Digital Image Correlation with Enhanced Accuracy and Efficiency: A Comparison of Two Subpixel Registration Algorithms

The two major subpixel registration algorithms, currently being used in subset-based digital image correlation, are the classic Newton-Raphson (FA-NR) algorithm with forward additive mapping strategy and the recently introduced inverse compositional Gauss-Newton (IC-GN) algorithm. Although the equiv...

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Bibliographic Details
Published in:Experimental mechanics Vol. 56; no. 8; pp. 1395 - 1409
Main Authors: Pan, B., Wang, B.
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2016
Springer Nature B.V
Subjects:
Algorithms
Bias
Biomedical Engineering and Bioengineering
Characterization and Evaluation of Materials
Computing time
Control
Convolution
Digital imaging
Displacement
Dynamical Systems
Engineering
Error analysis
Image enhancement
Interpolation
Lasers
Newton-Raphson method
Noise
Noise levels
Optical Devices
Optics
Photonics
Pixels
Proving
Random errors
Registration
Set theory
Shape functions
Solid Mechanics
Vibration
Error analysis
Digital image correlation
Inverse compositional Gauss-Newton algorithm
Subpixel registration
ISSN:0014-4851, 1741-2765
Online Access:Get full text
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Summary:The two major subpixel registration algorithms, currently being used in subset-based digital image correlation, are the classic Newton-Raphson (FA-NR) algorithm with forward additive mapping strategy and the recently introduced inverse compositional Gauss-Newton (IC-GN) algorithm. Although the equivalence of these two algorithms has been proved in existing studies, practical implementations of the two subpixel registration algorithms do involve differences, and therefore lead to different performance. In the present work, detailed theoretical error analyses of the two algorithms are performed. Based on the simple sum of squared difference criterion and the practical first-order shape function, analytic formulae that can quantify both the bias error (systematic error) and the variability (random error) in the displacements measured by IC-GN and FA-NR algorithms with various interpolation methods (i.e., cubic convolution interpolation, cubic polynomial interpolation, cubic B-spline interpolation and quintic B-spline interpolation) are derived. It is shown that, compared with FA-NR algorithm, IC-GN algorithm leads to reduced bias error in displacement estimation by eliminating noise-induced bias error, and gives rise on the average to smaller random errors in displacement estimation in the cases of high noise levels or using small subsets. Numerical tests with precisely controlled subpixel displacements confirm the correctness of the theoretical derivations. The results reveal that IC-GN algorithm outperforms the classic FA-NR algorithm not only in terms of computational efficiency, but also in respect of subpixel registration accuracy and noise-proof performance, and is strongly recommended as a standard subpixel registration algorithm for practical DIC applications instead of FA-NR algorithm.
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ISSN:0014-4851
1741-2765
DOI:10.1007/s11340-016-0180-z