425 research outputs found

    A parallel windowing approach to the Hough transform for line segment detection

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    In the wide range of image processing and computer vision problems, line segment detection has always been among the most critical headlines. Detection of primitives such as linear features and straight edges has diverse applications in many image understanding and perception tasks. The research presented in this dissertation is a contribution to the detection of straight-line segments by identifying the location of their endpoints within a two-dimensional digital image. The proposed method is based on a unique domain-crossing approach that takes both image and parameter domain information into consideration. First, the straight-line parameters, i.e. location and orientation, have been identified using an advanced Fourier-based Hough transform. As well as producing more accurate and robust detection of straight-lines, this method has been proven to have better efficiency in terms of computational time in comparison with the standard Hough transform. Second, for each straight-line a window-of-interest is designed in the image domain and the disturbance caused by the other neighbouring segments is removed to capture the Hough transform buttery of the target segment. In this way, for each straight-line a separate buttery is constructed. The boundary of the buttery wings are further smoothed and approximated by a curve fitting approach. Finally, segments endpoints were identified using buttery boundary points and the Hough transform peak. Experimental results on synthetic and real images have shown that the proposed method enjoys a superior performance compared with the existing similar representative works

    Automated identification of flagella from videomicroscopy via the medial axis transform

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    Ubiquitous in eukaryotic organisms, the flagellum is a well-studied organelle that is well-known to be responsible for motility in a variety of organisms. Commonly necessitated in their study is the capability to image and subsequently track the movement of one or more flagella using videomicroscopy, requiring digital isolation and location of the flagellum within a sequence of frames. Such a process in general currently requires some researcher input, providing some manual estimate or reliance on an experiment-specific heuristic to correctly identify and track the motion of a flagellum. Here we present a fully-automated method of flagellum identification from videomicroscopy based on the fact that the flagella are of approximately constant width when viewed by microscopy. We demonstrate the effectiveness of the algorithm by application to captured videomicroscopy of Leishmania mexicana, a parasitic monoflagellate of the family Trypanosomatidae. ImageJ Macros for flagellar identification are provided, and high accuracy and remarkable throughput are achieved via this unsupervised method, obtaining results comparable in quality to previous studies of closely-related species but achieved without the need for precursory measurements or the development of a specialised heuristic, enabling in general the automated generation of digitised kinematic descriptions of flagellar beating from videomicroscopy.Comment: 10 pages, 5 figures. Author accepted manuscript. Supplementary Material available at https://doi.org/10.1038/s41598-019-41459-

    The Detection and Quantification of Straight-Lined Irregularities on Surfaces

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    Under the microscope, scratches or abrasions on hard otherwise flat surfaces are usually revealed as straight-lined irregularities. At a more macroscopic level creases in thin sheets such as of paper and textile fabrics are also observed to be straight-lined. A computer-aided image analytical method is described here not only for identifying such features but also for counting them, measuring their lengths and evaluating their contrast. Further measures are derived that are in accord with the qualitative visual impact of each line within the milleau of lines in the original image. The method makes use of a parametric transformation from two orthogonally-illuminated images of the surface using the equation p=x∙cos(θ) + y∙sin(θ) where x,y are image coordinates, θ is the angle that a straight line makes with the x-axis and p is the perpendicular distance of that line from the coordinate origin. As distinct from the well-known Hough transform, estimates are made for θ at all points in the initial images that are illuminated at a low angle from two orthogonal directions

    Advancement and applications of the template matching approach to indexing electron backscatter patterns

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    Electron backscatter diffraction is a well-established characterisation technique used to determine the orientation and crystal phase of a crystalline material. A pattern is formed by dynamical interaction of elections with the crystal lattice, which can be understood and simulated by using Bloch wave theory. The conventional method of indexing a diffraction pattern is to use a Hough transform to convert the lines of the pattern to points that are easily accessible to a computer. As the bands of the pattern are direct projections of the crystal planes, the interplanar angles can then be computed and compared to a look up table to determine phase and orientation. This method works well for most examples, however, is not well suited to more complex unit cells, due to the fact it ignores more subtle features of the patterns. This thesis proposes a refined template matching approach which uses efficient pattern matching algorithms, such as those used in the field of computer vision, for phase determination and orientation analysis. This thesis introduces the method and demonstrates its efficacy, as well as introducing advanced methods for pseudosymmetry analysis and phase mapping. A new metric for phase confidence is also proposed and the refined method is shown to be able to correctly determine phases and pseudosymmetric orientations. Finally, preliminary work on a direct electron detector stage is presented. Work on the development, testing the pattern centre reliability, modulation transfer and an example map is shown.Open Acces

    Coastal bathymetry from satellite high resolution monitoring

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    [EN] Bathymetry is traditionally obtained by echo sounding technology. However, bathymetry can be also obtained from satellite imaging, which is much more cheaper than echo-sound measurements. This is obtained by analyzing the waves near to the shoreline. In order so, wave properties such as wavelength and celerity should be measured, after which the bathymetry is estimated using linear wave theory. In this internship a new method based in the continuous wavelet transform has been implemented. In order to obtain the celerity, two images with a time lag are needed. Two data sets are used. On the one hand a video product, with 12 Pléiades images with a time lag between them of 8s. On the other hand a set of Sentinel-2 images. In the latter, a time shift between bands because of a lag in the acquisition is exploited. An application for the extraction and preparation of Sentinel-2 data in a form of a Graphical User Interface has been implemented. The site that has been studied will be the shore of Capbreton, which hosts one of the world’s deepest canyons. The images have been be pre-filtered by using FFT and Radon filters, with several methods that include windowing of fixed and variable size. Those filtering techniques have be implemented and its results compared. Best results are obtained using a variable-size windowing technique. Finally, the wavelet method has been applied to both datasets to achieve wave propagation information.Soñes Bori, J. (2019). Coastal bathymetry from satellite high resolution monitoring. Universitat Politècnica de València. http://hdl.handle.net/10251/140103TFG

    Coronal Mass Ejection Detection using Wavelets, Curvelets and Ridgelets: Applications for Space Weather Monitoring

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    Coronal mass ejections (CMEs) are large-scale eruptions of plasma and magnetic feld that can produce adverse space weather at Earth and other locations in the Heliosphere. Due to the intrinsic multiscale nature of features in coronagraph images, wavelet and multiscale image processing techniques are well suited to enhancing the visibility of CMEs and supressing noise. However, wavelets are better suited to identifying point-like features, such as noise or background stars, than to enhancing the visibility of the curved form of a typical CME front. Higher order multiscale techniques, such as ridgelets and curvelets, were therefore explored to characterise the morphology (width, curvature) and kinematics (position, velocity, acceleration) of CMEs. Curvelets in particular were found to be well suited to characterising CME properties in a self-consistent manner. Curvelets are thus likely to be of benefit to autonomous monitoring of CME properties for space weather applications.Comment: Accepted for publication in Advances in Space Research (3 April 2010

    Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

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    In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

    A review of vision-based gait recognition methods for human identification

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    Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field
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