Analysis and extraction of stepped frequency radar signature for micro-motion structure

Radar signature produced by micro-motion structures contains movement and structure information which is useful for radar target recognition. For stepped frequency (SF) radar, the Doppler modulation is coupled with the stepped carrier frequency. It shifts and smears the high-resolution range profile and makes it difficult to analyse and extract the micro-motion information from the range profile directly. The authors propose to comprehend the SF radar as a special pulse Doppler radar, and the range profile as a Doppler profile for the convenience of motion analysis. The signature of moving targets for SF radar is analysed from this point of view and the equivalent instantaneous Doppler frequency (EIDF) is introduced. Then, a typical micro-motion, rotation, is taken as an example. The sinusoidal vibration of the peaks in the range profile sequence is explained in detail, especially the relationship between the sinusoid parameters and the rotation parameters. An approach to extract the rotation parameters from the range profile or EIDF spectrum sequence is proposed based on the Hough transform. Simulated and experimental results validate the theoretical analysis and the feature extraction method.     

基于改进Hough变换的直线图形快速提取算法

为能够有效解决实时直线图形提取问题,提出了一种基于多约束Hough变换(HT)的直线提取算法.该算法首先分析了数字图像中直线边缘的三种结构特征,提出采用基元结构表示目标边缘点,并在约束条件下计算基元结构的基元倾角.在此基础上,结合传统的HT的思想对基元结构进行极角约束HT,以获得最终的直线参数.实验结果表明,对合成图像和自然图像,该算法比梯度HT的运算速度分别提高约190倍和22倍.     

一种基于改进的傅里叶变换的弱故障信号特征提取新方法研究

弱信号特征提取一直是故障诊断领域的难点,文章结合传统傅里叶变换,提出一种将时间序列变换为频域,再从频域转换到时域的复数域,并对该复数域进行幅值计算的方法对弱信号进行特征提取.通过仿真计算发现该方法突出了信号的局部特征信息,不仅能对夹杂在信号中的微弱冲击成分进行较好的再现,而且也能在强背景噪声环境下提取微弱故障信息.最后通过齿轮齿面接触型故障实验验证了该方法的有效性.     

改进随机Hough变换的中心检测

针对复杂背景下干扰因素多,圆的形态不规则,传统随机Hough变换圆检测速度慢,检测精度低的问题,提出一种复杂背景下的不规则圆检测方法。该方法首先通过连通区域标记算法,获得目标圆的感兴趣区域(region of interest, ROI),然后对圆的边缘点进行分区采样,提高随机采样的有效性;确定圆心位置后,取圆心的8邻域像素为圆心分别做圆,从而提高不规则圆的检测精度。通过对热保护器校准点的中心检测结果表明,提出的方法可在复杂背景下实现圆的精确检出,检测速度更快,检测精度更高。     

基于Hough变换的车窗提取算法

在HOV乘客数检测系统中，对车窗的定位与提取，可以极大地减少计算量，提高系统的运算速度以及检测的准确度。针对图像中车窗边缘的图像特点，提出了一种基于相位编组法进行图像分块，在图像块内进行决速Hough变换的直线检测，并结合积分投影方法对车窗进行定位与提取。实验结果表明该算法具有较快的运算速度和较高的检测准确率。     

基于改进经验小波变换的滚动轴承故障特征提取方法研究

针对振动信号的非线性、非平稳性和早期故障特征信号难以提取的特点,提出一种基于改进经验小波变换的故障特征提取方法。通过包络分析和对包络曲线进行阈值分割修整的方法来确定经验小波变换分解的模态数和频率边界,解决传统经验小波变换需要预先设置分解模态数和难以对信号频谱进行适当分割问题,以实现对振动信号故障信息更准确的描述。实验表明,该频谱分割方法能够有效检测信号最佳模态分解数,使得信号的频谱分割更为容易、可靠。相比传统EWT和EMD,改进经验小波变换的滚动轴承内圈、外圈Hilbert变换时频图对振动信号的故障相关特征描述更为清晰,在滚动轴承故障特征提取方面表现更为优越。     

基于HT的显微视觉亚像素定位算法

根据显微图像的几何特点,提出了基于HT（Hough Transform）十字显微图像亚像素定位算法（SLACHT）和基于HT近似圆显微图像亚像素定位算法（SLARHT）,对显微图像特征中的直线和近似圆的亚像素定位做了深入的研究。通过与经典直线HT定位算法和经典随机HT圆定位算法进行实验对比,验证了该定位算法的优越性;通过与Matlab中的直线HT定位算法和圆HT定位算法进行实验对比,验证了该算法的可靠性。大量实验表明,该算法的定位精度优于0．01像素,具有运行速度快、鲁棒性强的特点。     

