梯度-曲面拟合法的亚像素位移测量算法

数字图像相关方法作为光学测量的一种测量方式在结构变形测量中有广泛应用,其中亚像素位移测量精度成为关键。基于亚像素位移测量精度影响因素的分析,研究了计算子区灰度插值、计算窗口大小以及拟合距离对亚像素位移测量的影响。提出了一种梯度-曲面拟合法的亚像素位移测量算法,即,先采用梯度法计算出结构的亚像素位移,再对计算子区进行亚像素插值,然后调整拟合距离进行亚像素曲面拟合。最后利用计算机模拟算例验证了该方法具有良好的计算效率、计算精度及抗噪声能力。     

激光散斑的亚像素位移法计算及比较

根据激光散斑相关测量中采用的插值法、梯度算法和牛顿一拉夫森迭代法3种亚像素位移算法的特点,逐一在亚像素尺度计算出磁流体干燥过程中的同一激光散斑图的偏均值和标准差变化曲线。基于得到的偏差均值和标准差讨论了3种算法处理激光散斑图的特点,比较了3种算法处理散斑图的计算精度、稳定性和计算用时。结果显示,在小变形的亚像素位移测量时,梯度算法的精度较高,且较为稳定,计算效率也较牛顿一拉夫森算法快约9倍。因此,在进行小变形亚像素位移测量时,采用梯度算法计算激光散斑是有效的。     

基于灰度梯度正则化去噪的改进数字图像相关法

为了提高数字图像相关法的抗噪声能力,分析了灰度梯度计算误差对数字图像相关法测量精度的影响,提出采用Tikhonov正则化方法计算图像灰度梯度,进而通过反向组合高斯-牛顿(IC-GN)法计算图像的亚像素位移;基于数值仿真散斑图研究灰度梯度的相对计算误差,并分析采用Tikhonov正则化方法计算图像灰度梯度后,改进数字图像相关法的测量精度,结合实验验证所提方法在实际噪声环境中的抗噪声能力。结果表明:灰度梯度对数字图像相关法测量精度有较大影响,而采用Tikhonov正则化方法后,可以有效提高数字图像相关法的测量精度与抗噪声能力。     

基于直方图均衡化的Robinson图像边缘检测算法

针对图像边缘检测,现有Robinson相关算法存在效率低、阈值设定随机性大、易出现伪边缘等问题,提出一种改进Robinson的图像边缘检测算法。该算法利用直方图均衡化对图像进行增强,然后将Robinson算子原有的八方向梯度依照两两垂直原则组合为八组,分别计算每组梯度的范数,并取其最大值作为该像素点的梯度。最后取整幅图像的灰度均值作为阈值来识别图像的边缘像素和背景像素。实验表明,相对于现有相关算法,该算法检测结果更加清晰完整,同时避免了传统算子人为设定阈值随机性大的问题。     

基于微区统计特性的数字散斑相关测量亚像素位移梯度算法

提出了一种形式简单、求解快速、稳定性好和精度高的用于数字散斑相关方法亚像素位移求解的微区梯度算法。介绍了算法原理,并给出了四种不同模式的算法公式。采用模拟图像,介绍了四种模式在计算时间、计算子窗口时对结果的影响,并对适用范围和求解精度等方面进行了分析对比。最后的计算实例验证了算法的可行性和优越性。     

面内位移测量的基于梯度的数字图像相关方法

将整像素位移搜索和基于微区统计性质的亚像素位移梯度算法相结合的数字图像相关方法具有计算简单、求解效率高等优点。该方法的基本假设是变形前后的子区做刚体平移,这则与有位移梯度存在的实际情况相矛盾。首先分析了该基本假设的理论误差,在基于梯度的数字图像相关方法中,得出变形子区做刚体平移的假设和在Newton-Rapshon方法中子区均匀变形的假设所获得的变形子区中心位移在理论上为相同的结论。然后用四组实验来验证该方法在实际实验条件下的计算精度和稳定性,并在铝板试件的单向拉伸实验中,将该方法与N-R方法在有应变存在情况下的计算结果作比较,结果表明该方法计算的位移和理论位移符合得很好,但其计算速度和效率要远优越于N-R方法。     

基于梯度的数字体图像相关方法测量物体内部变形

数字体图像相关方法通过分析两幅以体素为单位的三维体图像获得物体内部的三维变形信息.在利用数字体图像相关方法测量物体内部变形的过程中,整体素的位移场可以通过简单的空域搜索获得,因此如何计算亚体素位移是提高该方法位移测量精度的关键.介绍了一种基于空间灰度梯度的亚体素位移测量算法,并用计算机模拟生成的可精确控制位移的三维散斑...     

