OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 21 — Jul. 20, 2010
  • pp: 4044–4051

Study of the performance of different subpixel image correlation methods in 3D digital image correlation

Zhenxing Hu, Huimin Xie, Jian Lu, Tao Hua, and Jianguo Zhu  »View Author Affiliations

Applied Optics, Vol. 49, Issue 21, pp. 4044-4051 (2010)

View Full Text Article

Enhanced HTML    Acrobat PDF (1297 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



The three-dimensional digital image correlation (3D-DIC) method is rapidly developing and is being widely applied to engineering and manufacturing. Despite its extensive use, the error caused by different image matching algorithms is seldom discussed. An algorithm for 3D speckle image generation is proposed, and the performances of different subpixel correlation algorithms are studied. The advantage is that there is no interpolation bias of texture in the simulation before and after deformation, and the error from the interpolation of speckle can be omitted in this algorithm. An error criterion for 3D reconstruction is proposed. 3D speckle images were simulated, and the performance of four subpixel algorithms is addressed. Based on the research results of different subpixel algorithms, a first-order Newton–Raphson iteration method and gradient-based method are recommended for 3D-DIC measurement.

© 2010 Optical Society of America

OCIS Codes
(150.0155) Machine vision : Machine vision optics
(150.1135) Machine vision : Algorithms
(120.4880) Instrumentation, measurement, and metrology : Optomechanics

ToC Category:
Instrumentation, Measurement, and Metrology

Original Manuscript: January 27, 2010
Revised Manuscript: June 5, 2010
Manuscript Accepted: June 24, 2010
Published: July 15, 2010

