Experimental Mechanics

, Volume 58, Issue 5, pp 815–830 | Cite as

A q-Factor-Based Digital Image Correlation Algorithm (qDIC) for Resolving Finite Deformations with Degenerate Speckle Patterns

  • A. K. Landauer
  • M. Patel
  • D. L. Henann
  • C. Franck


Digital image correlation (DIC) has become a widely utilized non-contact, full-field displacement measurement technique for obtaining accurate material kinematics. Despite the significant advances made to date, high resolution reconstruction of finite deformations for images with intrinsically low quality speckle patterns or poor signal-to-noise content has not been fully addressed. In particular, large image distortions imposed by materials undergoing finite deformations create significant challenges for most classical DIC approaches. To address this issue, this paper describes a new open source DIC algorithm (qDIC) that incorporates cross-correlation quality factors (q-factors), which are specifically designed to assess the quality of the reconstructed displacement estimate during the motion reconstruction process. A q-factor provides a robust assessment of the uniqueness and sharpness of the cross-correlation peak, and thus a quantitative estimate of the subset-based displacement measure per given image subset and level of applied deformation. We show that the incorporation of energy- and entropy-based q-factor metrics leads to substantially improved displacement predictions, lower noise floor, and reduced decorrelation even at significant levels of image distortion or poor speckle quality. Furthermore, we show that q-factors can be utilized as a quantitative metric for constructing a hybrid incremental-cumulative displacement correlation scheme for accurately resolving very large homogeneous and inhomogeneous deformations, even in the presence of significant image data loss.


Digital image correlation Finite deformation Correlation quality factor Iterative deformation method Elastomeric foam 



The authors thank Dr. Jonathan Estrada for assistance in formulation of the FIDIC algorithm, and Xiqui Li for technical discussions. The authors gratefully acknowledge support from the Army Research Office under grant W911NF-16-1-0084 and an NSF Graduate Research Fellowship to AL (DGE 1058262).


