Skip to main content
SpringerLink
Log in

Displacement estimation using a modified seed prediction algorithm in digital image correlation

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

A new model based strategy for predicting the initial guess in digital image correlation (DIC) analysis is presented. This is accomplished by integrating the physics of deformation with measurements from images using the Bayesian framework. For the purpose of illustration synthetic and experimental data from problems of rigid body motion and finite strains are considered. The proposed algorithm is validated by comparing the predicted deformations with those obtained using the open-source software, Ncorr. The method is successful in accurately capturing the displacements and also achieved a 50% reduction in the computation time in contrast to the existing correlation based approaches. It is anticipated that the formulation presented in this paper will form the basis for developing efficient modelling based computational strategies for DIC applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

References

  1. Peters, W H and Ranson W F 1982 Digital Imaging Techniques In: Experimental Stress Analysis. OE. 21: 427–431

  2. Schreier H, Orteu J J and Sutton M A 2009 Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications. Springer

  3. Pan B, Qian K, Xie H and Asundi A 2009 Two-dimensional digital image correlation for in-plane displacement and strain measurement: A review. Meas. Sci. Technol. 20: 062001

    Article  Google Scholar 

  4. Pan B 2018 Digital image correlation for surface deformation measurement: Historical developments, recent advances and future goals. Meas. Sci. Technol. 29: 082001

    Article  Google Scholar 

  5. Tong W 2005 An evaluation of digital image correlation criteria for strain mapping applications. Strain. 41: 167–175

    Article  Google Scholar 

  6. Bing P, Hui-min X, Bo-qin X and Fu-long D 2006 Performance of sub-pixel registration algorithms in digital image correlation. Meas. Sci. Technol. 17: 1615–1621

    Article  Google Scholar 

  7. Lecompte D, Smits A, Bossuyt S, Sol H, Vantomme J, Van Hemelrijck D and Habraken A M 2006 Quality assessment of speckle patterns for digital image correlation. Optics and Lasers in Engineering 44: 1132–1145

    Article  Google Scholar 

  8. Dong Y L and Pan B 2017 A review of speckle pattern fabrication and assessment for digital image correlation. Exp. Mech. 57: 1161–1181

    Article  Google Scholar 

  9. Sutton M A, Yan J H, Tiwari V, Schreier H W and Orteu J J 2008 The effect of out-of-plane motion on 2D and 3D digital image correlation measurements. Optics and Lasers in Engineering 46: 746–757

    Article  Google Scholar 

  10. Sutton M, Wolters W, Peters W, Ranson W and McNeill S 1983 Determination of displacements using an improved digital correlation method. Image and Vision Computing 1: 133–139

    Article  Google Scholar 

  11. Zhou Y, Pan B and Chen Y Q 2012 Large deformation measurement using digital image correlation: A fully automated approach. Appl. Opt. 51: 7674

    Article  Google Scholar 

  12. Zhou Y and Chen Y Q 2013 Feature matching for automated and reliable initialization in three-dimensional digital image correlation. Optics and Lasers in Engineering 51: 213–223

    Article  Google Scholar 

  13. Pan B 2009 Reliability-guided digital image correlation for image deformation measurement. Appl. Opt. 48: 1535

    Article  Google Scholar 

  14. Zhang X, Chen J, Wang Z, Zhan N and Wang R 2012 Digital image correlation using ring template and quadrilateral element for large rotation measurement. Optics and Lasers in Engineering 50: 922–928

    Article  Google Scholar 

  15. Zhao J, Zeng P, Lei L and Ma Y 2012 Initial guess by improved population-based intelligent algorithms for large inter-frame deformation measurement using digital image correlation. Optics and Lasers in Engineering 50: 473–490

    Article  Google Scholar 

  16. Zhang Y, Yan L and Liou F 2018 Improved initial guess with semi-subpixel level accuracy in digital image correlation by feature-based method. Optics and Lasers in Engineering. 104: 149–158

    Article  Google Scholar 

  17. Li W, Li Y and Liang J 2019 Enhanced feature-based path-independent initial value estimation for robust point-wise digital image correlation. Optics and Lasers in Engineering. 121: 189–202

    Article  Google Scholar 

  18. Kieu H, Pan T, Wang Z, Le M, Nguyen H and Vo M 2014 Accurate 3D shape measurement of multiple separate objects with stereo vision. Meas. Sci. Technol. p. 8

  19. Wu R, Qian H and Zhang D 2016 Robust full-Eld measurement considering rotation using digital image correlation. Meas. Sci. Technol. p. 10

  20. Jiang Z, Kemao Q, Miao H, Yang J and Tang L 2015 Path-independent digital image correlation with high accuracy, speed and robustness. Optics and Lasers in Engineering. 65: 93–102

    Article  Google Scholar 

  21. Pan B, Wang Y and Tian L 2017 Automated initial guess in digital image correlation aided by Fourier-Mellin transform. Opt. Eng. 56: 014103