非线性小波变换在故障特征提取中的应用

经典小波变换在不同尺度采用一种小波基 ,不能很好地匹配信号的局部特征 ,因而造成降噪信号丢失了原始信号中部分的有用信息。为了克服上述缺陷 ,提出了一种基于第二代小波变换的非线性小波变换振动信号预处理方法 .应用第二代小波变换的预测器和更新器相互独立的特点 ,根据预测方差最小的选取原则 ,确定每个变换样本的最佳预测器 ,使预测器能够适应信号的局部特征。模拟数据和振动信号的分析表明 ,该方法克服了传统小波降噪方法局部信息丢失的缺陷 ,不仅可以有效地去除信号中的噪声 ,而且能够保留信号的局部特征。作为一种预处理方法 ,在某发电厂的故障诊断中有效地从振动信号中提取了故障特征。     

基于局部Otsu分割与Hough变换的海天线检测

海面波浪、船只与光照等因素的影响,使得可见光海面图像中的海天线难以被准确检测。为提高海天线检测的准确性与鲁棒性,提出了基于局部Otsu分割与Hough变换的海天线检测方法。首先,通过纵向中值滤波快速地抑制灰度图像中的光斑等高频噪声。然后,根据图像特点进行纵向分块处理来补偿光照的不均匀性并将船只的干扰范围限定在部分图像块中,再进行局部Otsu分割得到二值图像并提取其中的边缘像素,抑制了波浪边缘的干扰。最后,采用Hough变换拟合边缘像素以得到海天线。实验结果表明所提方法具有较高的准确性、鲁棒性与实时性,其检测准确率达93.0%,显著高于三种代表性的海天线检测方法。     

Robust ellipse detection based on hierarchical image pyramid and Hough transform

In this research we propose a fast and robust ellipse detection algorithm based on a multipass Hough transform and an image pyramid data structure. The algorithm starts with an exhaustive search on a low-resolution image in the image pyramid using elliptical Hough transform. Then the image resolution is iteratively increased while the candidate ellipses with higher resolution are updated at each step until the original image resolution is reached. After removing the detected ellipses, the Hough transform is repeatedly applied in multiple passes to search for remaining ellipses, and terminates when no more ellipses are found. This approach significantly reduces the false positive error of ellipse detection as compared with the conventional randomized Hough transform method. The analysis shows that the computing complexity of this algorithm is Θ(n(5/2)), and thus the computation time and memory requirement are significantly reduced. The developed algorithm was tested with images containing various numbers of ellipses. The effects of noise-to-signal ratio combined with various ellipse sizes on the detection accuracy were analyzed and discussed. Experimental results revealed that the algorithm is robust to noise. The average detection accuracies were all above 90% for images with less than seven ellipses, and slightly decreased to about 80% for images with more ellipses. The average false positive error was less than 2%.     

基于雷达图特征提取和近红外光谱技术的葛根粉鉴别方法研究

基于雷达图表示原理,对葛根粉的近红外光谱进行了鉴别研究。首先,利用近红外光谱技术获得了中药葛根粉在12000cm~(-1)~4000cm~(-1)范围内的近红外漫反射光谱作为指纹图谱,然后采用多层雷达图对指纹图谱逐步进行特征提取,最后用优选图特征进行分类研究。该方法在保证数据整体性的前提下,有很好的直观可视性,最小识别误差率达1.04%。研究结果表明该方法既是一种可行的葛根粉鉴别新方法,同时又可作为可视化中药指纹图谱模式分析新方法 。     

基于随机共振及图谱理论的齿轮故障特征提取

机车齿轮箱齿轮工作环境恶劣且极易出现齿轮损伤.齿轮箱齿根裂纹损伤检测是保证列车安全运行的有效措施.本文提出了一种基于随机共振以及图谱理论相结合的齿根裂纹故障提取方法.基于随机共振以及图谱理论的故障提取方法具体过程如下:首先,运用复Morlet小波梳状滤波器对原始信号进行解调处理;然后,运用图谱理论方法对仿真信号进行处理...     

基于特征端元提取的像元分解方法

通过对端元概念的引申,提出了更具一般性的特征端元的概念,从而将端元方法的适用范围从高光谱数据拓展到了多光谱数据和全色遥感数据.同时改进了端元提取算法,并提出了基于特征端元提取的像元分解方法.实验结果表明,基于特征端元提取的混合像元分解方法可以得到较好的像元分解结果,并且该方法与传统的端元概念及混合像元分解结果具有良好的可比性.     

