Abstract Current iterative digital image correlation (DIC) algorithms can efficiently converge at the deformation vector with high accuracy when they are fed with reliable initial guess. Thus, the adaptability of DIC method is dominated to a large extent by the estimation of initial guess. In recent years, image feature-based technique, especially the scale-invariant feature transform (SIFT), was introduced to DIC for the estimation of initial guess in the case of large and complex deformation, due to its robustness in handling the images with translation, rotation, scaling, and localized distortion. However, feature extraction and matching in SIFT are very time consuming, which limits the applications of the SIFT-aided DIC. In this study, we developed a SIFT-aided path-independent DIC method and accelerated it by introducing the parallel computing on graphics processing unit (GPU) or multi-core CPU. In our method, SIFT features are used to estimate the initial guess for the inverse compositional Gauss-Newton (IC-GN) algorithm at each point of interest (POI). The experimental study shows that the developed method can deal with large and inhomogeneous deformation with high accuracy. Parallel computing (especially on GPU) accelerates significantly the proposed DIC method. The achieved computation speed satisfies the need for real-time processing with high resolution for the images of normal sizes.

中文翻译：

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通过并行计算加速 SIFT 辅助的路径无关数字图像相关

摘要 当前的迭代数字图像相关（DIC）算法在输入可靠的初始猜测值时，可以高效地收敛于变形向量并具有高精度。因此，DIC 方法的适应性在很大程度上取决于初始猜测的估计。近年来，基于图像特征的技术，尤其是尺度不变特征变换（SIFT），由于其在处理具有平移的图像方面的鲁棒性，被引入到 DIC 中，用于估计大而复杂变形情况下的初始猜测、旋转、缩放和局部失真。然而，SIFT 中的特征提取和匹配非常耗时，这限制了 SIFT 辅助 DIC 的应用。在这项研究中，我们开发了一种 SIFT 辅助的路径无关 DIC 方法，并通过在图形处理单元 (GPU) 或多核 CPU 上引入并行计算来加速它。在我们的方法中，SIFT 特征用于估计每个兴趣点 (POI) 处的逆合成高斯-牛顿 (IC-GN) 算法的初始猜测。实验研究表明，所开发的方法可以高精度地处理大且不均匀的变形。并行计算（尤其是在 GPU 上）显着加速了所提出的 DIC 方法。达到的计算速度满足了对正常尺寸图像进行高分辨率实时处理的需要。SIFT 特征用于估计每个兴趣点 (POI) 处的逆合成高斯-牛顿 (IC-GN) 算法的初始猜测。实验研究表明，所开发的方法可以高精度地处理大且不均匀的变形。并行计算（尤其是在 GPU 上）显着加速了所提出的 DIC 方法。达到的计算速度满足了对正常尺寸图像进行高分辨率实时处理的需要。SIFT 特征用于估计每个兴趣点 (POI) 处的逆合成高斯-牛顿 (IC-GN) 算法的初始猜测。实验研究表明，所开发的方法可以高精度地处理大且不均匀的变形。并行计算（尤其是在 GPU 上）显着加速了所提出的 DIC 方法。达到的计算速度满足了对正常尺寸图像进行高分辨率实时处理的需要。