Journal of the Optical Society of Korea

  • 제16권4호
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  • Pages.372-380
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  • 2012
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  • 1226-4776(pISSN)
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  • 2093-6885(eISSN)
한국광학회 (Optical Society of Korea)
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Assessment of Gradient-based Digital Speckle Correlation Measurement Errors

  • Jian, Zhao (School of Technology, Beijing Forestry University) ;
  • Dong, Zhao (School of Technology, Beijing Forestry University) ;
  • Zhe, Zhang (School of Technology, Beijing Forestry University)
  • 투고 : 2012.06.07
  • 심사 : 2012.09.05
  • 발행 : 2012.12.25
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초록

The optical method Digital Speckle Correlation Measurement (DSCM) has been extensively applied due its capability to measure the entire displacement field over a body surface. A formula of displacement measurement errors by the gradient-based DSCM method was derived. The errors were found to explicitly relate to the image grayscale errors consisting of sub-pixel interpolation algorithm errors, image noise, and subset deformation mismatch at each point of the subset. A power-law dependence of the standard deviation of displacement measurement errors on the subset size was established when the subset deformation was rigid body translation and random image noise was dominant and it was confirmed by both the numerical and experimental results. In a gradient-based algorithm the basic assumption is rigid body translation of the interrogated subsets, however, this is in contradiction to the real circumstances where strains exist. Numerical and experimental results also indicated that, subset shape function mismatch was dominant when the order of the assumed subset shape function was lower than that of the actual subset deformation field and the power-law dependence clearly broke down. The power-law relationship further leads to a simple criterion for choosing a suitable subset size, image quality, sub-pixel algorithm, and subset shape function for DSCM.

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참고문헌

  1. M. A. Sutton, J. L. Turner, H. A. Bruck, and T. A. Chae, "Full-field representation of discretely sampled surface deformation for displacement and strain analysis," Express Mech. 31, 168-177 (1991). https://doi.org/10.1007/BF02327571
  2. B. W. Smith, M. Li, and W. Tong, "Error assessment for strain mapping by digital image correlation," Express Tech. 22, 19-21 (1998).
  3. P. Bing, H.-m. Xie, P.-w. Chen, F.-l. Huang, and Q.-m. Zhang, "Assessment and correction of lens distortion for digital image correlation," Acta Metrologica Sinica 30, 62-67 (2009).
  4. 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). https://doi.org/10.1364/OE.16.007037
  5. D. Lecompte, A. Smits, S. Bossuyt, H. Sol, J. Vantomme, D. Van Hemelrijck, and A. M. Habraken, "Quality assessment of speckle patterns for digital image correlation," Optics and Lasers in Engineering 44, 1132-1145 (2006). https://doi.org/10.1016/j.optlaseng.2005.10.004
  6. H. Wang, Y. Kang, and X. Heping, "Advance in digital speckle correlation method and its application," Advance in Mechanics 35, 195-203 (2005).
  7. B. Pan, H.-m. Xie, B.-q. Xu, and F.-l. Dai, "Performance of sub-pixel registration algorithms in digital image correlation," Measurement Science and Technology 17, 1615-1620 (2006). https://doi.org/10.1088/0957-0233/17/6/045
  8. H. W. Schreier, J. R. Braasch, and M. A. Sutton, "Systematic errors in digital image correlation caused by gray-value interpolation," Opt. Eng. 42, 2915-2921 (2000).
  9. H. W. Shreier and M. A. Sutton, "Systematic errors in digital image correlation due to under matched subset shape functions," Express Mech. 42, 303-310 (2002). https://doi.org/10.1007/BF02410987
  10. H. Lu and P. D. Cary, "Deformation measurements by digital image correlation: implementation of a second-order displacement gradient," Express Mech. 40, 394-400 (2000).
  11. 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," Optics and Lasers in Engineering 48, 469-477 (2010). https://doi.org/10.1016/j.optlaseng.2009.08.010
  12. Z. Y. Wang, H. Q. Li, J. W. Tong, and J. T. Ruan, "Statistical analysis of the effect of intensity pattern noise on the displacement measurement precision of digital image correlation using self-correlated images," Express Mech. 47, 701-707 (2007). https://doi.org/10.1007/s11340-006-9005-9
  13. B. Pan, K. Qian, H. Xie, and A. Asundi, "Two-dimensional digital image correlation for in plane displacement and strain measurement: a review," Measurement Science and Technology 20, 062001 (2009). https://doi.org/10.1088/0957-0233/20/6/062001
  14. M. A. Sutton, J. J. Orteu, and H. W. Schreier, Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications (Springer, 2009).
  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). https://doi.org/10.1117/1.601966
  16. B. Pan, B.-q. Xu, and H.-m. Xie, "In-plane displacement measurement by gradient-based digital image correlation." Optical Technique 31, 643-647 (2005).
  17. W. Tong, H. Yao, and Y. Xuan, "An improved error evaluation in one-dimensional deformation measurements by linear digital image correlation," Express Mech. 51, 1019-1031 (2011). https://doi.org/10.1007/s11340-010-9423-6

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