Elimination of systematic error in digital image correlation caused by intensity interpolation by introducing position randomness to subset points

•A random subset strategy was presented.•Random-subset-based subpixel registration algorithms were developed.•An increase in random offset will give rise to a decrease in systematic error.•The systematic error can be virtually eliminated if an appropriate random offset is selected. A strategy called...

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Bibliographic Details
Published in:Optics and lasers in engineering Vol. 114; pp. 60 - 75
Main Authors: Su, Yong, Zhang, Qingchuan, Fang, Zheng, Wang, Yaru, Liu, Yang, Wu, Shangquan
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.03.2019
Subjects:
Bias reduction
Digital image correlation
Random subset
Systematic error
Bias reduction
Random subset
Systematic error
Digital image correlation
ISSN:0143-8166, 1873-0302
Online Access:Get full text
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Summary:•A random subset strategy was presented.•Random-subset-based subpixel registration algorithms were developed.•An increase in random offset will give rise to a decrease in systematic error.•The systematic error can be virtually eliminated if an appropriate random offset is selected. A strategy called random subset is presented to reduce systematic error in digital image correlation caused by intensity interpolation. In classical digital image correlation techniques, the subset points fall at integer locations; by contrast, the proposed random subset strategy imposes random offsets on the conventional locations, and consequently the subset points are located at non-integer positions. The introduction of position randomness to subset points makes the random subset strategy distinctly different from the classical digital image correlation technique. The random offsets can follow various probability distributions. In this work, two types of random offset were studied as examples, and the maximum of the random offsets was adjusted to modify performance. Using the random subset strategy, random-subset-based subpixel registration algorithms were developed in this study, including the random-subset-based forward additive Gaussian–Newton algorithm and the random-subset-based inverse compositional Gauss–Newton algorithm. The measurement accuracy of the random subset-based subpixel registration algorithms was investigated. Numerical simulations and real-world experiments demonstrated that as the maximum of the random offsets increases, the amplitude of the sinusoidal-shaped systematic error changes from positive to negative. Hence, if an appropriate random offset is selected, systematic error can be considerably reduced. In fact, the accuracy enhancement is greater than one order of magnitude, and systematic error is virtually eliminated.
ISSN:0143-8166
1873-0302
DOI:10.1016/j.optlaseng.2018.10.012