OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 31 — Nov. 1, 2012
  • pp: 7674–7683

Large deformation measurement using digital image correlation: a fully automated approach

Yihao Zhou, Bing Pan, and Yan Qiu Chen  »View Author Affiliations


Applied Optics, Vol. 51, Issue 31, pp. 7674-7683 (2012)
http://dx.doi.org/10.1364/AO.51.007674


View Full Text Article

Enhanced HTML    Acrobat PDF (1066 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

In digital image correlation, the iterative spatial domain cross-correlation algorithm is considered as a gold standard for matching the corresponding points in two images, but requires an accurate initial guess of the deformation parameters to converge correctly and rapidly. In this work, we present a fully automated method to accurately initialize all points of interest for the deformed images in the presence of large rotation and/or heterogeneous deformation. First, a robust computer vision technique is adopted to match feature points detected in reference and deformed images. The deformation parameters of the seed point are initialized from the affine transform, which is fitted to the matched feature points around it. Subsequently, the refined parameters are automatically transferred to adjacent points using a modified quality-guided initial guess propagation scheme. The proposed method not only ensures a rapid and correct convergence of the nonlinear optimization algorithm by providing a complete and accurate initial guess of deformation for each measurement point, but also effectively deals with deformed images with relatively large rotation and/or heterogeneous deformation. Tests on both simulated speckle images and real-world foam compression experiment verify the effectiveness and robustness of the proposed method.

© 2012 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(120.0120) Instrumentation, measurement, and metrology : Instrumentation, measurement, and metrology

ToC Category:
Image Processing

History
Original Manuscript: July 16, 2012
Revised Manuscript: September 12, 2012
Manuscript Accepted: September 20, 2012
Published: October 30, 2012

Citation
Yihao Zhou, Bing Pan, and Yan Qiu Chen, "Large deformation measurement using digital image correlation: a fully automated approach," Appl. Opt. 51, 7674-7683 (2012)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-51-31-7674


