Abstract
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.
Similar content being viewed by others
Notes
See GitHub, https://github.com/FranckLab
References
Chu TC, Ranson WF, Sutton MA (1985) Applications of digital-image-correlation techniques to experimental mechanics. Exp Mech 25(3):232–244. https://doi.org/10.1007/BF02325092
Schreier H, Orteu JJ, Sutton MA (2009) Image correlation for shape, motion and deformation measurements. Springer, US
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. https://doi.org/10.1016/0262-8856(83)90064-1
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. https://doi.org/10.1007/BF02322488
Sutton MA (2013) Computer vision-based, noncontacting deformation measurements in mechanics: a generational transformation. Appl Mech Rev 65(5):050,802–23. https://doi.org/10.1115/1.4024984
Bay BK (2008) Methods and applications of digital volume correlation. J Strain Anal Eng Des 43(8):745–760. https://doi.org/10.1243/03093247JSA436
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. https://doi.org/10.1007/BF02323555
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. https://doi.org/10.1007/s11340-007-9037-9
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. https://doi.org/10.1117/1.JBO.18.12.121512
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. https://doi.org/10.4028/www.scientific.net/AMM.70.93
Hild F, Roux S (2012) Comparison of local and global approaches to digital image correlation. Exp Mech 52(9):1503–1519. https://doi.org/10.1007/s11340-012-9603-7
Blaber J, Adair B, Antoniou A (2015) Ncorr: Open-source 2d digital image correlation matlab software. Exp Mech 55(6):1105–1122. https://doi.org/10.1007/s11340-015-0009-1
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. https://doi.org/10.1016/j.optlaseng.2011.05.005
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. https://doi.org/10.1007/s11340-013-9746-1
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. https://doi.org/10.1111/str.12173
Crammond G, Boyd S, Dulieu-Barton J (2013) Speckle pattern quality assessment for digital image correlation. Opt Lasers Eng 51(12):1368–1378. https://doi.org/10.1016/j.optlaseng.2013.03.014
Dong Y, Pan B (2017) A review of speckle pattern fabrication and assessment for digital image correlation. Exp Mech 57(8):1161–1181. https://doi.org/10.1007/s11340-017-0283-1
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. https://doi.org/10.1115/1.4030821
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. https://doi.org/10.1016/j.optlaseng.2009.08.010
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. https://doi.org/10.1364/OE.16.007037
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. https://doi.org/10.1016/j.optlastec.2010.04.010
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. https://doi.org/10.1016/j.optlaseng.2005.10.004
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. https://doi.org/10.1016/j.optlaseng.2007.01.012
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. https://doi.org/10.1016/j.ijleo.2015.08.034
Bossuyt S (2013) Optimized patterns for digital image correlation, pp 239–248. Springer, New York. https://doi.org/10.1007/978-1-4614-4235-6-34
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. https://doi.org/10.2478/v10254-012-0008-x
Vijaya Kumar BVK, Hassebrook L (1990) Performance measures for correlation filters. Appl Opt 29 (20):2997–3006. https://doi.org/10.1364/AO.29.002997
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. https://doi.org/10.1088/0957-0233/25/11/115301
Javidi B (1989) Nonlinear joint power spectrum based optical correlation. Appl Opt 28(12):2358–2367. https://doi.org/10.1364/AO.28.002358
Horner JL, Leger JR (1985) Pattern recognition with binary phase-only filters. Appl Opt 24(5):609–611. https://doi.org/10.1364/AO.24.000609
Scarano F (2002) Iterative image deformation methods in piv. Meas Sci Technol 13(1):R1. https://doi.org/10.1088/0957-0233/13/1/201
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. https://doi.org/10.1007/s11340-014-9874-2
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. https://doi.org/10.1088/0957-0233/6/5/012
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. https://doi.org/10.1007/s00348-005-1017-1
Lewis JP (1995) Fast normalized cross-correlation. In: Vision interface, vol 10, pp 120–123
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. https://doi.org/10.1007/s00348-008-0511-7
Westerweel J, Scarano F (2005) Universal outlier detection for piv data. Exp Fluids 39(6):1096–1100. https://doi.org/10.1007/s00348-005-0016-6
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. https://doi.org/10.1088/0957-0233/24/6/065301
Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423. https://doi.org/10.1145/584091.584093
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. https://doi.org/10.1111/str.12134
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. https://doi.org/10.1112/plms/s1-31.1.100
LePage WS, Daly S, Shaw JA (2016) Cross polarization for improved digital image correlation. Exp Mech 56(6):969—-985. https://doi.org/10.1007/s11340-016-0129-2
Acknowledgements
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Landauer, A.K., Patel, M., Henann, D.L. et al. A q-Factor-Based Digital Image Correlation Algorithm (qDIC) for Resolving Finite Deformations with Degenerate Speckle Patterns. Exp Mech 58, 815–830 (2018). https://doi.org/10.1007/s11340-018-0377-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11340-018-0377-4