Weitere Artikel dieser Ausgabe durch Wischen aufrufen
The original version of this article was revised: to include labels on figures 4 and 9 and corrections made to the equations 3,4 and 8.
A correction to this article is available online at https://doi.org/10.1007/s11340-019-00567-3.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Jones E, Reu P (2017) Distortion of Digital Image Correlation (DIC) Displacements and Strains from Heat Waves. Exp Mech:1–24
Sutton MA et al (2008) The effect of out-of-plane motion on 2D and 3D digital image correlation measurements. 46(10):746–757
Schreier HW, Braasch JR, Sutton MA (2000) Systematic errors in digital image correlation caused by intensity interpolation. Opt Eng 39(11):2915–2921 CrossRef
Wang YQ et al (2009) Quantitative Error Assessment in Pattern Matching: Effects of Intensity Pattern Noise, Interpolation, Strain and Image Contrast on Motion Measurements. Strain 45(2):160–178 CrossRef
Lehoucq R, Reu P, Turner D (2017) The effect of the ill-posed problem on quantitative error assessment in digital image correlation. Exp Mech:1–13
Schreier HW, Sutton MAJEM (2002) Systematic errors in digital image correlation due to undermatched subset shape functions. 42(3):303–310
Yu L, Bing P (2015) The errors in digital image correlation due to overmatched shape functions. Meas Sci Technol 26(4):045202 CrossRef
Grediac M, Blaysat B, Sur F (2017) A Critical Comparison of Some Metrological Parameters Characterizing Local Digital Image Correlation and Grid Method. Exp Mech 57(6):871–903 CrossRef
Reu, P.L., et al., DIC challenge: developing images and guidelines for evaluating accuracy and resolution of 2D analyses. 2017: p. 1–33 CrossRef
Reu PL et al (2017) DIC Challenge: Developing Images and Guidelines for Evaluating Accuracy and Resolution of 2D Analyses. Exp Mech
Grédiac M, Sur F (2013) Effect of Sensor Noise on the Resolution and Spatial Resolution of Displacement and Strain Maps Estimated with the Grid Method. Strain
Lucas, B.D. and T. Kanade, An iterative image registration technique with an application to stereo vision. 1981
Sur F, Blaysat B, Grédiac M (2017) Rendering Deformed Speckle Images with a Boolean Model. Journal of Mathematical Imaging and Vision
Yuan Y et al (2014) Accurate displacement measurement via a self-adaptive digital image correlation method based on a weighted ZNSSD criterion. 52:75–85
Wang D et al (2016) Bias reduction in sub-pixel image registration based on the anti-symmetric feature. 27(3):035206
MATLAB. LOWESS Smoothing. https://www.mathworks.com/help/curvefit/lowess-smoothing.html. Accessed 18 Feb 2019
Cleveland WSJAS (1981) LOWESS: A program for smoothing scatterplots by robust locally weighted regression. 35(1):54 CrossRef
Patterson EA (2007) et al, Calibration and evaluation of optical systems for full-field strain measurement. 45(5):550–564
Balcaen R (2017) et al, Stereo-DIC uncertainty quantification based on simulated images. 57(6):939–951
A Good Practices Guide for Digital Image Correlation (2018) M.A.I. E.M.C. Jones, Editor. International Digital Image Correlation Society
- Spatial DIC Errors due to Pattern-Induced Bias and Grey Level Discretization
S. S. Fayad
D. T. Seidl
P. L. Reu
- Springer US
in-adhesives, MKVS, Zühlke/© Zühlke, Nordson/© Nordson, ViscoTec/© ViscoTec, Hellmich GmbH/© Hellmich GmbH