Experimental Mechanics

, Volume 55, Issue 6, pp 1105–1122 | Cite as

Ncorr: Open-Source 2D Digital Image Correlation Matlab Software

  • J. Blaber
  • B. Adair
  • A. Antoniou


Digital Image Correlation (DIC) is an important and widely used non-contact technique for measuring material deformation. Considerable progress has been made in recent decades in both developing new experimental DIC techniques and in enhancing the performance of the relevant computational algorithms. Despite this progress, there is a distinct lack of a freely available, high-quality, flexible DIC software. This paper documents a new DIC software package Ncorr that is meant to fill that crucial gap. Ncorr is an open-source subset-based 2D DIC package that amalgamates modern DIC algorithms proposed in the literature with additional enhancements. Several applications of Ncorr that both validate it and showcase its capabilities are discussed.


Digital image correlation Large deformation Complex ROI Nickel superalloy Crack 



This work has been partially supported by the National Science Foundation (NSF) Graduate Research Fellowship under Grant No. DGE-1148903 and an NSF CAREER Grant No. CMMI-1351705. The fracture toughness test described in this paper was performed at the Mechanical Properties Research Lab at Georgia Tech.

Supplementary material

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  1. 1.
    Peters W, Ranson W (1982) Digital imaging techniques in experimental stress analysis. Opt Eng 21(3):213427CrossRefGoogle Scholar
  2. 2.
    Chu T, Ranson W, Sutton M (1985) Applications of digital-image-correlation techniques to experimental mechanics. Exp Mech 25(3):232–244CrossRefGoogle Scholar
  3. 3.
    Vendroux G, Knauss W (1998) Submicron deformation field measurements: Part 2. Improved digital image correlation. Exp Mech 38(2):86–92CrossRefGoogle Scholar
  4. 4.
    Bruck HA, McNeill SR, Sutton MA, Peters WH III (1989) Digital image correlation using Newton–Raphson method of partial differential correction. Exp Mech 29:261–267CrossRefGoogle Scholar
  5. 5.
    Cheng P, Sutton MA, Schreier HW, McNeill SR (2002) Full-field speckle pattern image correlation with B-spline deformation function. Exp Mech 42:344–352CrossRefGoogle Scholar
  6. 6.
    Kammers AD, Daly S (2011) Small-scale patterning methods for digital image correlation under scanning electron microscopy. Meas Sci Technol 22:125501CrossRefGoogle Scholar
  7. 7.
    Scrivens WA, Luo Y, Sutton MA, Collette SA, Myrick ML, Miney P, Colavita PE, Reynolds AP, Li X (2007) Development of patterns for digital image correlation measurements at reduced length scales. Exp Mech 47(1):63–77. doi: 10.1007/s11340-006-5869-y CrossRefGoogle Scholar
  8. 8.
    Sutton MA, Li N, Joy DC, Reynolds AP, Li X (2007) Scanning electron microscopy for quantitative small and large deformation measurements. Part I: SEM imaging at magnifications from 200 to 10,000. Exp Mech 47(6):775–787. doi: 10.1007/s11340-007-9042-z CrossRefGoogle Scholar
  9. 9.
    Wang JW, He Y, Fan F, Liu XH, Xia S, Liu Y, Harris CT, Li H, Huang JY, Mao SX (2013) Two-phase electrochemical lithiation in amorphous silicon. Nano Lett 13(2):709–715CrossRefGoogle Scholar
  10. 10.
    Van Puymbroeck N, Michel R, Binet R, Avouac J-P, Taboury J (2000) Measuring earthquakes from optical satellite images. Appl Opt 39(20):3486–3494CrossRefGoogle Scholar
  11. 11.
    Rubino V, Lapusta N, Rosakis A, Leprince S, Avouac J (2014) Static laboratory earthquake measurements with the digital image correlation method. Exp Mech 1–18Google Scholar
