Automated initial guess in digital image correlation aided by Fourier–Mellin transform

Bing Pan; Yuejiao Wang; Long Tian

doi:10.1117/1.OE.56.1.014103

9 January 2017 Automated initial guess in digital image correlation aided by Fourier–Mellin transform

Bing Pan, Yuejiao Wang, Long Tian

Author Affiliations +

Optical Engineering, Vol. 56, Issue 1, 014103 (January 2017). https://doi.org/10.1117/1.OE.56.1.014103

Abstract

The state-of-the-art digital image correlation (DIC) method using iterative spatial-domain cross correlation, e.g., the inverse-compositional Gauss–Newton algorithm, for full-field displacement mapping requires an initial guess of deformation, which should be sufficiently close to the true value to ensure a rapid and accurate convergence. Although various initial guess approaches have been proposed, automated, robust, and fast initial guess remains to be a challenging task, especially when large rotation occurs to the deformed images. An integrated scheme, which combines the Fourier–Mellin transform-based cross correlation (FMT-CC) for seed point initiation with a reliability-guided displacement tracking (RGDT) strategy for the remaining points, is proposed to provide accurate initial guess for DIC calculation, even in the presence of large rotations. By using FMT-CC algorithm, the initial guess of the seed point can be automatically and accurately determined between pairs of interrogation subsets with up to ±180 deg of rotation even in the presence of large translation. Then the initial guess of the rest of the calculation points can be accurately predicted by the robust RGDT scheme. The robustness and effectiveness of the present initial guess approach are verified by numerical simulation tests and real experiment.

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Bing Pan, Yuejiao Wang, and Long Tian "Automated initial guess in digital image correlation aided by Fourier–Mellin transform," Optical Engineering 56(1), 014103 (9 January 2017). https://doi.org/10.1117/1.OE.56.1.014103

Received: 8 September 2016; Accepted: 16 December 2016; Published: 9 January 2017

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