基于改进轮廓提取的Hough变换椭圆检测方法

分析椭圆目标在成像平面上的特点,提出一种基于改进轮廓提取的Hough变换椭圆检测方法,可以有效地在图像中提取出椭圆的轮廓。对边缘检测后的图像进行边缘点分类,去除交叉点和孤立点,保存端点;根据端点,提取图像中的非封闭轮廓,再扫描图像,提取剩余的封闭轮廓;针对随机Hough变换,根据每条轮廓的长度判断该轮廓需要采样计算的次数,在每个循环的内部,设定阈值来判断每组椭圆参数的统计值,用以跳出循环。实验结果表明,与普通随机Hough变换相比,该算法的内存占用量与计算量大大减少。     

基于对称点搜索的一维RHT在椭圆检测中的应用

为了降低传统椭圆检测方法对于边缘梯度精度和椭圆完整性的过高要求,有效地提取出图像中的多个未知椭圆,该文提出了一种基于对称点搜索的仅需要一维参数空间累加阵列的快速椭圆检测方法。该方法充分利用椭圆的对称性,借助随机获取的对称点组确定候选椭圆的3个待定参数,并引入基于椭圆生成和膨胀匹配的候选椭圆确认方法用于克服椭圆形变对候选椭圆确认造成的影响。实验结果表明该文算法与基于RHT的其他具有代表性的椭圆检测方法相比具有检测速度快,精度高,抵抗椭圆的部分缺失和形变能力强等优点。     

基于灰度导数加权的椭圆高精度提取算法

为了满足姿态测量时对椭圆合作目标的高精度定位要求,提出了一种基于灰度导数加权的椭圆拟合算法(WGG).首先,在进行预处理之后的二值图像上提取初始边缘点,采用简单的代数拟合方法得到椭圆参数;其次,考虑二值化过程中的不确定因素会对提取边界点带来干扰,根据原始图缘中边缘灰度的渐变姓,利用边缘点的灰度导数值对其进行高斯函数拟合;最后,求取每一个边缘点的正交相切点,并构造优化目标函数求取椭圆最优参数.实验结果表明,对测试图像提取出采的椭圆拟合误差能达到0.013像素以内,与真实值点的平均误差为0.017像素,真实图像中椭圆的平均拟合误差为0.008 6像素.对于不同的外界环境该方法都能获得更精确的亚像素边缘和椭圆中心,具有很高的精确性和鲁棒性.     

用改进的随机Hough变换检测胸水图像中的细胞

针对胸水细胞显微图像的特点提出一种改进的随机Hough变换(MRHT,modified randomized hough transform)检测圆和椭圆的算法.该算法分为两步:利用椭圆的几何性质,确定可能的椭圆中心的位置;在限定区域内,通过多次3点随机抽样,计算椭圆除中心坐标外的其他3个参数.研究表明,该算法可以同时检测多个圆和椭圆,可以从胸水细胞显微图像的复杂背景中较为准确地提取圆形和椭圆形细胞.实验结果表明,该算法有较高的检测效率,检测精度和较强的鲁棒性.     

基于形状约束Snake模型的人脸轮廓提取

提出了一种具有形状约束的人脸轮廓提取算法,该算法根据人脸形状近似椭圆的特征,在S12ake的能量函数中加入形状能量,使控制点间的联系得到加强,以提高收敛结果的准确性,实验结果表明,提出的方法能够较好地提取复杂背景中的人脸轮廓,是一种有效的方法.     

基于Hough变换的椭圆检测改进算法

在背景复杂的图像中,针对多椭圆检测时椭圆中心定位不准、虚假椭圆过多的缺点,提出一种基于Hough变换的改进算法。该算法对参数空间和Hough变换计算的改进提高了椭圆检测的准确度,并利用参数方程判断候选椭圆的真假。实验结果表明,该检测方法具有较强地抗干扰能力,能够在复杂的环境中准确快速地检测出多个椭圆。     