  1. 1.
    Chu TC, Ranson WF, Sutton MA (1985) Applications of digital-image-correlation techniques to experimental mechanics. Exp Mech 25(3):232–244. CrossRefGoogle Scholar
  2. 2.
    Schreier H, Orteu JJ, Sutton MA (2009) Image correlation for shape, motion and deformation measurements. Springer, USCrossRefGoogle Scholar
  3. 3.
    Sutton MA, Wolters WJ, Peters WH, Ranson WF, McNeill S (1983) Determination of displacements using an improved digital correlation method. Image Vis Comput 1(3):133–139. CrossRefGoogle Scholar
  4. 4.
    Luo PF, Chao YJ, Sutton MA, Peters WH (1993) Accurate measurement of three-dimensional deformations in deformable and rigid bodies using computer vision. Exp Mech 33(2):123–132. CrossRefGoogle Scholar
  5. 5.
    Sutton MA (2013) Computer vision-based, noncontacting deformation measurements in mechanics: a generational transformation. Appl Mech Rev 65(5):050,802–23. CrossRefGoogle Scholar
  6. 6.
    Bay BK (2008) Methods and applications of digital volume correlation. J Strain Anal Eng Des 43(8):745–760. CrossRefGoogle Scholar
  7. 7.
    Bay BK, Smith TS, Fyhrie DP, Saad M (1999) Digital volume correlation: three-dimensional strain mapping using x-ray tomography. Exp Mech 39(3):217–226. CrossRefGoogle Scholar
  8. 8.
    Franck C, Hong S, Maskarinec SA, Tirrell DA, Ravichandran G (2007) Three-dimensional full-field measurements of large deformations in soft materials using confocal microscopy and digital volume correlation. Exp Mech 47(3):427–438. CrossRefGoogle Scholar
  9. 9.
    Fu J, Pierron F, Ruiz PD (2013) Elastic stiffness characterization using three-dimensional full-field deformation obtained with optical coherence tomography and digital volume correlation. J Biomed Opt 18:18–18–16. CrossRefGoogle Scholar
  10. 10.
    Pierron F, McDonald SA, Hollis D, Withers P, Alderson A (2011) Assessment of the deformation of low density polymeric auxetic foams by x-ray tomography and digital volume correlation. In: Applied Mechanics and Materials, vol 70, pp 93–98. Trans Tech Publications.
  11. 11.
    Hild F, Roux S (2012) Comparison of local and global approaches to digital image correlation. Exp Mech 52(9):1503–1519. CrossRefGoogle Scholar
  12. 12.
    Blaber J, Adair B, Antoniou A (2015) Ncorr: Open-source 2d digital image correlation matlab software. Exp Mech 55(6):1105–1122. CrossRefGoogle Scholar
  13. 13.
    Pan B, Wu D, Xia Y (2012) Incremental calculation for large deformation measurement using reliability-guided digital image correlation. Opt Lasers Eng 50 (4):586–592. CrossRefGoogle Scholar
  14. 14.
    Reu P (2013) A study of the influence of calibration uncertainty on the global uncertainty for digital image correlation using a monte carlo approach. Exp Mech 53(9):1661–1680. CrossRefGoogle Scholar
  15. 15.
    Wang Y, Lava P, Reu P, Debruyne D (2016) Theoretical analysis on the measurement errors of local 2d dic: part i temporal and spatial uncertainty quantification of displacement measurements. Strain 52(2):110–128. CrossRefGoogle Scholar
  16. 16.
    Crammond G, Boyd S, Dulieu-Barton J (2013) Speckle pattern quality assessment for digital image correlation. Opt Lasers Eng 51(12):1368–1378. CrossRefGoogle Scholar
  17. 17.
    Dong Y, Pan B (2017) A review of speckle pattern fabrication and assessment for digital image correlation. Exp Mech 57(8):1161–1181. CrossRefGoogle Scholar
  18. 18.
    Estrada JB, Franck C (2015) Intuitive interface for the quantitative evaluation of speckle patterns for use in digital image and volume correlation techniques. J Appl Mech 82(9):095,001. CrossRefGoogle Scholar
  19. 19.
    Pan B, Lu Z, Xie H (2010) Mean intensity gradient: an effective global parameter for quality assessment of the speckle patterns used in digital image correlation. Opt Lasers Eng 48(4):469–477. CrossRefGoogle Scholar
  20. 20.
    Pan B, Xie H, Wang Z, Qian K, Wang Z (2008) Study on subset size selection in digital image correlation for speckle patterns. Opt Express 16(10):7037–7048. CrossRefGoogle Scholar
  21. 21.
    Hua T, Xie H, Wang S, Hu Z, Chen P, Zhang Q (2011) Evaluation of the quality of a speckle pattern in the digital image correlation method by mean subset fluctuation. Opt Laser Technol 43(1):9–13. CrossRefGoogle Scholar
  22. 22.
    Lecompte D, Smits A, Bossuyt S, Sol H, Vantomme J, Hemelrijck DV, Habraken A (2006) Quality assessment of speckle patterns for digital image correlation. Opt Lasers Eng 44(11):1132–1145. CrossRefGoogle Scholar
  23. 23.
    Yaofeng S, Pang JH (2007) Study of optimal subset size in digital image correlation of speckle pattern images. Opt Lasers Eng 45(9):967–974. CrossRefGoogle Scholar
  24. 24.
    Liu XY, Li RL, Zhao HW, Cheng TH, Cui GJ, Tan QC, Meng GW (2015) Quality assessment of speckle patterns for digital image correlation by shannon entropy. Optik–Int J Light Elect Opt 126(23):4206–4211. CrossRefGoogle Scholar
  25. 25.
    Bossuyt S (2013) Optimized patterns for digital image correlation, pp 239–248. Springer, New York. Google Scholar
  26. 26.
    Stoilov G, Kavardzhikov V, Pashkouleva D (2012) A comparative study of random patterns for digital image correlation. J Theor Appl Mech 42(2):55–66. CrossRefGoogle Scholar
  27. 27.
    Vijaya Kumar BVK, Hassebrook L (1990) Performance measures for correlation filters. Appl Opt 29 (20):2997–3006. CrossRefGoogle Scholar
  28. 28.
    Xue Z, Charonko JJ, Vlachos PP (2014) Particle image velocimetry correlation signal-to-noise ratio metrics and measurement uncertainty quantification. Meas Sci Technol 25(11):115,301. CrossRefGoogle Scholar
  29. 29.
    Javidi B (1989) Nonlinear joint power spectrum based optical correlation. Appl Opt 28(12):2358–2367. CrossRefGoogle Scholar
  30. 30.
    Horner JL, Leger JR (1985) Pattern recognition with binary phase-only filters. Appl Opt 24(5):609–611. CrossRefGoogle Scholar
  31. 31.
    Scarano F (2002) Iterative image deformation methods in piv. Meas Sci Technol 13(1):R1. CrossRefGoogle Scholar
  32. 32.
    Bar-Kochba E, Toyjanova J, Andrews E, Kim KS, Franck C (2015) A fast iterative digital volume correlation algorithm for large deformations. Exp Mech 55(1):261–274. CrossRefGoogle Scholar
  33. 33.
    Jambunathan K, Ju XY, Dobbins BN, Ashforth-Frost S (1995) An improved cross correlation technique for particle image velocimetry. Meas Sci Technol 6(5):507. CrossRefGoogle Scholar
  34. 34.
    Nogueira J, Lecuona A, Rodríguez PA, Alfaro JA, Acosta A (2005) Limits on the resolution of correlation piv iterative methods. practical implementation and design of weighting functions. Exp Fluids 39 (2):314–321. CrossRefGoogle Scholar
  35. 35.
    Lewis JP (1995) Fast normalized cross-correlation. In: Vision interface, vol 10, pp 120–123Google Scholar
  36. 36.
    Schrijer FFJ, Scarano F (2008) Effect of predictor–corrector filtering on the stability and spatial resolution of iterative piv interrogation. Exp Fluids 45(5):927–941. CrossRefGoogle Scholar
  37. 37.
    Westerweel J, Scarano F (2005) Universal outlier detection for piv data. Exp Fluids 39(6):1096–1100. CrossRefGoogle Scholar
  38. 38.
    Charonko JJ, Vlachos PP (2013) Estimation of uncertainty bounds for individual particle image velocimetry measurements from cross-correlation peak ratio. Measurement Science and Technology 24(6):065,301. CrossRefGoogle Scholar
  39. 39.
    Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423. MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Rossi M, Lava P, Pierron F, Debruyne D, Sasso M (2015) Effect of dic spatial resolution, noise and interpolation error on identification results with the vfm. Strain 51(3):206–222. CrossRefGoogle Scholar
  41. 41.
    Michell JH (1899) On the direct determination of stress in an elastic solid, with application to the theory of plates. Proc Lond Math Soc s1-31(1):100–124. MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    LePage WS, Daly S, Shaw JA (2016) Cross polarization for improved digital image correlation. Exp Mech 56(6):969—-985. CrossRefGoogle Scholar

Copyright information

© Society for Experimental Mechanics 2018

Authors and Affiliations

  • A. K. Landauer
    • 1
  • M. Patel
    • 1
  • D. L. Henann
    • 1
  • C. Franck
    • 1
  1. 1.School of Engineering Brown UniversityProvidenceUSA

Personalised recommendations