Full-automatic seed point selection and initialization for digital image correlation robust to large rotation and deformation

Abstract

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.