椭圆球面波脉冲信号快速设计方法

在基于椭圆球面波的非正弦短波通信系统中,椭圆球面波脉冲信号原设计方法存在 计算量大、计算时间长的问题。为了解决此问题,提出了椭圆球面波脉冲信号快速设计方法 。该方法利用基于带通采样的椭圆球面波函数数值解法,求得椭圆球面波函数近似数值解, 椭圆球面波函数在时间轴上向右平移,可得椭圆球面波脉冲信号。理论分析和仿真结果表明 ,快速设计方法能使计算量和计算时间降低2～4个数量级,同时椭圆球面波脉冲信号的时域 、频域和正交性能还能保持与原设计方法相当。     

基于Nyquist采样的椭圆球面波函数数值解法

为了解决椭圆球面波函数数值解法时积分方程该如何离散化以及采用何种方法求取实对称矩阵的特征值和特征向量问题,提出了基于奈奎斯特采样的椭圆球面波函数数值解法。该数值解法利用奈奎斯特采样定理确定的采样频率对积分方程进行离散化,利用Jacobi方法求取实对称矩阵的全部特征值和相应的特征向量,求得的特征向量就是椭圆球面波函数的近似数值解。对基于奈奎斯特采样的椭圆球面波函数数值解法进行了理论推导、性能分析和仿真。理论分析和仿真结果表明,该数值解法方法简单,实用性强,求得的椭圆球面波函数精度高,椭圆球面波函数之间正交性好。     

摄影测量中一种基于HEIV模型的编码识别方法

在摄影测量中,经常发生椭圆点检测错误从而影响三维场景的匹配问题,甚至导致三维重建任务的失败。针对该问题,在基于HEIV(Heteroscedastic Errors-in-Variables)椭圆拟合方法的基础上,设计了一种可纠错的编码点,将信息论中的RS可纠错编码方法用于编码点的设计,并对编码点的解算及提取进行分析。实验表明,该方法大大提高了编码点的抗噪性,在编码出现错误时可以实现纠错,并且能够纠正多位错误,较好地解决了实际应用中编码点识别错误的问题。     

基于同态加密与改进ECC的无线传感器网络安全聚集算法

针对WSN数据聚集安全算法通信与计算成本较高的问题,提出一种基于同态加密与改进椭圆曲线密码学的WSN安全数据聚集算法。首先,采用有态公钥加密算法降低加密算法的计算与通信成本,并且分别使用同态加密与消息认证码聚集密文与签名;然后,基站可提取每个节点的数据,验证消息的完整性、发送者的合法性以及识别恶意节点;最终,基于TelosB平台进行实验,并且使用快速算法实现椭圆曲线标量乘法降低该方法的执行时间。实验结果表明,该方法在保证数据安全性的前提下,计算成本与通信成本均明显低于其他同类型算法。     

A propagating interface model strategy for global trajectory planning among moving obstacles

A global trajectory planning method which employs a collision-trend index and a propagating interface model to perform mobile robot navigation is presented in this paper. To simplify the mathematical representation and geometrical approximation, all the objects in the workspace are modeled as ellipses. Using a series of geometrical transformations between the ellipses, which represent the mobile robot and obstacles, the computational complexity of collision detection in trajectory planning can be reduced tremendously. To keep the front propagating in the normal direction with positive propagating speed, the collision-trend index plays an important role in determining the propagating speed for a front over workspace. The index is obtained by mapping the geometrical relationship between the ellipses into the profile of a Gaussian distribution. Several simulations to demonstrate the performance of the proposed method are given. The results reveal that the proposed method is always able to generate an optimal collision-free trajectory for a mobile robot navigating in an environment with dynamic and static obstacles.     

