Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Self-adaptive and Bidirectional Dynamic Subset Selection Algorithm for Digital Image Correlation

  • Zhang, Wenzhuo (College of Materials Science and Engineering, Sichuan University) ;
  • Zhou, Rong (College of Materials Science and Engineering, Sichuan University) ;
  • Zou, Yuanwen (College of Materials Science and Engineering, Sichuan University)
  • Received : 2017.01.24
  • Accepted : 2017.03.04
  • Published : 2017.04.30
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Abstract

The selection of subset size is of great importance to the accuracy of digital image correlation (DIC). In the traditional DIC, a constant subset size is used for computing the entire image, which overlooks the differences among local speckle patterns of the image. Besides, it is very laborious to find the optimal global subset size of a speckle image. In this paper, a self-adaptive and bidirectional dynamic subset selection (SBDSS) algorithm is proposed to make the subset sizes vary according to their local speckle patterns, which ensures that every subset size is suitable and optimal. The sum of subset intensity variation (${\eta}$) is defined as the assessment criterion to quantify the subset information. Both the threshold and initial guess of subset size in the SBDSS algorithm are self-adaptive to different images. To analyze the performance of the proposed algorithm, both numerical and laboratory experiments were performed. In the numerical experiments, images with different speckle distribution, different deformation and noise were calculated by both the traditional DIC and the proposed algorithm. The results demonstrate that the proposed algorithm achieves higher accuracy than the traditional DIC. Laboratory experiments performed on a substrate also demonstrate that the proposed algorithm is effective in selecting appropriate subset size for each point.

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References

  1. W. H. Peters and W. F. Ranson, "Digital imaging techniques in experimental stress analysis," Optical Engineering, vol. 21, no. 3, pp. 427-431, 1982.
  2. T. C. Chu, W. F. Ranson, and M. A. Sutton, "Applications of digital-image-correlation techniques to experimental mechanics," Experimental Mechanics, vol. 25, no.3, pp. 232-244, 1985. https://doi.org/10.1007/BF02325092
  3. 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 & Technology, vol. 20, no. 6, article no. 062001, 2009.
  4. B. Pan, "Recent progress in digital image correlation," Experimental Mechanics, vol. 51, no. 7, pp. 1223-1235, 2011. https://doi.org/10.1007/s11340-010-9418-3
  5. M. A. Sutton and F. Hild, "Recent advances and perspectives in digital image correlation," Experimental Mechanics, vol. 55, no. 1, pp. 1-8, 2015. https://doi.org/10.1007/s11340-015-9991-6
  6. S. Yoneyama, "Basic principle of digital image correlation for in-plane displacement and strain measurement," Advanced Composite Materials, vol. 25, no. 2, pp. 105-123, 2016.
  7. X. Xu, Y. Su, Y. Cai, T. Cheng, and Q. Zhang, "Effects of various shape functions and subset size in local deformation measurements using DIC," Experimental Mechanics, vol. 55, no. 8, pp. 1575-1590, 2015. https://doi.org/10.1007/s11340-015-0054-9
  8. D. Lecompte, A. Smits, S. Bossuyt, H. Sol, J. Vantomme, D. V. Hemelrijck, and A. M. Habraken, "Quality assessment of speckle patterns for digital image correlation," Optics & Lasers in Engineering, vol. 44, no. 11, pp. 1132-1145, 2006. https://doi.org/10.1016/j.optlaseng.2005.10.004
  9. T. L. Alexander, J. E. Harvey, and A. R. Weeks, "Average speckle size as a function of intensity threshold level: comparison of experimental measurements with theory," Applied Optics, vol. 33, no. 35, pp. 8240-8250, 1994. https://doi.org/10.1364/AO.33.008240
  10. S. Yaofeng and J. H. Pang, "Study of optimal subset size in digital image correlation of speckle pattern images," Optics & Lasers in Engineering, vol. 45, no. 9, pp. 967-974, 2007. https://doi.org/10.1016/j.optlaseng.2007.01.012
  11. B. Pan, H. Xie, Z. Wang, K. Qian, and Z. Wang, "Study on subset size selection in digital image correlation for speckle patterns," Optics Express, vol. 16, no. 10, pp. 7037-7048, 2008. https://doi.org/10.1364/OE.16.007037
  12. 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 & Lasers in Engineering, vol. 48, no. 4, pp. 469-477, 2010. https://doi.org/10.1016/j.optlaseng.2009.08.010
  13. X. Liu, R. Li, H. Zhao, T. Cheng, G. Cui, Q. Tan, and G. Meng, "Quality assessment of speckle patterns for digital image correlation by Shannon entropy," Optik - International Journal for Light and Electron Optics, vol. 126, no. 23, pp. 4206-4211, 2015. https://doi.org/10.1016/j.ijleo.2015.08.034
  14. J. Park, S. Yoon, T. H. Kwon, and K. Park, "Assessment of speckle-pattern quality in digital image correlation based on gray intensity and speckle morphology," Optics & Lasers in Engineering, vol. 91, pp. 62-72, 2017. https://doi.org/10.1016/j.optlaseng.2016.11.001
  15. G. M. Hassan, C. MacNish, A. Dyskin, and I. Shufrin, "Digital image correlation with dynamic subset selection," Optics & Lasers in Engineering, vol. 84, pp. 1-9, 2016.
  16. R. Keys, "Cubic convolution interpolation for digital image processing," IEEE Transactions on Acoustics Speech & Signal Processing, vol. 29, no. 6, pp. 1153-1160, 1981. https://doi.org/10.1109/TASSP.1981.1163711
  17. B. Pan, H. Xie, and Z. Wang, "Equivalence of digital image correlation criteria for pattern matching," Applied Optics, vol. 49, no. 28, pp. 5501-5509, 2010. https://doi.org/10.1364/AO.49.005501
  18. H. A. Bruck, S. R. Mcneill, M. A. Sutton, and W. H. Peters, "Digital image correlation using Newton-Raphson method of partial differential correction," Experimental Mechanics, vol. 29, no. 3, pp. 261-267, 1989. https://doi.org/10.1007/BF02321405
  19. G. Vendroux and W. G. Knauss, "Submicron deformation field measurements. Part 2: Improved digital image correlation," Experimental Mechanics, vol. 38, no. 2, pp. 86-92, 1998. https://doi.org/10.1007/BF02321649
  20. G. M. Hassan, A. V. Dyskin, C. K. MacNish, and N. V. Dinh, "A comparative study of techniques of distant reconstruction of displacement and strain fields using the DISTRESS simulator," Optik - International Journal for Light and Electron Optics, vol. 127, no. 23, pp. 11288-11305, 2016. https://doi.org/10.1016/j.ijleo.2016.09.026
  21. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Transactions on Systems Man & Cybernetics, vol. 9, no. 1, pp. 62-66, 1979. https://doi.org/10.1109/TSMC.1979.4310076
  22. Z. Peng and K. E. Goodson, "Subpixel displacement and deformation gradient measurement using digital image/speckle correlation (DISC)," Optical Engineering, vol. 40, no. 8, pp. 1613-1620, 2001. https://doi.org/10.1117/1.1387992