325 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

    A review of hough transform and line segment detection approaches

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    In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

    A review of hough transform and line segment detection approaches

    Get PDF
    In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

    An approach for parameter estimation of combined CPPM and LFM radar signal

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    AbstractIn this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation (CPPM) and linear frequency modulation (LFM), which can be widely used in electronic countermeasures, is addressed. An approach is proposed to estimate the initial frequency and chirp rate of the combined signal by exploiting the second-order cyclostationarity of the intra-pulse signal. In addition, under the condition of the equal pulse width, the pulse repetition interval (PRI) of the combined signal is predicted using the low-order Volterra adaptive filter. Simulations demonstrate that the proposed cyclic autocorrelation Hough transform (CHT) algorithm is theoretically tolerant to additive white Gaussian noise. When the value of signal noise to ratio (SNR) is less than −4dB, it can still estimate the intra-pulse parameters well. When SNR=−3dB, a good prediction of the PRI sequence can be achieved by the Volterra adaptive filter algorithm, even only 100 training samples

    Setting upper limits on the strength of periodic gravitational waves from PSR J1939+2134 using the first science data from the GEO 600 and LIGO detectors

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    Data collected by the GEO 600 and LIGO interferometric gravitational wave detectors during their first observational science run were searched for continuous gravitational waves from the pulsar J1939+2134 at twice its rotation frequency. Two independent analysis methods were used and are demonstrated in this paper: a frequency domain method and a time domain method. Both achieve consistent null results, placing new upper limits on the strength of the pulsar's gravitational wave emission. A model emission mechanism is used to interpret the limits as a constraint on the pulsar's equatorial ellipticity

    Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition

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    An increasing need for biometrics recognition systems has grown substantially to address the issues of recognition and identification, especially in highly dense areas such as airports, train stations, and financial transactions. Evidence of these can be seen in some airports and also the implementation of these technologies in our mobile phones. Among the most popular biometric technologies include facial, fingerprints, and iris recognition. The iris recognition is considered by many researchers to be the most accurate and reliable form of biometric recognition because iris can neither be surgically operated with a chance of losing slight nor change due to aging. However, presently most iris recognition systems available can only recognize iris image with frontal-looking and high-quality images. Angular image and partially capture image cannot be authenticated with the existing method of iris recognition. This research investigates the possibility of developing a technique for recognition partially captured iris image. The technique is designed to process the iris image at 50%, 25%, 16.5%, and 12.5% and to find a threshold for a minimum amount of iris region required to authenticate the individual. The research also developed and implemented two Dimensional (2D) Legendre wavelet filter for the iris feature extraction. The Legendre wavelet filter is to enhance the feature extraction technique. Selected iris images from CASIA, UBIRIS, and MMU database were used to test the accuracy of the introduced technique. The technique was able to produce recognition accuracy between 70 – 90% CASIA-interval with 92.25% accuracy, CASIA-distance with 86.25%, UBIRIS with 74.95%, and MMU with 94.45%

    Review on Classification Methods used in Image based Sign Language Recognition System

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    Sign language is the way of communication among the Deaf-Dumb people by expressing signs. This paper is present review on Sign language Recognition system that aims to provide communication way for Deaf and Dumb pople. This paper describes review of Image based sign language recognition system. Signs are in the form of hand gestures and these gestures are identified from images as well as videos. Gestures are identified and classified according to features of Gesture image. Features are like shape, rotation, angle, pixels, hand movement etc. Features are finding by various Features Extraction methods and classified by various machine learning methods. Main pupose of this paper is to review on classification methods of similar systems used in Image based hand gesture recognition . This paper also describe comarison of various system on the base of classification methods and accuracy rate

    Researches on Non-standard Optics for Advanced Gravitational Waves Interferometers

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    This thesis presents a collection of different researches on non-standard optics in view of enhancing the performances of the Advanced Gravitational waves interferometric detectors, where the thermal noise of the test masses is expected to be a limiting factor for their sensitivity. We provide a quantitative analysis of the impact of non-Gaussian beams on different kinds of thermal noises. We developed the theory of mesa beam, in view of a future implementation in advanced GW interferometers of the mesa beam idea, focusing on the analytical derivation of the quantities (i.e. beam width, divergence, propagation factor), which are chosen as ISO standard reference parameters for the characterization of an optical beam. We also analytically proved a new duality relation between optical cavities with non-spherical mirrors. The interest of the GW community in this new beam technology led us to the construction and testing of a prototype mesa beam Fabry-Perot cavity with Mexican-hat mirror. Part of the work of this thesis was devoted to the development of new simulation programs of optical systems. These programs provided the theoretical expected behaviour of our experiment, in particular cavity modes structure and misalignments sensitivity to be confronted with the experimental results. We also explored another complementary way of reducing the mirror thermal noise, beside the beam shaping, that is the multi-layered coating thickness optimization. We show it to be effective in reducing the coating noise and explore the possible implications for GW interferometers in terms of sensitivity. During this analysis we developed an independent model for the coating effective elastic parameters, which is based on the well understood subject of homogenization theory.Comment: Ph.D. thesis, University of Pisa & LIGO-Caltech, 185 page

    AlGaAs Coating Studies for Present and Future Gravitational Wave Detectors

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    With the first observation of a binary black hole merger in GW150914 \cite{150914Discovery}, LIGO heralded a new era in the field of observational astronomy. It was the first observation of any astronomical object by measuring its gravitational wave signature, opening a new window to the cosmos. A few years later, the first astronomical event to be observed by both electromagnetic waves and gravitational waves was made \cite{Cowperthwaite2017GW170817, Abbott2017GW170817}. Gravitational wave detectors are multi-kilometer long interferometers measure changes to their length imparted by passing gravitational waves that are smaller than an atomic nucleus. Such extreme precision requires extensive mitigation of many different noise sources, discussed in chapter 1. This work is primarily focused on thermal noise of the gravitational wave detector test mass coatings, detailed in chapter 2. Chapter 3 outlines the theory behind the cryogenic gentle nodal suspension \cite{Cesarini_GeNS}, the experiment the author constructed at Syracuse to measure coating thermal noise of possible test mass coatings. Chapters 4 and 5 will delve into the data that the author measured in the cryogenic gentle nodal suspension. There is a special focus in this work on AlGaAs/GaAs multi-layers, referred to as just AlGaAs for simplicity, as a possible test mass coating. This crystalline coating has shown remarkably low levels of loss at room temperature \cite{Cole2013OG, Penn2019MechAlGaAs}. This work serves as a follow-up to the room temperature loss measurements, with the goals of understanding the loss mechanisms inside an AlGaAs coating through the shape of its loss curve over temperature \cite{Martin2008measurements} and applying the findings to possible cold temperature gravitational wave detectors \cite{Akutsu2021, ET2020, CEHorizon2021}. The mechanical loss of the AlGaAs coating was found to be largely varied by mode, with the lowest mode displaying loss levels of ϕcoating≲1×10−6\phi_{\mathrm{coating}} \lesssim 1 \times 10^{-6} and the highest mode showing loss of ϕcoating≲1×10−4\phi_{\mathrm{coating}} \lesssim 1 \times 10^{-4}. Investigations into the mechanisms contributing to excess loss in some of the modes are currently underway. The lowest loss modes are consistent with room temperature measurements
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