A novel rotated sigmoid weight function for higher performance in heterogeneous deformation measurement with digital image correlation

乙状窦函数 数字图像相关 计算机科学 变形(气象学) 图像(数学) 功能(生物学) 相关性 人工智能 计算机视觉 数字图像 材料科学 光学 图像处理 数学 几何学 物理 复合材料 进化生物学 人工神经网络 生物
作者
Xiaosen Ye,Jiaqing Zhao
出处
期刊:Optics and Lasers in Engineering [Elsevier BV]
卷期号:159: 107214-107214 被引量:5
标识
DOI:10.1016/j.optlaseng.2022.107214
摘要

• A new rotated sigmoid weight (RSW) function based DIC (RSW-DIC) is proposed to tackle the problem that the results of the weight function based DIC are influenced by the initial subset size (here ‘initial’ means ‘original’, the initial subset size keepsconstant over iterations, but the equivalent subset size is varying according to the weight distribution). The RSW-DIC has much better performance than other weight function based methods for unknown heterogeneous displacement. • The spatial resoltion of the proposed method is much better than other DIC methods using the same subset and shape functionorder. • The proposed RSW-DIC has better noise resistance than other participated methods. The Measurement resolution can be reduced by using a big initial subset size without increasing spatial resolution much. Conventional digital image correlation (C-DIC) combined with a rotated Gaussian weight (RGW) function for subset has demonstrated attractive ability in resolving heterogeneous deformation parameters. To further improve the performance, the selection of an optimum weight function becomes the key issue. In this paper, a novel rotated sigmoid weight (RSW) function is proposed. RSW function aims to get a more uniform weight distribution near the subset center, and to realize the continuous change of the equivalent subset size as well. The performance of the RSW function is compared with the Gaussian weight (GW) function, RGW function, the rotated inverse distance weight (RIDW) function and the inverse distance square weight (RIDSW) function through Star 5 image set from the DIC challenge 2.0 and the simulated image set. A total of six methods with different weight functions and rotated weights are systematically compared. The experiment results clearly show that DIC combined with RSW function (i.e. RSW-DIC) has the best spatial resolution without sacrificing much measurement resolution. The spatial resolution of RSW-DIC is only about half of the other methods for both first- and second-order shape functions when a big initial subset size is adopted.
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