  Digital image correlation (DIC) is a widely used optical metrology for
quantitative deformation measurement due to its non-contact, low-cost, highly
precise feature. DIC relies on nonlinear optimization algorithm. Thus it is
quite important to efficiently obtain a reliable initial guess. The most widely
used method for obtaining initial guess is reliability-guided digital image
correlation (RG-DIC) method, which is reliable but path-dependent. This
path-dependent method limits the further improvement of computation speed of
DIC using parallel computing technology, and error of calculation may be spread
out along the calculation path. Therefore, a reliable and path-independent
algorithm which is able to provide reliable initial guess is desirable to reach
full potential of the ability of parallel computing. In this paper, an
algorithm used for initial guess estimation is proposed. Numerical and real
experiments show that the proposed algorithm, adaptive incremental
dissimilarity approximations algorithm (A-IDA), has the following
characteristics: 1) Compared with inverse compositional Gauss-Newton (IC-GN)
sub-pixel registration algorithm, the computational time required by A-IDA
algorithm is negligible, especially when subset size is relatively large; 2)
the efficiency of A-IDA algorithm is less influenced by search range; 3) the
efficiency is less influenced by subset size; 4) it is easy to select the
threshold for the proposed algorithm.
