Active binocular vision systems are powerful tools in machine vision. With a virtually unlimited field-of-view they have access to huge amounts of information, yet are able to confine their resources to specific regions of interest. Since they can dynamically interact with the environment, they are able to successfully address problems that are ill-posed to passive systems. A primary goal of an active binocular vision systems is to ascertain depth information. Since they employ two cameras and are able to sample a scene from two distinct vantage points, they are well suited for such a task. The depth recovery process is composed of two interrelated components: image registration and sampling. Image registration is the process of determining corresponding points between the stereo images. Once points in the images have been matched, 3D information can be recovered via triangulation. Image sampling determines how the image is discretized and represented. Image registration and sampling are highly interdependent. The choice of sampling scheme can profoundly impact the accuracy and complexity of the registrations process. In many situations, particular registration algorithms are simply incompatible with some sampling schemes. In this dissertation we meticulously address both registration and sampling in the context of stereopis for active binocular vision systems. Throughout the development of this work, contributions in each area are addressed with an eye toward their eventual integration into a cohesive registration procedure appropriate for active binocular vision systems. The actual synthesis is a daunting task that is beyond the scope of this single dissertation. The focus of this work is to assiduously analyze both registration and sampling, establishing a solid foundation for their future aggregation. One of the most successful approaches to image registration is phase-differencing. Phase-differencing algorithms provide a fast, powerful means for depth recovery. Unfortunately, phase-differencing techniques suffer from two significant impediments: phase nonlinearities and neglect of multispectral information. This dissertation uses the amenable properties of white noise images to analytically quantify the behavior of phase in these regions of phase nonlinearity. The improved understanding gained from this analysis enables us to create a new, more effective method for identifying these regions based on the second derivative of phase. We also suggest a novel approach that combines our method of nonlinear phase detection with strategies of both phase-differencing and local correlation. This hybrid approach retains the advantageous properties of phase-differencing while incorporating the multispectral aspects of local correlation. This task of registration is greatly simplified if the camera geometry is known and the search for corresponding points can be restricted to epipolar lines. Unfortunately, computation of epipolar lines for an active system requires calibration which can be both highly complex and inaccurate. While it is possible to register images without calibration information, such unconstrained algorithms are usually time consuming and prone to error. In this dissertation we propose compromise. Even without the instantaneous knowledge of the system geometry, we can restrict the region of correspondence by imposing limits on the possible range of configurations, and as a result, confine our search for matching points to what we refer to as epipolar spaces. For each point in one image, we define the corresponding epipolar space in the other image as the union of all associated epipolar lines over all possible system geometries. Epipolar spaces eliminate the need for calibration at the cost of an increased search region. Since the average size of a search space is directly related to the accuracy and efficiency of any registration algorithm, it is essential to mitigate the increase. The major contribution of this dissertation is the derivation of an optimal nonuniform sampling that minimizes the average area per epipolar space. / text

M. Tech. Electronic Engineering. / Discusses the main objective of this research to develop a robust image-based algorithm to detect and determine the orientation of small helipad using shape descriptors and associated pre-processing techniques.

Σκοπός της παρούσας εργασίας είναι η αξιοποίηση και ο οδηγός χρήσης του συστήματος ανάλυσης εικόνας σε περιβαλλοντικές, ορυκτολογικές και πετρογραφικές εφαρμογές. Για να δείξουμε την αξιοποίηση του συστήματος ανάλυσης εικόνας εφαρμόστηκαν μέθοδοι ανάλυσης εικόνας σε εικόνες ηλεκτρονικού και πολωτικού μικροσκοπίου, από λεπτές και στιλπνές τομές, με σκοπό την υποβοήθηση στην επίλυση διαφόρων περιβαλλοντικών ορυκτολογικών και πετρογραφικών προβλημάτων όπως εντοπισμός αμιάντου, υπολογισμός ποσοστιαίας συμμετοχής ορυκτών, υπολογισμός περιφέρειας και εμβαδού κρυστάλλου, υπολογισμός πολυπλοκότητας των ορίων των κρυστάλλων σε λεπτή τομή καθώς και υπολογισμός πορώδους πετρωμάτων. Δόθηκε επίσης οδηγός χρήσης του συστήματος ανάλυσης εικόνας βασιζόμενος στο λογισμικό που χρησιμοποιείται στο Γεωλογικό Τμήμα του Πανεπιστημίου Πατρών. Έγινε αναφορά και δόθηκαν παραδείγματα με εικόνες όλων των μενού και υπομενού του λογισμικού. / Aim of present work is the exploitation and the manual of use of Image analysis in environmental, mineralogical and petrographic applications. In order to show the exploitation Image analysis, applied methods of analysis of picture in pictures of electronic and polotic microscope,from thin and glossy sections, aiming at the assistance in the resolution of various environmental mineralogical and petrographic problems as localisation of asbestos, calculation of percentage attendance mining, calculation of region and area of crystal, calculation of complexity of limits of crystals in thin section as well as calculation porosities of rocks. Also was given a manual of Image analysis based on the software that is used in the Geological Department of Patras University. Reported and given examples with pictures all the menus and submenus of the software.

