value Decomposition - SVD) e ponto mais próximo iterativo (Iterative Closest Point - ICP). Além disso, análise modal operacional em conjunto com o sistema de medição multi-camera e as técnicas de registro de nuvens de pontos são usadas para determinar a viabilidade de usar medidas ópticas (e.g. three-dimensional point tracking – 3DPT) para estimar os parâmetros modais de uma pá de gerador eólico comparando seus resultados com técnicas de medição mais convencionais. / Abstract: The specific objective of this research is to extend the capabilities of three-dimensional (3D) Point Tracking (PT) to identify the dynamic characteristics of large and complex structures, such as utility-scale wind turbine blades. A multi-camera system (composed of multiple independently calibrated stereovision systems) is developed to obtain high spatial resolution of discrete points from displacement measurement over very large areas. A proposal of stitching techniques is presented and employed to perform the alignment of two point clouds, obtained with 3DPT measurement, of a structure under dynamic excitation. The point cloud registration techniques are exploited as a technique for dynamic measuring (displacement) of large structures with high spatial resolution of the model. Three different image registration algorithms are proposed to perform the junction of the points clouds of each stereo system, Principal Component Analysis (PCA), Singular value Decomposition (SVD) and Iterative Closest Point (ICP). Furthermore, operational modal analysis in conjunction with the multi-camera measurement system and registration techniques are used to determine the feasibility of using optical measurements (e.g. three-dimensional point tracking (3DPT)) to estimate the modal parameters of a utility-scale wind turbine blade by comparing with traditional techniques. / Doutor