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Erschienen in: Experimental Mechanics 2/2020

07.11.2019 | Research paper

Spatial DIC Errors due to Pattern-Induced Bias and Grey Level Discretization

verfasst von: S. S. Fayad, D. T. Seidl, P. L. Reu

Erschienen in: Experimental Mechanics | Ausgabe 2/2020

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Abstract

Digital image correlation (DIC) is an optical metrology method widely used in experimental mechanics for full-field shape, displacement and strain measurements. The required strain resolution for engineering applications of interest mandates DIC to have a high image displacement matching accuracy, on the order of 1/100th of a pixel, which necessitates an understanding of DIC errors. In this paper, we examine two spatial bias terms that have been almost completely overlooked. They cause a persistent offset in the matching of image intensities and thus corrupt DIC results. We name them pattern-induced bias (PIB), and intensity discretization bias (IDB). We show that the PIB error occurs in the presence of an undermatched shape function and is primarily dictated by the underlying intensity pattern for a fixed displacement field and DIC settings. The IDB error is due to the quantization of the gray level intensity values in the digital camera. In this paper we demonstrate these errors and quantify their magnitudes both experimentally and with synthetic images.

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Fußnoten
1
Heteroscedastic noise is defined here as noise with a variance profile linear with respect to the intensity. It is inherent to camera sensors and has been discussed in detail in the DIC literature.
 
2
The DIC Challenge sine wave period is off by a maximum of .07 pixels at the highest frequency due to an incorrect index within the deformation code. Any deviation from formulas discussed in the text is deemed insignificant by the authors. This code has now been fixed and is available for download.
 
3
E is over four times smaller than the PIB for this DIC Challenge set. This is shown in Appendix 1 as the remaining error after averaging displacement results from 50 images is 19% that of the original error composed primarily of PIB.
 
4
Center-weighted subsets suffer more from temporal-variance errors than uniformly-weighted subsets. Therefore, in applications where the temporal-variance error cannot be removed through image or displacement averaging, care must be taken when using weighted subsets.
 
5
It was necessary to utilize this MATLAB DIC code as the subset is defined by non-integer intensities. This code was validated against VIC 2D using an affine shape function on the 2D DIC Challenge 2.0-star image Appendix 3.
 
6
Averaging images a-priori to DIC calculation is computationally more efficient than averaging the DIC displacements from multiple correlations of images taken at equal states. Like the red line in Figure 1 (c-f), the latter method is used multiple times in the paper.
 
7
The VIC-Snap software saved these 12-bit images to 16-bit by default as 16-bit is a more standard TIFF format. These images are discussed in terms of the bit depth for the remainder of the paper. Later analyses performed using 8-bit images were derived from converting the 12-bit images from this single experiment.
 
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Metadaten
Titel
Spatial DIC Errors due to Pattern-Induced Bias and Grey Level Discretization
verfasst von
S. S. Fayad
D. T. Seidl
P. L. Reu
Publikationsdatum
07.11.2019
Verlag
Springer US
Erschienen in
Experimental Mechanics / Ausgabe 2/2020
Print ISSN: 0014-4851
Elektronische ISSN: 1741-2765
DOI
https://doi.org/10.1007/s11340-019-00553-9

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