基于灰度矩的CCD图像亚像素边缘检测算法研究

根据灰度矩算子在目标成像前后的矩不变特性,利用Tabatabai等人提出的前三阶灰度矩建立了亚像素边缘检测算法的完备数学模型。通过实验对该算法的有效性和检测精度进行了研究,给出了工件的实测尺寸对比结果。实验表明:基于灰度矩算子的亚像素边缘检测算法比传统算子具有更高的检测定位精度,可满足图像目标高精度实时在线测量的要求。     

光学测量中椭圆圆心定位算法研究

提出了一种椭圆圆心定位算法。先对图像中的椭圆目标进行粗定位,获取椭圆的粗定位信息,再用Canny算子对图像中的感兴趣区域进行边缘检测。根据椭圆的粗定位信息以及图像的边缘灰度分布特征,用高斯曲线拟合的方法求得精确的亚像素边缘点的坐标。针对亚像素边缘点中出现的“孤立点”以及噪声点,分别用曲率滤波和均值滤波的方法加以滤波,将滤波后的亚像素边缘点用最小二乘法加以拟合,求得精确的椭圆圆心数据。实验证明了该算法的定位精度和抑制噪声能力,通过测试算法运行时间,证明该算法具有很好的实时性。     

基于自调整测量基准图的直线电机动子位置测量累积误差消减

为解决直线电机动子位置测量中多帧图像位移叠加而产生的误差累积问题,提出一种加阈值变换基准图（TTBM）的机器视觉累积误差消减算法。首先,根据直线电机动子的一维刚体平动特点,生成并优选灰度变化更加平滑的一维散斑图像作为目标拍摄图。其次,采用相位相关算法（PCA）改进灰度梯度方法,对配准图像进行整像素平移后再计算亚像素位移,以增大基准图像与配准图像之间的位移测量范围。最后,提出自调整基准图方法,根据图像位移的测量范围设定基准图像变换阈值,降低位移叠加次数,减小累积误差,实现直线电机长行程精密位移测量。同时,研究了基准图像变换阈值对测量精度的影响,给出了基准图变换的最佳阈值选取范围。仿真和实验表明,所提方法可以有效地减小直线电机动子测量中叠加而产生的累积误差。     

A subpixel motion estimation algorithm based on digital correlation for illumination variant and noise image sequences

One of the challenges in practical subpixel motion estimation is how to obtain high accuracy with sufficient robustness to both illumination variations and additive noise. Motivated by the fact that the normalized spatial cross-correlation is invariant to illumination, we introduce a gradient-based subpixel registration method by maximizing the digital correlation (DC) function between the reference and target frames. Such DC function is remodeled with the presence of image noise, yielding that the correlation coefficient is only sensitive to noise standard variance. To fairly suppress the noise corruption, not only the target frame but also the reference one is reformulated into Taylor gradient expression with half but opposite motion vector. The final solution to motion estimates can be approximated into a closed form by reserving first-order coefficient terms of unregistered motion variables. The error trend of approximated solution is discussed. Computer simulations and actual experiments’ results demonstrate the superiority of the proposed method to the LMSE-based method and ordinary DC method when illumination variations and noise exist. Among the experiments, the influences of real subpixel translation value and noise variance degree on accuracy are studied; correspondingly, an optimized iterative idea for big translations and the recommended noise level adaptive to our method are introduced.     

Subpixel image registration with reduced bias

A backward linear digital image correlation algorithm was introduced to obtain subpixel image registration without noise-induced bias for an image set consisting of a noise-free reference image and a number of noisy current images. Furthermore, a correction procedure using additional reference images (generated by offsetting the original image to displacement increments of either half-pixels or even quarter-pixels) was proposed to reduce subpixel approximation bias in the analysis. Numerical results of six sets of synthetic images showed that the proposed algorithm was effective in improving the accuracy of subpixel image registration.     