Zhenxing Hu, Huimin Xie, Jian Lu, Tao Hua, and Jianguo Zhu, "Study of the performance of different subpixel image correlation methods in 3D digital image correlation," Appl. Opt. 49, 4044-4051 (2010)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. J.-J. Orteu, “3-D computer vision in experimental mechanics,” Opt. Lasers Eng. 47, 282–291 (2009). [CrossRef]
  2. L. Robert, F. Nazaret, T. Cutard, and J. J. Orteu, “Use of 3-D digital image correlation to characterize the mechanical behavior of a fiber reinforced refractory castable,” Exp. Mech. 47, 761–773 (2007). [CrossRef]
  3. M. A. Sutton, X. Ke, S. M. Lessner, M. Goldbach, M. Yost, F. Zhao, and H. W. Schreier, “Strain field measurements on mouse carotid arteries using microscopic three-dimensional digital image correlation,” J. Biomed. Mater. Res. Part A 84, 178–190 (2008). [CrossRef]
  4. P. Compston, M. Styles, and S. Kalyanasundaram, “Low energy impact damage modes in aluminum foam and polymer foam sandwich structures,” J. Sandwich Struct. Mater. 8, 365–379 (2006). [CrossRef]
  5. F. Barthelat, Z. Wu, B. C. Prorok, and H. D. Espinosa, “Dynamic torsion testing of nanocrystalline coatings using high-speed photography and digital image correlation,” Exp. Mech. 43, 331–340 (2003). [CrossRef]
  6. V. Tiwari, M. Sutton, and S. McNeill, “Assessment of high speed imaging systems for 2D and 3D deformation measurements: methodology development and validation,” Exp. Mech. 47, 561–579 (2007). [CrossRef]
  7. Y. B. Guo, Y. Yao, X. G. Di, and Ieee, “Research on structural parameter optimization of binocular vision measuring system for parallel mechanism,” in Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation (IEEE, 2006), 1131–1135. [CrossRef]
  8. K. Zhang, B. Xu, L. X. Tang, and H. M. Shi, “Modeling of binocular vision system for 3D reconstruction with improved genetic algorithms,” Int. J. Adv. Manuf. Technol. 29, 722–728(2006). [CrossRef]
  9. T. Becker, K. Splitthof, T. Siebert, and P. Kletting, “Error estimations of 3D digital image correlation measurements,” Proc. SPIE 6341, 63410F (2006). [CrossRef]
  10. T. Siebert, T. Becker, K. Spiltthof, I. Neumann, and R. Krupka, “High-speed digital image correlation: error estimations and applications,” Opt. Eng. 46, 051004–051007 (2007). [CrossRef]
  11. T. Siebert, T. Becker, K. Spiltthof, I. Neumann, and R. Krupka, “Error estimations in digital image correlation technique,” in Advances in Experimental Mechanics V (Trans Tech, 2007), pp. 265–270.
  12. B. Pan, H. M. Xie, B. Q. Xu, and F. L. Dai, “Performance of sub-pixel registration algorithms in digital image correlation,” Meas. Sci. Technol. 17, 1615–1621 (2006). [CrossRef]
  13. H. W. Schreier, “Investigation of two and three-dimensional image correlation techniques with applications in experimental mechanics,” Ph.D. thesis (University of South Carolina, 2003).
  14. H. A. Bruck, S. R. McNeill, M. A. Sutton, and W. H. Peters, “Digital image correlation using Newton–Raphson method of partial-differential correlation,” Exp. Mech. 29, 261–267(1989). [CrossRef]
  15. C. Q. Davis and D. M. Freeman, “Statistics of subpixel registration algorithms based on spatiotemporal gradients or block matching,” Opt. Eng. 37, 1290–1298 (1998). [CrossRef]
  16. 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]
  17. M. A. Sutton, S. R. McNeill, J. S. Jang, and M. Babai, “Effects of subpixel image-restoration on digital correlation error-estimates,” Opt. Eng. 27, 870–877 (1988).
  18. D. Zhang, X. Zhang, and G. Cheng, “Compression strain measurement by digital speckle correlation,” Exp. Mech. 39, 62–65 (1999). [CrossRef]
  19. G. C. Jin, X. F. Yao, and N. K. Bao, “Applications of speckle metrology to vibration and deformation measurements of electronic devices,” in The Seventh Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, 2000. ITHERM 2000 (IEEE, 2000), vol. 2, 253–255. [CrossRef]
  20. B. Wattrisse, A. Chrysochoos, J. M. Muracciole, and M. Nemoz-Gaillard, “Analysis of strain localization during tensile tests by digital image correlation,” Exp. Mech. 41, 29–39 (2001). [CrossRef]
  21. D. J. Chen, F. P. Chiang, Y. S. Tan, and H. S. Don, “Digital speckle-displacement measurement using a complex spectrum method,” Appl. Opt. 32, 1839–1849 (1993). [CrossRef] [PubMed]
  22. L. Oriat and E. Lantz, “Subpixel detection of the center of an object using a spectral phase algorithm on the image,” Pattern Recogn. 31, 761–771 (1998). [CrossRef]
  23. P. Zhou and K. E. Goodson, “Subpixel displacement and deformation gradient measurement using digital image/speckle correlation (DISC),” Opt. Eng. 40, 1613–1620 (2001). [CrossRef]
  24. J. Zhang, G. C. Jin, S. P. Ma, and L. B. Meng, “Application of an improved subpixel registration algorithm on digital speckle correlation measurement,” Opt. Laser Technol. 35, 533–542(2003). [CrossRef]
  25. M. C. Pitter, C. W. See, and M. G. Somekh, “Subpixel microscopic deformation analysis using correlation and artificial neural networks,” Opt. Express 8, 322–327 (2001). [CrossRef] [PubMed]
  26. M. C. Pitter, C. W. See, and M. G. Somekh, “Fast subpixel digital image correlation using artificial neural networks,” in Proceedings of 2001 International Conference on Image Processing (ICIP 2001), Vol 2, 901–904 (IEEE, 2001).
  27. H. Jin and H. A. Bruck, “Pointwise digital image correlation using genetic algorithms,” Exp. Tech. 29, 36–39 (2005). [CrossRef]
  28. H. Q. Jin and H. A. Bruck, “Theoretical development for pointwise digital image correlation,” Opt. Eng. 44, 067003(2005). [CrossRef]
  29. P. Luo, Y. Chao, M. Sutton, and W. Peters, “Accurate measurement of three-dimensional deformations in deformable and rigid bodies using computer vision,” Exp. Mech. 33, 123–132(1993). [CrossRef]
  30. J. D. Helm, S. R. McNeill, and M. A. Sutton, “Improved three-dimensional image correlation for surface displacement measurement,” Opt. Eng. 35, 1911–1920 (1996). [CrossRef]
  31. B. Pan, H. Xie, L. Yang, and Z. Wang, “Accurate measurement of satellite antenna surface using 3D digital image correlation technique,” Strain 45, 194–200 (2008). [CrossRef]
  32. M. Sutton, S. McNeill, J. Helm, and Y. Chao, “Advances in two-dimensional and three-dimensional computer vision,” in Photomechanics, P. K. Rasotgi, ed., Vol. 77 of Topics in Applied Physics (Springer-Verlag, 2000), pp. 323–372. [CrossRef]
  33. B. Pan, K. M. Qian, H. M. Xie, and A. Asundi, “Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review,” Meas. Sci. Technol. 20 (2009). [CrossRef]
  34. W. Tong, “An evaluation of digital image correlation criteria for strain mapping applications,” Strain 41, 167–175 (2005). [CrossRef]
  35. B. Pan, “Reliability-guided digital image correlation for image deformation measurement,” Appl. Opt. 48, 1535–1542 (2009). [CrossRef] [PubMed]

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