Computer-vision technology methods for assessing food quality were evaluated for their ability to provide non-contact measurements of apple slices. The methods evaluated were camera calibration, measurements of physical parameters of apple slices, and measurement of biochemical changes in apple slices. Each measure of food quality that was assessed by computer-vision was compared to a conventional method of measurement. The computer-vision system was capable of measuring area, thickness and volumes of apple slices. Color measurements from the computer-vision system were correlated with phenolic compound degradation in the beginning of the drying process and with hydroxymethylfurfural development later in the drying process.

Spectral unmixing produces spatial abundance maps of endmembers or ‘pure’ materials using sub-pixel scale decomposition. It is particularly well suited to extracting a greater portion of the rich information content in hyperspectral data in support of real-world issues such as mineral exploration, resource management, agriculture and food security, pollution detection, and climate change. However, illumination or shading effects, signature variability, and the noise are problematic. The Least Square (LS) based spectral unmixing technique such as Non-Negative Sum Less or Equal to One (NNSLO) depends on “shade” endmembers to deal with the amplitude errors. Furthermore, the LS-based method does not consider amplitude errors in abundance constraint calculations, thus, often leads to abundance errors. The Spectral Angle Constraint (SAC) reduces the amplitude errors, but the abundance errors remain because of using fully constrained condition. In this study, a Genetic Algorithm (GA) was adapted to resolve these issues using a series of iterative computations based on the Darwinian strategy of ‘survival of the fittest’ to improve the accuracy of abundance estimates. The developed GA uses a Spectral Angle Mapper (SAM) based fitness function to calculate abundances by satisfying a SAC-based weakly constrained condition. This was validated using two hyperspectral data sets: (i) a simulated hyperspectral dataset with embedded noise and illumination effects and (ii) AVIRIS data acquired over Cuprite, Nevada, USA. Results showed that the new GA-based unmixing method improved the abundance estimation accuracies and was less sensitive to illumination effects and noise compared to existing spectral unmixing methods, such as the SAC and NNSLO. In case of synthetic data, the GA increased the average index of agreement between true and estimated abundances by 19.83% and 30.10% compared to the SAC and the NNSLO, respectively. Furthermore, in case of real data, GA improved the overall accuracy by 43.1% and 9.4% compared to the SAC and NNSLO, respectively. / xvi, 85 leaves : ill. (chiefly col.) ; 29 cm

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This thesis addresses the problem of producing three-dimensional constrained Delaunay triangulated meshes from the sequential two dimensional MRI medical image slices. The approach is to generate the volumetric meshes of the scanned organs as a result of a several low-level tasks: image segmentation, connected component extraction, isosurfacing, image smoothing, mesh decimation and constrained Delaunay tetrahedralization. The proposed methodology produces a portable application that can be easily adapted and extended by researchers to tackle this problem. The application requires very minimal user intervention and can be used either independently or as a pre-processor to an adaptive mesh refinement system. / Finite element analysis of the MRI medical data depends heavily on the quality of the mesh representation of the scanned organs. This thesis presents experimental test results that illustrate how the different operations done during the process can affect the quality of the final mesh.

Autism and associated gene mutations can be studied with genetic mouse models. Magnetic Resonance Imaging (MRI) of these animal models quantifies the impact of genetics on brain morphology. Using MRI, three genetic mouse models of autism were imaged: Neuroligin 3 R451C knock-in, Methyl-CpG binding protein-2 308-truncation and Integrin β-3 knock-out. Morphological differences were identified using a newly developed MRI mouse cerebellum atlas. The results show all three genes to alter cerebellar anatomy. Each studied gene affected a unique set of cerebellar structures. I hypothesize that the results and known behavioural phenotypes of the models are linked, with anatomy contributing to specific behaviours. In the future work section, a surface-based analysis method is presented to investigate the variance in cerebellum foliation across disease models and inbred strains. This work shows that autism risk-genes alter distinct regions of the cerebellum.

Autism and associated gene mutations can be studied with genetic mouse models. Magnetic Resonance Imaging (MRI) of these animal models quantifies the impact of genetics on brain morphology. Using MRI, three genetic mouse models of autism were imaged: Neuroligin 3 R451C knock-in, Methyl-CpG binding protein-2 308-truncation and Integrin β-3 knock-out. Morphological differences were identified using a newly developed MRI mouse cerebellum atlas. The results show all three genes to alter cerebellar anatomy. Each studied gene affected a unique set of cerebellar structures. I hypothesize that the results and known behavioural phenotypes of the models are linked, with anatomy contributing to specific behaviours. In the future work section, a surface-based analysis method is presented to investigate the variance in cerebellum foliation across disease models and inbred strains. This work shows that autism risk-genes alter distinct regions of the cerebellum.

No description available.