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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 24 — Dec. 2, 2013
  • pp: 29979–29999

Pixel-level robust digital image correlation

Corneliu Cofaru, Wilfried Philips, and Wim Van Paepegem  »View Author Affiliations

Optics Express, Vol. 21, Issue 24, pp. 29979-29999 (2013)

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Digital Image Correlation (DIC) is a well-established non-contact optical metrology method. It employs digital image analysis to extract the full-field displacements and strains that occur in objects subjected to external stresses. Despite recent DIC progress, many problematic areas which greatly affect accuracy and that can seldomly be avoided, received very little attention. Problems posed by the presence of sharp displacement discontinuities, reflections, object borders or edges can be linked to the analysed object’s properties and deformation. Other problematic areas, such as image noise, localized reflections or shadows are related more to the image acquisition process. This paper proposes a new subset-based pixel-level robust DIC method for in-plane displacement measurement which addresses all of these problems in a straightforward and unified approach, significantly improving DIC measurement accuracy compared to classic approaches. The proposed approach minimizes a robust energy functional which adaptively weighs pixel differences in the motion estimation process. The aim is to limit the negative influence of pixels that present erroneous or inconsistent motions by enforcing local motion consistency. The proposed method is compared to the classic Newton-Raphson DIC method in terms of displacement accuracy in three experiments. The first experiment is numerical and presents three combined problems: sharp displacement discontinuities, missing image information and image noise. The second experiment is a real experiment in which a plastic specimen is developing a lateral crack due to the application of uniaxial stress. The region around the crack presents both reflections that saturate the image intensity levels leading to missing image information, as well as sharp motion discontinuities due to the plastic film rupturing. The third experiment compares the proposed and classic DIC approaches with generic computer vision optical flow methods using images from the popular Middlebury optical flow evaluation dataset. Results in all experiments clearly show the proposed method’s improved measurement accuracy with respect to the classic approach considering the challenging conditions. Furthermore, in image areas where the classic approach completely fails to recover motion due to severe image de-correlation, the proposed method provides reliable results.

© 2013 OSA

OCIS Codes
(100.2000) Image processing : Digital image processing
(110.4280) Imaging systems : Noise in imaging systems
(120.7250) Instrumentation, measurement, and metrology : Velocimetry
(110.4153) Imaging systems : Motion estimation and optical flow
(100.4999) Image processing : Pattern recognition, target tracking

ToC Category:
Image Processing

Original Manuscript: October 1, 2012
Revised Manuscript: January 12, 2013
Manuscript Accepted: August 27, 2013
Published: November 27, 2013

Corneliu Cofaru, Wilfried Philips, and Wim Van Paepegem, "Pixel-level robust digital image correlation," Opt. Express 21, 29979-29999 (2013)

