Fast, Accurate and Fully Parallelizable Digital Image Correlation

10/12/2017 ∙ by Peihan Tu, et al. ∙ 0

Digital image correlation (DIC) is a widely used optical metrology for surface deformation measurements. DIC relies on nonlinear optimization method. Thus an initial guess is quite important due to its influence on the converge characteristics of the algorithm. In order to obtain a reliable, accurate initial guess, a reliability-guided digital image correlation (RG-DIC) method, which is able to intelligently obtain a reliable initial guess without using time-consuming integer-pixel registration, was proposed. However, the RG-DIC and its improved methods are path-dependent and cannot be fully parallelized. Besides, it is highly possible that RG-DIC fails in the full-field analysis of deformation without manual intervention if the deformation fields contain large areas of discontinuous deformation. Feature-based initial guess is highly robust while it is relatively time-consuming. Recently, path-independent algorithm, fast Fourier transform-based cross correlation (FFT-CC) algorithm, was proposed to estimate the initial guess. Complete parallelizability is the major advantage of the FFT-CC algorithm, while it is sensitive to small deformation. Wu et al proposed an efficient integer-pixel search scheme, but the parameters of this algorithm are set by the users empirically. In this technical note, a fully parallelizable DIC method is proposed. Different from RG-DIC method, the proposed method divides DIC algorithm into two parts: full-field initial guess estimation and sub-pixel registration. The proposed method has the following benefits: 1) providing a pre-knowledge of deformation fields; 2) saving computational time; 3) reducing error propagation; 4) integratability with well-established DIC algorithms; 5) fully parallelizability.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 4

page 5

page 6

page 7

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.