  12. 12.
    Dickinson AS, Taylor AC, Ozturk H, Browne M (2011) Experimental validation of a finite element model of the proximal femur using digital image correlation and a composite bone model. Engineering, Journal of BiomechanicalGoogle Scholar
  13. 13.
    Zhang D, Eggleton C, Arola D (2002) Evaluating the mechanical behavior of arterial tissue using digital image correlation. Exp Mech 42(4):409–416. doi: 10.1007/BF02412146 CrossRefGoogle Scholar
  14. 14.
    Franck C, Maskarinec SA, Tirrell DA, Ravichandran G (2011) Three-dimensional traction force microscopy: a new tool for quantifying cell-matrix interactions. PLoS One 6(3):e17833CrossRefGoogle Scholar
  15. 15.
    Wang H, Lai W, Antoniou A, Bastawros A (2014) Application of digital image correlation for multiscale biomechanics. In: Corey Neu GG (ed) CRC handbook of imaging in biological mechanics. CRC Press, Oxfords, pp 141–151Google Scholar
  16. 16.
    Carroll JD, Abuzaid W, Lambros J, Sehitoglu H (2013) High resolution digital image correlation measurements of strain accumulation in fatigue crack growth. Int J Fatigue 57:140–150CrossRefGoogle Scholar
  17. 17.
    Tong W (1997) Detection of plastic deformation patterns in a binary aluminum alloy. Exp Mech 37(4):452–459. doi: 10.1007/BF02317313 CrossRefGoogle Scholar
  18. 18.
    Rehrl C, Kleber S, Antretter T, Pippan R (2011) A methodology to study crystal plasticity inside a compression test sample based on image correlation and EBSD. Mater Charact 62(8):793–800. doi: 10.1016/j.matchar.2011.05.009 CrossRefGoogle Scholar
  19. 19.
    Daly S, Ravichandran G, Bhattacharya K (2007) Stress-induced martensitic phase transformation in thin sheets of Nitinol. Acta Mater 55(10):3593–3600CrossRefGoogle Scholar
  20. 20.
    Reedlunn B, Daly S, Hector L, Zavattieri P, Shaw J (2013) Tips and tricks for characterizing shape memory wire part 5: full-field strain measurement by digital image correlation. Exp Tech 37(3):62–78CrossRefGoogle Scholar
  21. 21.
    Bastawros A, Bart-Smith H, Evans A (2000) Experimental analysis of deformation mechanisms in a closed-cell aluminum alloy foam. J Mech Phys Solids 48(2):301–322zbMATHCrossRefGoogle Scholar
  22. 22.
    Bart-Smith H, Bastawros A-F, Mumm D, Evans A, Sypeck D, Wadley H (1998) Compressive deformation and yielding mechanisms in cellular Al alloys determined using X-ray tomography and surface strain mapping. Acta Mater 46(10):3583–3592CrossRefGoogle Scholar
  23. 23.
    Antoniou A, Onck P, Bastawros AF (2004) Experimental analysis of compressive notch strengthening in closed-cell aluminum alloy foam. Acta Mater 52(8):2377–2386CrossRefGoogle Scholar
  24. 24.
    Jerabek M, Major Z, Lang R (2010) Strain determination of polymeric materials using digital image correlation. Polym Test 29(3):407–416CrossRefGoogle Scholar
  25. 25.
    Wang Y, Cuitiño AM (2002) Full-field measurements of heterogeneous deformation patterns on polymeric foams using digital image correlation. Int J Solids Struct 39(13):3777–3796CrossRefGoogle Scholar
  26. 26.
    Poissant J, Barthelat F (2008) A novel “subset splitting” procedure for digital image correlation on discontinuous displacement fields. Exp Mech 50:353–364CrossRefGoogle Scholar
  27. 27.
    Pan B, Dafang W, Yong X (2012) Incremental calculation for large deformation measurement using reliability-guided digital image correlation. Opt Lasers Eng 50:586–592CrossRefGoogle Scholar
  28. 28.
