基于机器视觉的钢拱架定位检测方法

针对岩石隧道掘进机(TBM)钢拱架拼接施工过程中的定位问题,提出一种基于机器视觉的钢拱架铰接孔定位检测方法。根据钢拱架拼接的现场环境,设计图像采集系统;使用具有旋转不变性的归一化的互相关值(NCC)模板匹配算法,可快速匹配钢拱架铰接孔所在的感兴趣区域;基于一维像素序列上的灰度梯度确定铰接孔外轮廓边缘点,并使用基于随机抽样一致性(RANSAC)算法的椭圆拟合方法确定钢拱架铰接孔圆心,实现钢拱架定位,为钢拱架拼接提供位姿信息。结果表明：该方法可实现钢拱架铰接孔的高效率、高精度定位检测。     

用阿基米德螺旋线逼近椭圆之节点划分方法

提出了一个椭圆曲线节点划分的新方法,用于数控车削此类零件编程的数学预处理。该方法的实质是用阿基米德螺旋线去逼近离散椭圆。计算结果证明,用此方法可显著减小逼近误差和避免大量的数据处理和存储。     

B超诊断中几种基本测量方法的研究

基于PC平台的辅助测量为B超诊断提供了更高的灵活性和精确度。针对B型基本测量方法,阐述B超诊断中几种基本测量方式的实现,包括距离、角度、曲线周长和面积、椭圆法测周长、面积和体积,具有较强的实际应用价值。其软件实现环境为Windows XP操作系统,Visual C 6.0编程环境。B超诊断对算法的实现效率和精确程度要求较高,算法的优化需进一步考虑。     

基于GRA-GRNN模型的露天金属矿爆破后矿岩界线位移分析

为了避免露天金属矿爆破后导致爆堆边缘矿石品位贫化损失,需要根据最低品位阈值重新计算矿岩边界,而影响矿岩边界发生改变因素众多,需要确定主要影响因素。因此,利用爆堆爆破前地形方向和爆堆地质数据,构建灰色关联-广义回归神经网络模型(GRA-GRNN)分析爆堆矿岩边界变化主要影响因素。首先对爆堆钻孔品位数据使用析取克里金法进行空间插值,并根据矿山工艺最低品位阈值提取爆破前的矿岩边界;其次,将爆破前后的数字DEM模型进行求差,求得爆破后的爆堆数字DEM模型,并构建爆破前后爆堆数字DEM模型空间分布椭圆,从而确定爆堆爆破后的水平形变方向;对影响爆堆爆破后形变的可能因素进行提取,并应用GRA-GRNN模型选取主要影响因素及对其强度进行分析,并将其结果与BP神经网络模型预测结果进行了对比。从实验结果可知,影响爆堆爆破后形变强度排前三的因素为:爆破前地形方向、爆孔排距和列距,强度分别为0.880、0.760和0.755,预测结果优于BP模型。     

椭圆齿轮动态特性仿真研究

研究了椭圆齿轮的动力学问题.以较为成熟的圆齿轮动力学研究成果作为研究基础,提出了包括离心力因素和扭转加速度因素的力学分析,使用拉格朗日键合图方法,建立了相应的动力学模型,并使用Simulink软件对不同偏心率的椭圆齿轮进行了仿真和对比分析.     

Applying the Hough transform pseudo-linearity property to improve computing speed

This work describes a general method of acceleration of the convergence of the Hough transform based, on the one hand, on an improvement of the image analysis speed, and, on the other hand, on the space undersampling of the image. This method is used in image processing to extract lines, circles, ellipses or arbitrary shapes. The results presented are applied to the detection of straight-line segments and ellipses, but can be extended to any type of transform.     

一种新的大型四肢动物体姿态识别方法

文中以对鹿体姿态的识别为例,针对鹿体姿态的特点,提出了一种新的可应用于大型四肢动物体姿态识别的方法。首先采用数学形态学的方法对图像进行了处理,获得了鹿体图像的准确轮廓,然后分别用四个椭圆对所获得的图像中鹿的颈部、去除颈部的躯干以及四肢进行拟合,最后利用线性代数的知识分别求出这四个用以表征鹿体姿态信息的椭圆的长短轴长度和方向。     

库尔勒市区GPS高程与水准高程的比较分析

介绍了利用GPS测定地面点高程的基本原理,将库尔勒市区GPS E级网的GPS高程与水准高程进行了比较分析,讨论了GPS高程的拟合方法和所能达到的精度.     

Performance evaluation of iterative geometric fitting algorithms

The convergence performance of typical numerical schemes for geometric fitting for computer vision applications is compared. First, the problem and the associated KCR lower bound are stated. Then, three well-known fitting algorithms are described: FNS, HEIV, and renormalization. To these, we add a special variant of Gauss-Newton iterations. For initialization of iterations, random choice, least squares, and Taubin's method are tested. Simulation is conducted for fundamental matrix computation and ellipse fitting, which reveals different characteristics of each method.     

An improved SAR-GMTI method based on eigen-decomposition of the sample covariance matrix

An improved two-channel Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) method based on eigen-decomposition of the covariance matrix is investigated. Based on the joint Probability Density Function (PDF) of the Along-Track Interferometric (ATI) phase and the similarity between the two SAR complex images, a novel ellipse detector is presented and is applied to the indication of ground moving targets. We derive its statistics and analyze the performance of detection process in detail. Compared with the approach using the ATI phase, the ellipse detector has a better performance of detection in homogenous clutter. Numerical experiments on simulated data are presented to validate the improved performance of the ellipse detector with respect to the ATI phase approach. Finally, the detection capability of the proposed method is demonstrated by measured SAR data.