Propagation function for accurate initialization and efficiency enhancement of digital image correlation

Propagation function for accurate initialization and efficiency enhancement of digital image correlation
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用于精确初始化和提高数字图像相关效率的传播函数

DOI:
10.1016/j.optlaseng.2012.06.017
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发表时间:
2012-12
影响因子:
4.6
通讯作者:
Yan Qiu Chen
Yan Qiu Chen
中科院分区:
工程技术2区
文献类型:
--
作者:
Yihao Zhou;Yan Qiu Chen

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数字图像相关(DIC)通过优化相关像素的亮度来匹配两幅图像中的对应位置。迭代算法被证明是解决优化问题的最有效的方法。然而,它需要对未知参数进行准确的初始猜测。现有的方法直接使用当前点的变形参数作为其相邻点的初始值。我们在本文中说明,直接传递的参数不产生准确的初始化时,变形存在于当前点的子集区域。相反,我们提出了一个解析的传播函数,它与相邻点的参数。对于广泛使用的一阶和二阶形状函数,推导出传播函数的闭合形式表达式,并使用合成散斑图像进行验证。与现有方法的比较表明,传播函数提供了更准确的初始估计所需的参数,并需要更少的迭代优化收敛。仿真和真实实验的测试也表明,传播函数可以处理大变形,允许广泛的步长,并减少计算成本。
Digital image correlation (DIC) matches corresponding locations in two images by optimization correlation of the intensity of related pixels. Iterative algorithm is proved to be the most effective method to solve the optimization. However, it requires accurate initial guess of the unknown parameter. Existing methods directly use the deformation parameter of the current point as the initial for its adjacent points. We illustrate in this paper that direct transfer of parameter does not produce accurate initialization when deformation is present in the subset area of the current point. Instead, we propose an analytic propagation function which relates the parameters of adjacent points. Closed-form expressions of the propagation function are derived for both widely used first-order and second-order shape function, and they are validated using synthetic speckle images. Comparison with existing method shows that the propagation function provides more accurate initial estimate of the desired parameters and requires fewer iterations for the optimization to converge. Tests on simulated and real-world experiments also show that the propagation function can deal with large deformation, allow a wide range of step sizes, and reduce computational cost.
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