Consistent symmetric axis method for robust detection of ellipses

Ellipse detection is an interesting issue in machine vision researches and many approaches have been suggested to determine the parameters. Recently, a symmetric axis method has been proposed to determine the centre of an ellipse, but other parameters are determined otherwise. A consistent symmetric axis method is proposed that utilises the information inherent in the symmetric axes throughout the entire process to compute all of the parameters. A closed-form solution is obtained, hence the calculation of the parameters is fast. In addition, since the determination of the symmetric axes involves a set of points on the ellipse, the procedure is robust. Experiments with disjoint ellipses, interacting ellipses, defective ellipses and real objects indicate the effectiveness of the proposed method both theoretically and practically     

Chord bisection strategy for fast ellipse location

It is shown that a previously proposed rapid sampling technique can be extended using a strategy based on chord bisection. The strategy is characterised by very high speed of operation coupled with accurate location, and can be matched to the level of robustness required by the application. The new technique has been tested on images in which the ellipses are cereal grains with close to 2:1 aspect ratio. But it appears to be suitable for locating ellipses of any eccentricity with near ultimate speed of processing     

基于误差理论的椭圆识别快速算法

圆锥曲线的自动识别由于其在实际生活中的广泛应用，一直以来是图像理解领域广泛讨论的课题。本文根据数据处理领域中的误差理论，提出了一种可同时适用于圆与椭圆这两种圆锥曲线识别的新算法，并在程序控制下自动给出识别结果和有关的精度评定。实验证明，该算法针对实际应用中大小、方向各异的两类圆锥曲线的检测，不需要做出任何假设或附加任何条件，也不需要任何累加计数，具有检测速度快、精度高的特点，易于执行，实用性强。     

Blind phase recovery for QAM communication systems

In this paper, a novel phase estimator that can be employed for both square and cross Quadrature Amplitude Modulation (QAM) based digital transmission is presented. It does not need gain control and requires only the knowledge of the type of the transmitted symbol constellation, i.e., square or cross. It is based on the evaluation of the fourth power of the received data and the measurement of the orientation of the concentration ellipses of the bivariate Gaussian distribution having the same second-order moments. The analytical evaluation of the estimation error as well as of the asymptotic variance is provided. Experimental results outline the good performance of the estimator described here, which is superior to that of well-known phase estimation methods. Finally, it is outlined how the eccentricity of the concentration ellipses can be used to devise a test for detecting the constellation type.     

基于哈夫变换的根系分布率计算

通过数码摄像机绕拍摄植物根系一周得到多幅二维图像,定量分析植物根系的三维构型.通过拍摄所得的三维图像,利用图像分割算法和骨架抽取算法,提取根系细化后的拓扑结构,然后根据广义哈夫变换,计算出根系各点旋转所得的椭圆的相关参数,再利用椭圆的右端点,计算得到根系在不同介质层和介质方向的分布情况,并在此基础上,提出一种改进的方法快速确定椭圆参数空间,大幅提升椭圆检测的速度,使得根系的三维构型定量分析可以真正应用于农业领域研究中,为农学家进一步研究植物根系及植物间作套种提供技术支撑.     

平面有效散（反）射区的计算公式

三本著名著作给出的平面有效散（反）射区（椭圆）的位置与尺寸的计算公式都有不正确之处，本文推导出它们的正确计算公式 。     

Intravital leukocyte detection using the gradient inverse coefficient of variation

The problem of identifying and counting rolling leukocytes within intravital microscopy is of both theoretical and practical interest. Currently, methods exist for tracking rolling leukocytes in vivo, but these methods rely on manual detection of the cells. In this paper we propose a technique for accurately detecting rolling leukocytes based on Bayesian classification. The classification depends on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. The leukocyte detection process consists of three sequential steps: the first step utilizes an ellipse matching algorithm to coarsely identify the leukocytes by finding the ellipses with a locally maximal GICOV. In the second step, starting from each of the ellipses found in the first step, a B-spline snake is evolved to refine the leukocytes boundaries by maximizing the associated GICOV score. The third and final step retains only the extracted contours that have a GICOV score above the analytically determined threshold. Experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods. The proposed GICOV method achieves 78.6% leukocyte detection accuracy with 13.1% false alarm rate.     

Canonical coordinates and the geometry of inference, rate, andcapacity

Canonical correlations measure the cosines of principal angles between random vectors. These cosines multiplicatively decompose concentration ellipses for second-order filtering and additively decompose the information rate for the Gaussian channel. More over, they establish a geometrical connection between error covariance, error rate, information rate, and principal angles. There is a limit to how small these angles can be made, and this limit determines the channel capacity