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Chinese Journal of Electronics 2021年第6期30卷 1141-1151页

作者： YANG Biao ZHU Shengqi HE Xiongpeng National Laboratory of Radar Signal Processing Xidian University

This paper presents a novel Interacting multi-model(IMM) Robust Cardinality balance multitarget multi-Bernoulli(R-CBMe MBer) filter to solve the maneuvering target tracking problem in the case of interval measurement, unknown detection probability and unknown clutter density. In essence, IMM R-CBMe MBer filter is an extended application of R-CBMe MBer *** the IMM R-CBMe MBer filter, the target state is first extended to distinguish clutter from the real *** detection probability and model probability of the target can be adaptively updated. Then, generalized likelihood function and IMM algorithm are introduced to interactively predict and update the state of the target in the IMM R-CBMe MBer filtering process. In addition,a particle application of the IMM R-CBMe MBer filter is given, and a numerical experiment is designed under nonlinear conditions. Meanwhile, Doppler information of the target is employed to estimate the velocity of each maneuvering target. Numerical experiments also verify that the IMM R-CBMe MBer filter can effectively estimate the target position, target velocity, target detection probability and clutter number in the condition of unknown detection probability, unknown clutter rate and interval measurement.

关键词： probability image filtering maneuvering target tracking problem clutter unknown detection probability target state model probability imm robust cardinality balance multiBernoulli filter novel Interacting multimodel robust cardinality balance multitarget multiBernoulli IMM R-CBMeMBer filter target detection probability interval measurement target tracking multiple maneuvering target tracking object detection IMM R-CBMeMBer filtering process tracking filters

Fast and accurate kernel density approximation using a divide-and-conquer approach

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第9期11卷 677-689页

作者： Yan-xia JIN Kai ZHANG James T. KWOK Han-chang ZHOU School of Electronics and Computer Science and Technology North University of China Taiyuan 030051 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Key Laboratory of Instrumentation Science and Dynamic Measurement North University of China Taiyuan 030051 China

Density-based nonparametric clustering techniques,such as the mean shift algorithm,are well known for their flexibility and effectiveness in real-world vision-based *** underlying kernel density estimation process can be very expensive on large *** this paper,the divide-and-conquer method is proposed to reduce these computational *** dataset is first partitioned into a number of small,compact *** of the kernel estimator in each local cluster are then fit to a single,representative density *** key novelty presented here is the efficient derivation of the representative density function using concepts from function approximation,such that the expensive kernel density estimator can be easily summarized by a highly compact model with very few basis *** proposed method has a time complexity that is only linear in the sample size and data ***,the bandwidth of the resultant density model is adaptive to local data *** on color image filtering/segmentation show that,the proposed method is dramatically faster than both the standard mean shift and fast mean shift implementations based on kd-trees while

关键词： Nonparametric clustering Kernel density estimation Mean shift image filtering

filtering images contaminated with pep and salt type noise with pulse-coupled neural networks

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Science in China(Series F) 2005年第3期48卷 322-334页

作者： ZHANGJunying LUZhijun SHILin DONGJiyang SHIMeihong SchoolofComputerScienceandEngineering XidianUniversityXi'an710071China InstituteofElectronicsEngineering XidianUniversityXi'an710071China SchoolofComputerScience Xi'anScienceandTechnologyUniversityXi'an710048China SchoolofComputerScienceandEngineering XidianUniversityXi'an710071China//InstituteofElectronicsEngineeringXidianUniversityXi'an710071China

Pulse coupled neural network (PCNN) has a specific feature that the fire of one neuron can capture its adjacent neurons to fire due to their spatial proximity and intensity similarity. In this paper, it is indicated that this feature itself is a very good mechanism for image filtering when the image is damaged with pep and salt (PAS) type noise. An adaptive filtering method, in which the noisy pixels are first located and then filtered based on the output of the PCNN, is presented. The threshold function of a neuron in the PCNN is designed when it is used for filtering random PAS and extreme PAS noise contaminated image respectively. The filtered image has no distortion for noisy pixels and only less mistiness for non-noisy pixels, compared with the conventional window-based filtering method. Excellent experimental results show great effectiveness and efficiency of the proposed method, especially for heavy-noise contaminated images.