基于特定听觉特征的盲信号提取方法

针对参考信号波形难以构造等情况,提出一种利用期望源信号听觉特征进行信号盲提取的方法,根据源信号的听觉特征先验知识,将提取信号与期望信号听觉特征向量的接近程度作为度量,使用非线性最小二乘法进行迭代计算分离向量,提取出与期望信号在听觉特征上最为接近的源信号。对这种方法进行了理论推导,并使用不同类型的信号进行了仿真。仿真结果表明,该方法具有良好的效果,且由于仅利用了信号的听觉特征,而不需要参考信号的具体波形,对于不同类型的信号,具有广泛的适应性,具有良好的应用价值。     

基于希尔伯特黄变换的刀具磨损特征提取

概述了希尔伯特黄变换(HHT)的基本理论和算法,对信号经过经验模态分解(EMD)后得到的固有模态函数(IMF)求取振幅均值,差值筛选出与刀具磨损相关的IMF分量,并对单分量固有模态函数求取边际谱,获取边际谱最大幅值点,建立他们与刀具磨损之间的映射关系,进行特征提取,将其作为神经网络的输入特征向量,结合希尔伯特三维时频谱进行刀具磨损状态的判断。研究结果证明,该方法可以作为刀具磨损监测中信号特征提取的一种简单和可靠的方法。     

Rotational symmetry extraction based on local feature match

提出了一种基于局部特征匹配的旋转对称提取算法,为基于特征的逆向工程提供了一种新的对称约束的提取方法.该算法首先利用特征线将模型分割成对应各个特征类型的体素的集合;然后计算各个体素的形状分布曲线,通过形状分布曲线匹配的方式获取几何形状相似的体素,并利用最小包围盒从中提取大小相似的体素以构成对称体素的集合;最后利用迭代最近点算法计算对称体素的旋转对称信息并利用均值漂移算法优化提取的对称.实验结果表明,体素分割结果对应构成模型的各个特征,具有明显的工程语义,对称提取结果的误差较小并具有较强的鲁棒性.     

基于S变换的心音信号特征提取

为了准确获得心音信号生理、病理信息,在对比研究各种时频分析方法的基础上,采用具有良好时频分辨率的S变换提取心音信号特征,实现心脏病辅助诊断。首先对心音信号进行包络提取、第一心音定位及心音分段,然后对一个周期信号进行S变换,最后提取变换后矩阵的统计学特征,并将该特征作为区别不同心音信号的特征向量。通过相关评价可知该方法可以有效识别不同心音信号。     

Multiple linear feature detection based on multiple-structuring-element center-surround top-hat transform

Linear feature detection is an important technique in different applications of image processing. To detect linear features in different types of images, a simple but effective algorithm based on a multiple-structuring-element center-surround top-hat transform is proposed. The center-surround top-hat transform is discussed and analyzed. Based on the properties of this transform for image feature detection, multiple structuring elements are constructed corresponding to the possible linear features at different directions. The whole algorithm is divided into four parts. First, the algorithm uses the center-surround top-hat transform to detect all the possible linear features at different directions through constructing multiple structuring elements. Second, the detected linear feature regions at each direction are processed by a closing operation to remove the possible holes or unconnected regions. Third, the processed results of the detected linear feature regions at all directions are combined to form all the possible detected linear feature regions. Fourth, the combined result is refined by using some simple operations to form the final result. Experimental results on different types of images from different applications verified the effective performance of the proposed algorithm. Moreover, the experimental results indicate that the proposed algorithm could be used in different applications.     

Rotation- and scale-invariant feature extraction by a diffractive optical inner-product transform

A diffractive optical element that is capable of extracting rotation- and scale-invariant features from input images is presented. The diffractive optical element performs an inner product between the input image and a set of basis functions. This set of basis functions is derived from that used in the Fourier-Mellin transform. Simulated results from scaled and rotated versions of two input images are presented.     

基于Gabor小波变换的汉字识别方法

基于Gabor滤波器时频局域性和方向选择性对应在汉字图像上即是对笔划宽度和方向的选择这一认识,考虑到笔划宽度多峰值、笔划是方向的特点,利用二维Gabor小波的多分辨率特性,对汉字图像进行了多尺度多方向分析,提取多个子平面的滤波器输出系数作为统计特征,实现了字符图像的高性能识别,为开展低质量、低分辨率的汉字字符图像识别研究提供了新方法。实验结果表明,用这种方法识别低分辨率的字符图像,效果优于其他方法。