将提升类小波变换(lifting wavelet-like transform,LWLT)应用于矩量法(method of moment,MOM)快速求解电场积分方程(electric fieldintegral equation,EFIE),对生成的稀疏化线性系统采用模基参数估计(model based parameter esti matio...将提升类小波变换(lifting wavelet-like transform,LWLT)应用于矩量法(method of moment,MOM)快速求解电场积分方程(electric fieldintegral equation,EFIE),对生成的稀疏化线性系统采用模基参数估计(model based parameter esti mation,MBPE)算法求解,可获得小波域的宽带解,结合小波逆变换,最终实现目标电磁散射特性的宽频分析。通过不同三维散射体的计算分析,验证了算法的正确性。与传统模基参数估计算法相比,所提算法在计算时间和内存耗费上均有很大改善。展开更多
A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. S...A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate.展开更多
文摘将提升类小波变换(lifting wavelet-like transform,LWLT)应用于矩量法(method of moment,MOM)快速求解电场积分方程(electric fieldintegral equation,EFIE),对生成的稀疏化线性系统采用模基参数估计(model based parameter esti mation,MBPE)算法求解,可获得小波域的宽带解,结合小波逆变换,最终实现目标电磁散射特性的宽频分析。通过不同三维散射体的计算分析,验证了算法的正确性。与传统模基参数估计算法相比,所提算法在计算时间和内存耗费上均有很大改善。
基金Projects(90820302, 60805027) supported by the National Natural Science Foundation of ChinaProject(200805330005) supported by Research Fund for Doctoral Program of Higher Education, ChinaProject(2009FJ4030) supported by Academician Foundation of Hunan Province, China
文摘A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate.
