The license plate location technique is an important imageprocessing step in license plate recognition system. Vehicle license plates are distinguished from backgrounds using features proposed in existing literatures...
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The license plate location technique is an important imageprocessing step in license plate recognition system. Vehicle license plates are distinguished from backgrounds using features proposed in existing literatures. However, the effect of location is quite affected by feature selection. In this paper, we propose a method of precise license plate location fusing salient features. The method is mainly divided into three steps. First, candidate license plate regions are detected using improved Harris corner feature with much less time than traditional method. Then, candidates are sifted to only retain license plates based on two salient features named color combination and mean difference which are first proposed in this paper. Finally, the license plates are located precisely according to the projection feature. In experiment, the proposed algorithm was tested with 1942 real images captured in different environment and the license plates are successfully located as 97.6% in average with only 109ms. The experiment results demonstrates the effectiveness and efficient of our algorithm.
Precise pupil features detection is an important factor for face recognition. This paper presents a robust and accurate algorithm to precisely estimate pupil features: pupil center and pupil radius. First, a pupil par...
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Dual-energy X-ray imaging is an important method of medical imaging, capable of not only obtaining spatial information of imaging object but also disclosing its chemical components, and has many applications in clinic...
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ISBN:
(纸本)0819461857
Dual-energy X-ray imaging is an important method of medical imaging, capable of not only obtaining spatial information of imaging object but also disclosing its chemical components, and has many applications in clinic. The current computation methods of dual-energy imaging are still based on the model of mono-energy spectrum imaging with some linear calibration, while they are incapable to reflect correctly the physical characteristics of dual-energy imaging and obstruct deeper research in this field. The article presents a new medical X-ray imaging model in accordance with physics of imaging and its corresponding computational method. The computation process includes two steps: first, to compute two attenuation parameters that have clear physical meaning: equivalent electron density and attenuation parameter of photoemission;then to compute the components of high- and low-density mass through a group of simple equation with two variables. Experiments showed that such method has quite a satisfactory precision in theory, that is, the solutions of parameters under different exposure voltages and thickness of tissue for several main tissues of human body are much low in deviations, whose quotient of standard deviation divided by mean are mostly under 0.1%, and at most 0.32%. The method provides not only a new computational way for dual-energy X-ray imaging, but also a feasible analysis for its nature. In addition, the method can be used to linearly rectify data of dual-energy CT and analyze the chemical component of reconstructed object by means of parameters clear in physics.
Refinement is a necessary and effective step in some node localization schemes of wireless sensor networks (WSN). Suitable refinement procedure can improve the node localization accuracy and raise the robustness of th...
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ISBN:
(纸本)9780863418365
Refinement is a necessary and effective step in some node localization schemes of wireless sensor networks (WSN). Suitable refinement procedure can improve the node localization accuracy and raise the robustness of the localization algorithm. However, most existing refinement algorithms are costly duo to complex computation and frequent communication, and may induce serious coverage problem duo to nonconvergent iterations. In view of above facts, Steepest descent method is proposed to be used as the refinement algorithm in this paper, and corresponding simulation experiments are done to testify its feasibility and validity. The results show that steepest descent method can optimize the node positions to a fairish accuracy extent, and compared with existing refinement methods, it outperforms in communication cost, computation cost, and coverage rate.
Medical imaging techniques like computed/digital radiography (CR/DR) have introduced a formidably powerful tool in medicine. image enhancement takes an important roll in the CR/DR computerized analysis process. Much e...
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This paper presents a statistical reconstruction algorithm for dual-energy (DE) CT of polychromatic x-ray source. Each pixel in the imaged object is assumed to be composed of two basis materials (i.e., bone and soft t...
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How to accurately predict traffic data with weak regularity is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are proposed to deal with such a problem. It is t...
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Since feature selection can remove the irrelevant features and improve the performance of learning systems, it is an crucial step in machine learning. The feature selection methods using support vector machines have o...
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In this paper, we present an improvement on the discrete wavelet transform (DWT)-based feature extraction algorithm used in vehicle classification in Wireless Sensor Networks (WSN) [1] by providing a rule of selecting...
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An approach for head pose estimation has been proposed in this paper using Hough forest. The estimation of pose are generated by voting from image patches as in a Hough transform. The basic idea is that image patches ...
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