A prediction–correction method for fast and accurate initial displacement field estimation in digital image correlation
Hongfan Yang, Sihan Wang, Huanxiong Xia, Jianhua Liu, Aimin Wang, Yang Ye · Measurement Science and Technology · 2022
Abstract Initial displacement estimation is one of the most critical issues in digital image correlation. A better initial value can greatly improve the convergence rate and accuracy of the algorithms with subpixel accuracy. This paper developed an efficient estimation method to yield high-quality initial displacement fields. This method finds the initial displacement of each subset in a prediction–correction way, in which the displacement of the seed point is found by exhaustive search, but the other subsets are first predicted by an extrapolation scheme and then corrected by a monotonous search strategy. This method was tested by extensive experiments and validated by comparing with the well-known exhaustive search and adaptive rood pattern search methods, and then it was combined with the inverse compositional Gauss–Newton algorithm to perform subpixel-optimization experiments. The results demonstrated excellent features of accuracy, effectiveness, and convergence. Finally, we presented a three-dimensional surface reconstruction experiment using the proposed method, obtaining a geometric accuracy with a relative error of 0.016%.