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首页> 外文期刊>Optics and Lasers in Engineering >Full-automatic seed point selection and initialization for digital image correlation robust to large rotation and deformation
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Full-automatic seed point selection and initialization for digital image correlation robust to large rotation and deformation

机译：数字图像相关性的全自动种子点选择和初始化鲁棒到大的旋转和变形

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摘要

Accurate initial guess plays a key role to realize full-automatic digital image correlation (DIC) analysis, especially when large rotational deformation presents in deformed images. In this work, an efficient, robust and full-automatic initial guess approach combining the speeded-up robust features (SURF) algorithm and the reliability-guided displacement tracking (RGDT) strategy is proposed, which can not only automatically select and update seed point, but also can effectively deal with target images with large deformation and rotation. The scale- and rotation-invariant SURF algorithm can extract and match a certain number of feature points from two images even though the significant deformation and rotation present. The Euclidean distance and deformation information (including displacement and major orientation rotation angle) of the best-matched point are used to choose the seed point and determine its initial guess, respectively. Then, the RGDT strategy is then employed to continue the DIC analysis of rest calculation points. Compared with existing path-dependent initial guess using RGDT, the proposed method not only can automatically select seed points without manual intervention, but also can provide accurate initial value estimation for the deformed images in the presence of large rotation and/or deformation. Furthermore, it has evident efficiency advantages over existing path-independent initial guess methods based on SIFT and RANSAC. The robustness and effectiveness of the proposed method are validated by numerical simulation tests and real experiments.
机译：准确的初始猜测起到了实现全自动数字图像相关（DIC）分析的关键作用，尤其是当大的旋转变形在变形图像中存在。在这项工作中，提出了一种高效，鲁棒和全自动的初始猜测方法，组合加速鲁棒特征（冲浪）算法和可靠性引导的位移跟踪（RGDT）策略，这不仅可以自动选择和更新种子点，还可以有效地处理具有大变形和旋转的目标图像。即使存在显着的变形和旋转，可以提取和旋转不变的冲浪算法可以提取并匹配来自两个图像的特定数量点。最佳匹配点的欧几里德距离和变形信息（包括位移和主要取向旋转角度）用于选择种子点并分别确定其初始猜测。然后，然后采用RGDT策略来继续对静止计算点的DIC分析。与使用RGDT的现有路径依赖性初始猜测相比，所提出的方法不仅可以自动选择没有手动干预的种子点，而且还可以为变形图像提供大旋转和/或变形的变形图像的准确初始值估计。此外，它对基于SIFT和RANSAC的现有路径无关的初始猜测方法具有明显的效率优势。通过数值模拟试验和实验验证了所提出的方法的鲁棒性和有效性。

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