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  1. W. H. Peters and W. F. Ranson, “Digital imaging techniques in experimental stress-analysis,” Opt. Eng.21, 427–431 (1982). [CrossRef]
  2. M. A. Sutton, W. J. Wolters, W. H. Peters, W. F. Ranson, and S. R. McNeill, “Determination of displacements using an improved digital correlation method,” Image Vision Comput.1, 133–139 (1983). [CrossRef]
  3. W. H. Peters, W. F. Ranson, M. A. Sutton, T.C. Chu, and J. Anderson, “Application of digital correlation methods to rigid body mechanics,” Opt. Eng.22, 738–742 (1983). [CrossRef]
  4. B. Pan, A. Asundi, H-M. Xie, and J. X. Gao, “Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements,” Opt. Lasers Eng.47, 865–874 (2009). [CrossRef]
  5. J. Zhang, Y. Cai, W. Ye, and T.X. Yu, “On the use of the digital image correlation method for heterogeneous deformation measurement of porous solids,” Opt. Lasers Eng.49, 200–209 (2011). [CrossRef]
  6. L.B. Meng, G.C. Jin, and X.F. Yao, “Application of iteration and finite element smoothing technique for displacement and strain measurement of digital speckle correlation,” Opt. Lasers Eng.45, 56–73 (2007). [CrossRef]
  7. C. Tang, L. Wang, S. Yan, J. Wu, L. Cheng, and C. Li, “Displacement field analysis based on the combination digital speckle correlation method with radial basis function interpolation,” Appl. Opt.49, 4545–4553 (2010). [CrossRef] [PubMed]
  8. Y. Sun, J.H.L. Pang, C.K. Wong, and F. Su, “Finite element formulation for a digital image correlation method,” Appl. Opt.44, 7357–7363 (2005). [CrossRef] [PubMed]
  9. Y.N. Chen, W.Q. Jin, L. Zhao, and F.W. Li, “A subpixel motion estimation algorithm based on digital correlation for illumination variant and noise image sequences” Optik120, 835–844 (2009). [CrossRef]
  10. B. Pan, Z. Wang, and Z. Lu, “Genuine full-field deformation measurement of an object with complex shape using reliability-guided digital image correlation,” Opt. Express18, 1011–1023 (2010). [CrossRef] [PubMed]
  11. J. Poissant and F. Barthelat, “A novel subset splitting procedure for digital image correlation on discontinuous displacement fields,” Exp. Mech.50, 353–364 (2010). [CrossRef]
  12. C. Cofaru, W. Philips, and W. Van Paepegem, “Improved Newton-Raphson digital image correlation method for full-field displacement and strain calculation,” Appl. Opt.49, 6472–6484 (2010). [CrossRef] [PubMed]
  13. C. Cofaru, W. Philips, and W. Van Paepegem, “A three-frame digital image correlation (DIC) method for the measurement of small displacements and strains,” Meas. Sci. Technol.23, 105406 (14 pp.) (2012). [CrossRef]
  14. G. Besnard, F. Hild, and S. Roux, “Finite-Element’ displacement fields analysis from digital images: Application to Portevin-Le Châtelier bands,” Exp. Mech.46, 789–803 (2006). [CrossRef]
  15. J. Réthoré, S. Roux, and F. Hild, “From pictures to extended finite elements: extended digital image correlation (X-DIC),” C.R. Mécanique335, 131–137 (2007). [CrossRef]
  16. J. Réthoré, F. Hild, and S. Roux, “Extended digital image correlation with crack shape optimization,” Int. J. Numer. Meth. Eng.73, 248–272 (2008). [CrossRef]
  17. M. A. Sutton, J-J. Orteu, and H. W. Schreier, Image correlation for shape, motion and deformation measurements (Springer, 2009).
  18. B. Pan, H. Xie, and Z. Wang, “Equivalence of digital image correlation criteria for pattern matching,” Appl. Opt.49, 5501–5509 (2010). [CrossRef] [PubMed]
  19. B. Peng, Q. Zhang, W. Zhou, X. Hao, and L. Ding, “Modified correlation criterion for digital image correlation considering the effect of lighting variations in deformation measurements,” Opt. Eng.51, 017004 (2012). [CrossRef]
  20. C. Cofaru, W. Philips, and W. Van Paepegem, “A novel speckle pattern - adaptive Digital Image Correlation approach with robust strain calculation,” Opt. Lasers Eng.50, 187–198 (2012). [CrossRef]
  21. P.J. Huber and E.M. Ronchetti, Robust Statistics, 2nd Edition (John Wiley & Sons, New York (NY), 2009). [CrossRef]
  22. P.J. Rousseeuw and A.M. Leroy, Robust Regression and Outlier Detection (John Wiley & Sons, 1987) [CrossRef]
  23. F.R. Hampel, E.M. Ronchetti, P.J. Rousseeuw, and W.A. Stahel, Robust Statistics: The Approach Based on Influence Functions (John Wiley & Sons, 1986).
  24. H. A. Bruck, S. R. McNeill, M. A. Sutton, and W. H. Peters, “Digital image correlation using Newton-Raphson method of partial differential correction,” Exp. Mech.29, 261–267 (1989). [CrossRef]
  25. G. Vendroux and W. G. Knauss, “Submicron deformation field measurements: Part 2. Improved digital image correlation,” Exp. Mech.38, 86–92 (1998). [CrossRef]
  26. M. J. Black and P. Anandan, “The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields,” Comput. Vis. Image Underst.63, 75–104 (1996). [CrossRef]
  27. M. Ye, R. M. Haralick, and L. G. Shapiro, “Estimating piecewise-smooth optical flow with global matching and graduated optimization,” IEEE Trans. Pattern Anal. Mach. Intell.25, 1625–1630 (2003). [CrossRef]
  28. C-H. Teng, S-H. Lai, Y-S. Chen, and W-H. Hsu, “Accurate optical flow computation under non-uniform brightness variations,” Comput. Vis. Image Underst.97, 315–346 (2005). [CrossRef]
  29. Y-H. Kim, A. M. Martìnez, and A. C. Kak, “Robust motion estimation under varying illumination,” Image Vis. Comput.23, 365–375 (2005). [CrossRef]
  30. S. Baker, D. Scharstein, J.P Lewis, S. Roth, M. J. Black, and R. Szeliski, “A database and evaluation methodology for optical flow,” Int. J. Comput. Vis.92, 1–31 (2011). [CrossRef]
  31. X. Ren, “Local grouping for optical flow,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (Institute of Electrical and Electronics Engineers, New York, 2008) pp. 1–8.
  32. D. Sun, E. B. Sudderth, and M. J. Black, “Layered image motion with explicit occlusions, temporal consistency, and depth ordering,” Adv. Neural Inf. Process. Syst.23, 2226–2234 (2010).
  33. D. Sun, E. B. Sudderth, and M. J. Black, “Layered segmentation and optical flow estimation over time,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (Institute of Electrical and Electronics Engineers, New York, 2012) pp. 1768–1775.
  34. Y. Weiss, “Smoothness in layers: Motion segmentation using nonparametric mixture estimation,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (Institute of Electrical and Electronics Engineers, New York, 1997) pp. 520–526. [CrossRef]
  35. M. Werlberger, T. Pock, and H. Bischof, “Motion estimation with non-local total variation regularization,” Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (Institute of Electrical and Electronics Engineers, New York, 2010) pp. 2464–2471.
  36. M.J. Black, Robust incremental optical flow(PhD. Thesis, Yale University, 1992).
  37. C. Cofaru, W. Philips, and W. Van Paepegem, “Evaluation of digital image correlation techniques using realistic ground truth speckle images,” Meas. Sci. Technol.21, 055102 (17 pp.) (2010). [CrossRef]

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