关键词： image filtering pulse coupled neural networks fire of a neuron firing instant.

Analysis on the Application of image Processing Technology in Clothing Pattern Recognition

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Journal of Donghua University(English Edition) 2020年第3期37卷 224-231页

作者： LIU Jing ZHUANG Meiling AI Rongyu GAO Ting College of Textile and Clothing Qingdao UniversityQingdao 266071China

A clothing pattern is a significant embodiment of regional culture and national *** recognition of clothing patterns could be realized objectively and accurately by using digital image processing *** researches on the extraction techniques of various pattern elements were compared and *** the researches on clothing pattern color,outline and fabric texture extraction were *** the core technology chain model of clothing pattern extraction was *** research status,the core technology and the development trend of pattern element extraction technology based on two-dimensional images were ***’s more,a reference for the follow-up research of clothing patterns and the technology upgrading of textile and clothing industries were provided.

关键词： image filtering color space color clustering edge detection clothing pattern

A noise removal algorithm for DR images based on adaptive estimation of threshold

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Computer Aided Drafting,Design and Manufacturing 2012年第4期22卷 1-6页

作者： XIONG Bangshu JIA Bei YU Lei ZHAO Pingjun Key Laboratory of Nondestructive Test of Ministry of Education Nanchang Hangkong University Avic Hongdu Aviation Industry Group Co. Ltd. Aircarft Design & Research Institute

When DR （Digital Radiography） images are filtered, it is necessary to preserve the edges and key details. But the existing methods may inevitably take fine details mistaken for noise to remove. In order to solve the problem an improved anisotropic diffu- sion filtering model is proposed. Firstly, a novel diffusion function is introduced based on Perona and Malik model, which well overcomes the high rate of convergence. Secondly, the gradient threshold is modified to an adaptive estimation function, so it is bet- ter at adaptive threshold regulations according to the pixels and iteration times. Finally, the edges are extracted from the restored im- ages and the results are evaluated quantificationally. It is shown from the experiments that the proposed method is effective not only in noise reduction but also in details preserved.

关键词： image filtering anisotropic diffusion diffusion function gradient threshold edge extract

Most relevant weighted filtering with one-step singular correlation recognition

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Journal of Measurement Science and Instrumentation 2012年第4期3卷 328-332页

作者：刘超蔡建超 School of Mechanics Jinzhong University College of Physics and Electronics Engineering Shanxi University

Based on the recognition of one-step singular correlation and the remedying methods obtained before,the correlation properties of the neighborhood pixels and the characteristics of image de-noising were analyzed.A kind of most relevant weighted filtering method based on one-step singular correlation recognition(OSSC-MRWF)was put *** simulation experiments were done and the comparison with some commonly used methods under salt-and-pepper noises was *** results show that the proposed method can not only effectively recognize salt-and-pepper noises and mend up the noise points,but also protect the original information such as the edge details very *** accuracy and performance indicators are further improved considerably.

关键词： correlation function image filtering one-step singular correlation singular value recognition weighted filter- ing salt-and-pepper noise

A multi-scale scheme for image segmentation using neuro-fuzzy classification and curve evolution

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Computer Aided Drafting,Design and Manufacturing 2012年第4期22卷 7-10页

作者： YUAN Da Key Laboratory of Intelligent Information Processing in Universities of Shandong(Shandong Institute of Business and Technology)

In this paper, we present a new scheme to segment a given image. This scheme utilizes neuro-fuzzy system to derive a proper set of contour pixels based on multi-scale images. We use these fuzzy derivatives to develop a new curve evolution model. The model automatically detect smooth boundaries, scaling the energy term, and change of topology according to the extracted con- tour pixels set. We present the numerical implementation and the experimental results based on the semi-implicit method. Experi- mental results show that one can obtains a high clualitv edge contour.

关键词： image filtering anisotropic diffusion diffusion function gradient threshold edge extract

A Vision-based Demonstration and Verification Platform for Path Planning

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A Vision-based Demonstration and Verification Platform for P...