Pan Bing, Xie Huimin, Wang Zhaoyang, Qian Kemao, Wang Zhiyong
FML, Dept of Engineering Mechanics, Tsinghua University, Beijing, China.
Opt Express. 2008 May 12;16(10):7037-48. doi: 10.1364/oe.16.007037.
Digital Image Correlation (DIC) is a flexible and effective technique to measure the displacements on specimen surfaces by matching the reference subsets in the undeformed image with the target subsets in the deformed image. With the existing DIC techniques, the user must rely on experience and intuition to manually define the size of the reference subset, which is found to be critical to the accuracy of measured displacements. In this paper, the problem of subset size selection in the DIC technique is investigated. Based on the Sum of Squared Differences (SSD) correlation criterion as well as the assumption that the gray intensity gradients of image noise are much lower than that of speckle image, a theoretical model of the displacement measurement accuracy of DIC is derived. The theoretical model indicates that the displacement measurement accuracy of DIC can be accurately predicted based on the variance of image noise and Sum of Square of Subset Intensity Gradients (SSSIG). The model further leads to a simple criterion for choosing an optimal subset size for the DIC analysis. Numerical experiments have been performed to validate the proposed concepts, and the calculated results show good agreements with the theoretical predictions.
数字图像相关（DIC）是一种灵活且有效的技术，通过将未变形图像中的参考子集与变形图像中的目标子集进行匹配来测量试样表面的位移。使用现有的DIC技术时，用户必须依靠经验和直觉手动定义参考子集的大小，而这被发现对测量位移的准确性至关重要。本文研究了DIC技术中的子集大小选择问题。基于平方差之和（SSD）相关准则以及图像噪声的灰度强度梯度远低于散斑图像这一假设，推导了DIC位移测量精度的理论模型。该理论模型表明，基于图像噪声方差和子集强度梯度平方和（SSSIG）可以准确预测DIC的位移测量精度。该模型进一步得出了一个用于为DIC分析选择最佳子集大小的简单准则。已进行数值实验来验证所提出的概念，计算结果与理论预测吻合良好。
