数字图像相关(DIC)法中初值选取的好坏对图像亚像素位移的搜索效率、算法的收敛速度有着很大的影响。提出了一种基于KAZE特征匹配的数字图像相关法,即KAZE算法。KAZE算法与传统的特征检测算法的区别在于其是通过建立非线性尺度空间来消除噪声与提取特征点,可以有效地避免边界模糊和细节丢失。通过KAZE算法匹配出图像变形前后特征点对坐标,使用仿射变换估计初始变形参数;使用反向组合高斯牛顿法对估计初值进行迭代优化。结果表明,在不损失精度的情况下,该方法相对于传统方法具有更高的搜索效率。

The selection of initial values in digital image correlation (DIC) method has a great influence on the search efficiency of image subpixel displacement and the algorithmic convergence speed. A new digital image correlation method based on KAZE feature matching, namely KAZE algorithm, is proposed in this paper. Compared with the traditional feature detection algorithm, the KAZE algorithm can eliminate noise and extract feature points by establishing nonlinear scale space, which can effectively avoid boundary blurring and detail loss. The coordinates of feature points before and after image deformation are matched by the KAZE algorithm, and the initial deformation parameters are estimated by affine transformation; the inverse combined Gaussian Newton method is used to iteratively optimize the estimated initial values. The results show that this method has higher search efficiency than traditional methods without precision loss.