    Article  Google Scholar 

  22. Thiruselvam N I and Subramanian S J 2019 Feature‐assisted stereo correlation. Strain. 55

  23. Bruck H A, McNeill S R, Sutton M A and Peters W H 1989 Digital image correlation using Newton-Raphson method of partial differential correction. Experimental Mechanics. 29: 261–267

    Article  Google Scholar 

  24. Supreeth M, Radhika B and Pandurangan V 2021 Uncertainty quantification in full-field displacement and strain responses of materials using Kalman filter. Materials Today Communications. 26: 101875

    Article  Google Scholar 

  25. Hild F and Roux S 2006 Digital image correlation: From displacement measurement to identification of elastic properties – a review. Strain. 42: 69–80

    Article  Google Scholar 

  26. He T, Liu L and Makeev A 2018 Uncertainty analysis in composite material properties characterization using digital image correlation and finite element model updating. Composite Structures. 184: 337–351

    Article  Google Scholar 

  27. Pan B, Dafang W and Yong X 2012 Incremental calculation for large deformation measurement using reliability-guided digital image correlation. Optics and Lasers in Engineering. 50: 586–592

    Article  Google Scholar 

  28. Salvini P, Lux V and Marotta E 2015 Modal pursuit to detect large displacements and strain fields by digital image correlation. Strain. 51: 30–42

    Article  Google Scholar 

  29. Wang Z, Vo M, Kieu H and Pan T 2014 Automated fast initial guess in digital image correlation. Strain. 50: 28–36

    Article  Google Scholar 

  30. Simončič S, Klobčar D and Podržaj P 2015 Kalman filter based initial guess estimation for digital image correlation. Optics and Lasers in Engineering. 73: 80–88

    Article  Google Scholar 

  31. Schreier H W, Braasch J R and Sutton M A 2000 Systematic errors in digital image correlation caused by intensity interpolation. OE. 39: 2915–2921

    Article  Google Scholar 

  32. Papoulis A 2002 Probability, Random Variables, and Stochastic Processes. McGraw-Hill, Boston

    MATH  Google Scholar 

  33. Kalman R E 1960 A new approach to linear filtering and prediction problems. J. Basic Eng. 82: 35–45

    Article  MathSciNet  Google Scholar 

  34. Pan B and Wang B 2016 Digital image correlation with enhanced accuracy and efficiency: A comparison of two subpixel registration algorithms. Exp. Mech. 56: 1395–1409

    Article  Google Scholar 

  35. Blaber J, Adair B and Antoniou A 2015 Ncorr: Open-source 2D digital image correlation Matlab Software. Exp. Mech. 55: 1105–1122

    Article  Google Scholar 

  36. Siegmann P, Felipe-Sesé L and Díaz F A 2020 An alternative approach for improving DIC by using out-of-plane displacement information. Optics and Lasers in Engineering. 128: 105996

    Article  Google Scholar 

  37. Tekieli M, De Santis S, de Felice G, Kwiecień A and Roscini F 2017 Application of digital image correlation to composite reinforcements testing. Composite Structures. 160: 670–688

    Article  Google Scholar 

  38. Bavdekar V A, Deshpande A P and Patwardhan S C 2011 Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter. Journal of Process Control. 21: 585–601

    Article  Google Scholar 

  39. Daggumati S, Voet E, Van Paepegem W, Degrieck J, Xu J, Lomov S V and Verpoest I 2011 Local strain in a 5-harness satin weave composite under static tension: Part I - experimental analysis. Composites Science and Technology. 71: 1171–1179

    Article  Google Scholar 

  40. Reyne B, Manach P Y and Moës N 2019 Macroscopic consequences of Piobert-Lüders and Portevin–Le Chatelier bands during tensile deformation in Al-Mg alloys. Materials Science and Engineering: A. 746: 187–196

    Article  Google Scholar 

  41. Reyne B and Manach P Y 2018 AA5086 Tensile Tests with Portevin-Le Chatelier (PLC) Effect, Complete Raw Dataset: DIC Images, Raw Output and Elements of Post-Processing. Zenodo

Download references

Acknowledgements

The authors would like to thank Dr. S Daggumati, IIT Tirupati for sharing the composite test data.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Tirupati, Tirupati, India

    M SUPREETH & V PANDURANGAN

  2. Department of Civil and Environmental Engineering, Indian Institute of Technology Tirupati, Tirupati, India

    B RADHIKA

Authors
  1. M SUPREETH
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V PANDURANGAN
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B RADHIKA
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B RADHIKA.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

SUPREETH, M., PANDURANGAN, V. & RADHIKA, B. Displacement estimation using a modified seed prediction algorithm in digital image correlation. Sādhanā 48, 13 (2023). https://doi.org/10.1007/s12046-022-02065-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-022-02065-0

Keywords

Search

Navigation