Application of an improved subpixel registration algorithm on digital speckle correlation measurement

Digital speckle correlation method (DSCM) has been widely used in experimental mechanics to obtain the surface deformation fields. One of the challenges in practical applications is how to obtain the high accuracy with far less computation complexity. To determine the subpixel registration of the DSCM, a high efficient gradient-based algorithm is developed in this paper. The principle is described and four different modes of the algorithm are given. Based on computer-simulated images, the optimal mode of the algorithm is verified through the comparison of computation time, optimal subset-region size and sensitivity of the four modes. The influences of speckle-granule size and speckle-granule density on accuracy are studied and a quantitative estimation of the optimal speckle-granule size range is obtained. As the applications of this method, the practical deformation measurements with the rigid body translation and rotation as well as an experiment on biomechanics are presented to certify the feasibility and the validity of the algorithm.     

Performance of iterative gradient-based algorithms with different intensity change models in digital image correlation

Digital image correlation (DIC) is a non-contact and powerful tool for whole-field displacement and strain measurement in modern optical metrology. In the DIC technique, the gradient-based algorithm is one of the most commonly used sub-pixel registration algorithms due to its effectiveness and accuracy. Thus, this algorithm has been further investigated and improved by many researchers to increase its accuracy. In the existing gradient-based algorithms, there are three relation models for the gray level intensity of a point in the undeformed and deformed images: namely the constant intensity model, the linear intensity change model and the non-linear intensity change model. However, little quantitative investigation has been conducted to compare their performance. In this paper, three iterative gradient-based algorithms are given using the corresponding intensity change model and the iterative least squares (ILS) solving method. The accuracy, robustness and computational efficiency of the three algorithms for intensity variation are investigated through numerical simulation experiments. The experimental results reveal that the algorithm generated using the non-linear intensity change model is the most accurate, robust and efficient of the three algorithms and the algorithms generated using the linear and non-linear intensity change models have approximately the same accuracy when the intensity variation is small between undeformed and deformed images.     

一种基于边缘特征的亚像素投影配准算法

为了提高红外图像配准准确度,充分利用图像的边缘信息,结合线性插值,对原有的投影算法引入新的图像相似性评价函数——均方误差函数(Mean Sequare Erron,MSE),提出了基于边缘特征的亚像素投影配准算法.提取图像边缘特征明显的部分做配准能提高匹配概率,引入积分投影能有效降低算法的计算复杂度.算法中还运用了线性插值,使配准准确度达到亚像素级水平.     

基于相位相关的亚像素配准技术及其在电子稳像中的应用

介绍一种基于相位相关的图像配准方法,并将该方法应用于序列图像的运动检测,通过运动补偿,实现了序列图像的稳定输出。采用相位相关配准方法对序列图像进行亚像素精度配准,运用基于最小二乘的曲面拟合法,估计当前图像相对参考图像的亚像素级的平移量。在补偿图像运动时,为保证亚像素级的补偿精度,采用平滑算法,避免在图像补偿时出现马赛克现象。最后,对样本图像进行了亚像素级位移配准和图像补偿对比实验。实验结果表明,提出的方法可以检测到0．01pixel的运动量,最大配准误差为0．008pixel;采用亚像素级运动量补偿图像,最大误差〈0．5。     

An improved gradient-based dense stereo correspondence algorithm using guided filter

This paper presents an improved gradient-based real-time stereo correspondence algorithm using guided filter. Color intensity value is sensitive to radiometric distortions including exposure differences and illumination differences, thus the error correspondence rates of these methods are high. deMaeztu et al. [1] proposed to measure the similarity between pixels using the gradient value instead of color intensity. The method has better robustness to radiometric distortions than intensity-based local methods, but the running time is so long that it is not suitable for real-time applications, because the adaptive support weight of neighbor pixels depends on bilateral filter. Guided filter has edge-preserving character as bilateral filter, but runs faster than it, we use guided filter as adaptive support weight instead of bilateral filter of the neighbor pixels in a finite squared support window. The experimental results demonstrate that the improved algorithm performs much better compared with gradient-based method and other local methods, whether in accuracy or efficiency, according to the widely-used Middlebury stereo benchmarks, and the robustness to radiometric distortions of ours is better also.