    Pan B, Wang Z, Lu Z (2010) Genuine full-field deformation measurement of an object with complex shape using reliability-guided digital image correlation. Opt Express 18:1011–1023CrossRefGoogle Scholar
  29. 29.
    Lu H, Cary PD (2000) Deformation measurements by digital image correlation: implementation of a second-order displacement gradient. Exp Mech 40:393–400CrossRefGoogle Scholar
  30. 30.
    Helm JD, McNeill SR, Sutton MA (1996) Improved three-dimensional image correlation for surface displacement measurement. Soc Photo Opt Instrum Eng 35(7):1911–1920Google Scholar
  31. 31.
    Pan B (2009) Reliability-guided digital image correlation for image deformation measurement. Appl Opt 48:1535–1542CrossRefGoogle Scholar
  32. 32.
  33. 33.
  34. 34.
    Pan B, Li K, Tong W (2013) Fast, robust and accurate digital image correlation calculation without redundant computation. Exp Mech 53:1277–1289CrossRefGoogle Scholar
  35. 35.
    Pan B, Asundi A, Xie H, Gao J (2009) Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements. Opt Lasers Eng 47(7):865–874CrossRefGoogle Scholar
  36. 36.
    Schreier HW, Braasch JR, Sutton MA (2000) Systematic errors in digital image correlation caused by intensity interpolation. Soc Photo Opt Instrum Eng 39(11):2915–2921Google Scholar
  37. 37.
    Pan B, Li K (2011) A fast digital image correlation method for deformation measurement. Opt Lasers Eng 49:841–847CrossRefGoogle Scholar
  38. 38.
    Pan B, Xie H, Wang Z (2010) Equivalence of digital image correlation criteria for pattern matching. Appl Opt 49:5501–5509CrossRefGoogle Scholar
  39. 39.
    Baker S, Matthews I (2004) Lucas-kanade 20 years on: a unifying framework. Int J Comput Vis 56(3):221–255CrossRefGoogle Scholar
  40. 40.
    Baker S, Matthews I (2004) Lucas-kanade 20 years on: a unifying framework. Int J Comput Vis 56:221–255CrossRefGoogle Scholar
  41. 41.
    Pan B (2009) Reliability-guided digital image correlation for image deformation measurement. Appl Opt 408(8):8Google Scholar
  42. 42.
    Eberly D (2000) Least squares fitting of data. Magic Software, Chapel HillGoogle Scholar
  43. 43.
    Finley DR (2007) Efficient polygon fill algorithmGoogle Scholar
  44. 44.
    Nair D, Rajagopal R, Wenzel L (2000) Pattern matching based on a generalized Fourier transform. In: International symposium on optical science and technology. International Society for Optics and Photonics, Bellingham, pp 472–480Google Scholar
  45. 45.
    Milligan W, Orth E, Schirra J, Savage M (2004) Effects of microstructure on the high temperature constitutive behavior of IN100, Superalloys, pp 331–339Google Scholar
  46. 46.
    Jha S, Caton M, Larsen J (2007) A new paradigm of fatigue variability behavior and implications for life prediction. Mater Sci Eng A 468:23–32CrossRefGoogle Scholar
  47. 47.
    Barker VM, Johnson SW, Adair BS, Antolovich SD, Staroselsky A (2013) Load and temperature interaction modeling of fatigue crack growth in a Ni-base superalloy. Int J Fatigue 52:95–105CrossRefGoogle Scholar
  48. 48.
    Abràmoff MD, Magalhães PJ, Ram SJ (2004) Image processing with ImageJ. Biophoton Int 11(7):36–43Google Scholar
  49. 49.
    Tevenaz P (2000) Interpolation revisited. IEEE Trans Med Imaging 19(7):739–758Google Scholar
  50. 50.
    Keys RG (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160zbMATHMathSciNetCrossRefGoogle Scholar

Copyright information

© Society for Experimental Mechanics 2015

Authors and Affiliations

  1. 1.The Woodruff School of Mechanical EngineeringAtlantaUSA

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