2,611 research outputs found

    Multiscale Edge Detection using a Finite Element Framework for Hexagonal Pixel-based Images

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    Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

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    Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated

    Analysis of Retinal Image Data to Support Glaucoma Diagnosis

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    Fundus kamera je ĆĄiroce dostupnĂ© zobrazovacĂ­ zaƙízenĂ­, kterĂ© umoĆŸĆˆuje relativně rychlĂ© a nenĂĄkladnĂ© vyĆĄetƙenĂ­ zadnĂ­ho segmentu oka – sĂ­tnice. Z těchto dĆŻvodĆŻ se mnoho vĂœzkumnĂœch pracoviĆĄĆ„ zaměƙuje prĂĄvě na vĂœvoj automatickĂœch metod diagnostiky nemocĂ­ sĂ­tnice s vyuĆŸitĂ­m fundus fotografiĂ­. Tato dizertačnĂ­ prĂĄce analyzuje současnĂœ stav vědeckĂ©ho poznĂĄnĂ­ v oblasti diagnostiky glaukomu s vyuĆŸitĂ­m fundus kamery a navrhuje novou metodiku hodnocenĂ­ vrstvy nervovĂœch vlĂĄken (VNV) na sĂ­tnici pomocĂ­ texturnĂ­ analĂœzy. Spolu s touto metodikou je navrĆŸena metoda segmentace cĂ©vnĂ­ho ƙečiĆĄtě sĂ­tnice, jakoĆŸto dalĆĄĂ­ hodnotnĂœ pƙíspěvek k současnĂ©mu stavu ƙeĆĄenĂ© problematiky. Segmentace cĂ©vnĂ­ho ƙečiĆĄtě rovnÄ›ĆŸ slouĆŸĂ­ jako nezbytnĂœ krok pƙedchĂĄzejĂ­cĂ­ analĂœzu VNV. Vedle toho prĂĄce publikuje novou volně dostupnou databĂĄzi snĂ­mkĆŻ sĂ­tnice se zlatĂœmi standardy pro Ășčely hodnocenĂ­ automatickĂœch metod segmentace cĂ©vnĂ­ho ƙečiĆĄtě.Fundus camera is widely available imaging device enabling fast and cheap examination of the human retina. Hence, many researchers focus on development of automatic methods towards assessment of various retinal diseases via fundus images. This dissertation summarizes recent state-of-the-art in the field of glaucoma diagnosis using fundus camera and proposes a novel methodology for assessment of the retinal nerve fiber layer (RNFL) via texture analysis. Along with it, a method for the retinal blood vessel segmentation is introduced as an additional valuable contribution to the recent state-of-the-art in the field of retinal image processing. Segmentation of the blood vessels also serves as a necessary step preceding evaluation of the RNFL via the proposed methodology. In addition, a new publicly available high-resolution retinal image database with gold standard data is introduced as a novel opportunity for other researches to evaluate their segmentation algorithms.

    Meson Photo-Couplings From Lattice Quantum Chromodynamics

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    We explore the calculation of three-point functions featuring a vector current insertion in lattice Quantum Chromodynamics. These three-point functions, in general, contain information about many radiative transition matrix elements simultaneously. We develop and implement the technology necessary to isolate a single matrix element via the use of optimized operators, operators designed to interpolate a single meson eigenstate, which are constructed as variationally optimized linear combination of meson interpolating fields within a large basis. In order to frame the results we also explore some well known phenomenology arising within the context of the constituent quark model before transitioning to a lattice calculation of the spectrum of isovector mesons in a version of QCD featuring three flavors of quarks all tuned to approximately the physical strange quark mass. We then proceed to calculate radiative transition matrix elements for the lightest few isovector pseudoscalar and vector particles. The dependence of these form factors and transitions on the photon virtuality is extracted and some model intuitions are explored

    Improving Iris Recognition through Quality and Interoperability Metrics

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    The ability to identify individuals based on their iris is known as iris recognition. Over the past decade iris recognition has garnered much attention because of its strong performance in comparison with other mainstream biometrics such as fingerprint and face recognition. Performance of iris recognition systems is driven by application scenario requirements. Standoff distance, subject cooperation, underlying optics, and illumination are a few examples of these requirements which dictate the nature of images an iris recognition system has to process. Traditional iris recognition systems, dubbed stop and stare , operate under highly constrained conditions. This ensures that the captured image is of sufficient quality so that the success of subsequent processing stages, segmentation, encoding, and matching are not compromised. When acquisition constraints are relaxed, such as for surveillance or iris on the move, the fidelity of subsequent processing steps lessens.;In this dissertation we propose a multi-faceted framework for mitigating the difficulties associated with non-ideal iris. We develop and investigate a comprehensive iris image quality metric that is predictive of iris matching performance. The metric is composed of photometric measures such as defocus, motion blur, and illumination, but also contains domain specific measures such as occlusion, and gaze angle. These measures are then combined through a fusion rule based on Dempster-Shafer theory. Related to iris segmentation, which is arguably one of the most important tasks in iris recognition, we develop metrics which are used to evaluate the precision of the pupil and iris boundaries. Furthermore, we illustrate three methods which take advantage of the proposed segmentation metrics for rectifying incorrect segmentation boundaries. Finally, we look at the issue of iris image interoperability and demonstrate that techniques from the field of hardware fingerprinting can be utilized to improve iris matching performance when images captured from distinct sensors are involved

    Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey

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    Oriented object detection is one of the most fundamental and challenging tasks in remote sensing, aiming at locating the oriented objects of numerous predefined object categories. Recently, deep learning based methods have achieved remarkable performance in detecting oriented objects in optical remote sensing imagery. However, a thorough review of the literature in remote sensing has not yet emerged. Therefore, we give a comprehensive survey of recent advances and cover many aspects of oriented object detection, including problem definition, commonly used datasets, evaluation protocols, detection frameworks, oriented object representations, and feature representations. Besides, the state-of-the-art methods are analyzed and discussed. We finally discuss future research directions to put forward some useful research guidance. We believe that this survey shall be valuable to researchers across academia and industr
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