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. W. Peters and W. Ranson, “Digital imaging techniques in experimental stress analysis,” Opt. Eng. 21, 427–431 (1982).
  2. M. Sutton, W. Wolters, W. Peters, W. Ranson, and S. McNeill, “Determination of displacements using an improved digital correlation method,” Image Vis. Comput. 1, 133–139 (1983). [CrossRef]
  3. M. Sutton, C. MingQi, W. Peters, Y. Chao, and S. McNeill, “Application of an optimized digital correlation method to planar deformation analysis,” Image Vis. Comput. 4, 143–150 (1986). [CrossRef]
  4. H. Bruck, S. McNeill, M. Sutton, and W. Peters, “Digital image correlation using Newton–Raphson method of partial differential correction,” Exp. Mech. 29, 261–267 (1989). [CrossRef]
  5. G. Vendroux and W. Knauss, “Submicron deformation field measurements. Part 2. improved digital image correlation,” Exp. Mech. 38, 86–92 (1998). [CrossRef]
  6. H. Schreier, J. Braasch, and M. Sutton, “Systematic errors in digital image correlation caused by intensity interpolation,” Opt. Eng. 39, 2915–2921 (2000). [CrossRef]
  7. H. Lu and P. Cary, “Deformation measurements by digital image correlation: implementation of a second-order displacement gradient,” Exp. Mech. 40, 393–400 (2000). [CrossRef]
  8. H. Schreier and M. Sutton, “Systematic errors in digital image correlation due to undermatched subset shape functions,” Exp. Mech. 42, 303–310 (2002). [CrossRef]
  9. B. Pan, H. Xie, B. Xu, and F. Dai, “Performance of sub-pixel registration algorithms in digital image correlation,” Meas. Sci. Technol. 17, 1615–1621 (2006). [CrossRef]
  10. Y. Sun and J. Pang, “Study of optimal subset size in digital image correlation of speckle pattern images,” Opt. Lasers Eng. 45, 967–974 (2007). [CrossRef]
  11. B. Pan, H. Xie, Z. Wang, K. Qian, and Z. Wang, “Study on subset size selection in digital image correlation for speckle patterns,” Opt. Express 16, 7037–7048 (2008). [CrossRef]
  12. B. Pan, A. Asundi, H. Xie, and J. 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]
  13. Y. Wang, M. Sutton, H. Bruck, and H. Schreier, “Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements,” Strain 45, 160–178 (2009). [CrossRef]
  14. Z. Hu, H. Xie, J. Lu, T. Hua, and J. Zhu, “Study of the performance of different subpixel image correlation methods in 3D digital image correlation,” Appl. Opt. 49, 4044–4051 (2010). [CrossRef]
  15. B. Pan, Z. Lu, and H. Xie, “Mean intensity gradient: an effective global parameter for quality assessment of the speckle patterns used in digital image correlation,” Opt. Lasers Eng. 48, 469–477 (2010). [CrossRef]
  16. W. Peters, W. Ranson, M. Sutton, T. Chu, and J. Anderson, “Application of digital correlation methods to rigid body mechanics,” Opt. Eng. 22, 738–742 (1983).
  17. Z. He, M. Sutton, W. Ranson, and W. Peters, “Two-dimensional fluid-velocity measurements by use of digital-speckle correlation techniques,” Exp. Mech. 24, 117–121 (1984). [CrossRef]
  18. T. Chu, W. Ranson, and M. Sutton, “Applications of digital-image-correlation techniques to experimental mechanics,” Exp. Mech. 25, 232–244 (1985). [CrossRef]
  19. Z. Kahn-Jetter and T. Chu, “Three-dimensional displacement measurements using digital image correlation and photogrammic analysis,” Exp. Mech. 30, 10–16 (1990). [CrossRef]
  20. M. Sutton, J. Yan, X. Deng, C. Cheng, and P. Zavattieri, “Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading,” Opt. Eng. 46, 051003 (2007). [CrossRef]
  21. M. Sutton, J. Orteu, and H. Schreier, Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications (Springer Verlag, 2009).
  22. J. Orteu, “3-D computer vision in experimental mechanics,” Opt. Lasers Eng. 47, 282–291 (2009). [CrossRef]
  23. B. Pan, K. Qian, H. Xie, and A. Asundi, “Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review,” Meas. Sci. Technol. 20, 062001 (2009). [CrossRef]
  24. J. Gao and H. Shang, “Deformation-pattern-based digital image correlation method and its application to residual stress measurement,” Appl. Opt. 48, 1371–1381 (2009). [CrossRef]
  25. P. Zhou and K. Goodson, “Subpixel displacement and deformation gradient measurement using digital image/speckle correlation,” Opt. Eng. 40, 1613–1620 (2001). [CrossRef]
  26. D. Tsai and C. Lin, “Fast normalized cross correlation for defect detection,” Pattern Recogn. Lett. 24, 2625–2631 (2003). [CrossRef]
  27. D. Chen, F. Chiang, Y. Tan, and H. Don, “Digital speckle-displacement measurement using a complex spectrum method,” Appl. Opt. 32, 1839–1849 (1993). [CrossRef]
  28. F. Hild, B. Raka, M. Baudequin, S. Roux, and F. Cantelaube, “Multiscale displacement field measurements of compressed mineral-wool samples by digital image correlation,” Appl. Opt. 41, 6815–6828 (2002). [CrossRef]
  29. Z. Zhang, Y. Kang, H. Wang, Q. Qin, Y. Qiu, and X. Li, “A novel coarse-fine search scheme for digital image correlation method,” Measurement 39, 710–718 (2006). [CrossRef]
  30. B. Pan, “Reliability-guided digital image correlation for image deformation measurement,” Appl. Opt. 48, 1535–1542(2009). [CrossRef]
  31. 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. Express 18, 1011–1023 (2010). [CrossRef]
  32. B. Pan, H. Xie, and Z. Wang, “Equivalence of digital image correlation criteria for pattern matching,” Appl. Opt. 49, 5501–5509 (2010). [CrossRef]
  33. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of Alvey Vision Conference, Vol. 15 (British Machine Vision Association and Society for Pattern Recognition, 1988), p. 50.
  34. K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” Int. J. Comput. Vis. 60, 63–86 (2004). [CrossRef]
  35. D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004). [CrossRef]
  36. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Underst. 110, 346–359 (2008). [CrossRef]
  37. D. Lowe, “Demo software: Sift keypoint detector” (www.cs.ubc.ca/lowe/keypoints).
  38. M. Fischler and R. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24, 381–395 (1981). [CrossRef]
  39. B. Pan, W. Dafang, and X. Yong, “Incremental calculation for large deformation measurement using reliability-guided digital image correlation,” Opt. Lasers Eng. 50, 586–592 (2012). [CrossRef]
  40. Y. Zhou and Y. Chen, “Propagation function for accurate initialization and efficiency enhancement of digital image correlation,” Opt. Lasers Eng. 50, 1789–1797 (2012). [CrossRef]
  41. J. Nocedal and S. Wright, Numerical Optimization, 2nd ed. (Springer Verlag, 2006).
  42. B. Pan and K. Li, “A fast digital image correlation method for deformation measurement,” Opt. Lasers Eng. 49, 841–847 (2011). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited