Title: Fast, Accurate and Fully Parallelizable Digital Image Correlation,
Abstract: 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.