A prediction–correction method for fast and accurate initial displacement field estimation in digital image correlation. (1st October 2022)

• Record Type: Journal Article
• Authors: Yang, Hongfan; Wang, Sihan; Xia, Huanxiong; Liu, Jianhua; Wang, Aimin; Yang, Ye
• Is Part Of: Measurement science & technology. Volume 33:Number 10(2022)
• Publication Date: 2022-10-01
• Subjects: digital image correlation -- prediction–correction -- displacement -- extrapolation scheme -- monotonous search -- three-dimensional reconstruction; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac7a06
• Languages: English
• ISSNs: 0957-0233

Abstract

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%.