Abstract

In digital image correlation, the iterative spatial domain cross-correlation algorithm is considered as a gold standard for matching the corresponding points in two images, but requires an accurate initial guess of the deformation parameters to converge correctly and rapidly. In this work, we present a fully automated method to accurately initialize all points of interest for the deformed images in the presence of large rotation and/or heterogeneous deformation. First, a robust computer vision technique is adopted to match feature points detected in reference and deformed images. The deformation parameters of the seed point are initialized from the affine transform, which is fitted to the matched feature points around it. Subsequently, the refined parameters are automatically transferred to adjacent points using a modified quality-guided initial guess propagation scheme. The proposed method not only ensures a rapid and correct convergence of the nonlinear optimization algorithm by providing a complete and accurate initial guess of deformation for each measurement point, but also effectively deals with deformed images with relatively large rotation and/or heterogeneous deformation. Tests on both simulated speckle images and real-world foam compression experiment verify the effectiveness and robustness of the proposed method.

© 2012 Optical Society of America

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  1. W. Peters and W. Ranson, “Digital imaging techniques in experimental stress analysis,” Opt. Eng. 21, 427–431 (1982).
  2. M. Sutton, W. Wolters, W. Peters, W. Ranson, and S. McNeill, “Determination of displacements using an improved digital correlation method,” Image Vis. Comput. 1, 133–139 (1983).
    [CrossRef]
  3. M. Sutton, C. MingQi, W. Peters, Y. Chao, and S. McNeill, “Application of an optimized digital correlation method to planar deformation analysis,” Image Vis. Comput. 4, 143–150 (1986).
    [CrossRef]
  4. H. Bruck, S. McNeill, M. Sutton, and W. Peters, “Digital image correlation using Newton–Raphson method of partial differential correction,” Exp. Mech. 29, 261–267 (1989).
    [CrossRef]
  5. G. Vendroux and W. Knauss, “Submicron deformation field measurements. Part 2. improved digital image correlation,” Exp. Mech. 38, 86–92 (1998).
    [CrossRef]
  6. H. Schreier, J. Braasch, and M. Sutton, “Systematic errors in digital image correlation caused by intensity interpolation,” Opt. Eng. 39, 2915–2921 (2000).
    [CrossRef]
  7. H. Lu and P. Cary, “Deformation measurements by digital image correlation: implementation of a second-order displacement gradient,” Exp. Mech. 40, 393–400 (2000).
    [CrossRef]
  8. H. Schreier and M. Sutton, “Systematic errors in digital image correlation due to undermatched subset shape functions,” Exp. Mech. 42, 303–310 (2002).
    [CrossRef]
  9. B. Pan, H. Xie, B. Xu, and F. Dai, “Performance of sub-pixel registration algorithms in digital image correlation,” Meas. Sci. Technol. 17, 1615–1621 (2006).
    [CrossRef]
  10. Y. Sun and J. Pang, “Study of optimal subset size in digital image correlation of speckle pattern images,” Opt. Lasers Eng. 45, 967–974 (2007).
    [CrossRef]
  11. B. Pan, H. Xie, Z. Wang, K. Qian, and Z. Wang, “Study on subset size selection in digital image correlation for speckle patterns,” Opt. Express 16, 7037–7048 (2008).
    [CrossRef]
  12. B. Pan, A. Asundi, H. Xie, and J. Gao, “Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements,” Opt. Lasers Eng. 47, 865–874 (2009).
    [CrossRef]
  13. Y. Wang, M. Sutton, H. Bruck, and H. Schreier, “Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements,” Strain 45, 160–178 (2009).
    [CrossRef]
  14. Z. Hu, H. Xie, J. Lu, T. Hua, and J. Zhu, “Study of the performance of different subpixel image correlation methods in 3D digital image correlation,” Appl. Opt. 49, 4044–4051 (2010).
    [CrossRef]
  15. 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,” Opt. Lasers Eng. 48, 469–477 (2010).
    [CrossRef]
  16. W. Peters, W. Ranson, M. Sutton, T. Chu, and J. Anderson, “Application of digital correlation methods to rigid body mechanics,” Opt. Eng. 22, 738–742 (1983).
  17. Z. He, M. Sutton, W. Ranson, and W. Peters, “Two-dimensional fluid-velocity measurements by use of digital-speckle correlation techniques,” Exp. Mech. 24, 117–121 (1984).
    [CrossRef]
  18. T. Chu, W. Ranson, and M. Sutton, “Applications of digital-image-correlation techniques to experimental mechanics,” Exp. Mech. 25, 232–244 (1985).
    [CrossRef]
  19. Z. Kahn-Jetter and T. Chu, “Three-dimensional displacement measurements using digital image correlation and photogrammic analysis,” Exp. Mech. 30, 10–16 (1990).
    [CrossRef]
  20. M. Sutton, J. Yan, X. Deng, C. Cheng, and P. Zavattieri, “Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading,” Opt. Eng. 46, 051003 (2007).
    [CrossRef]
  21. M. Sutton, J. Orteu, and H. Schreier, Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications (Springer Verlag, 2009).
  22. J. Orteu, “3-D computer vision in experimental mechanics,” Opt. Lasers Eng. 47, 282–291 (2009).
    [CrossRef]
  23. B. Pan, K. Qian, H. Xie, and A. Asundi, “Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review,” Meas. Sci. Technol. 20, 062001 (2009).
    [CrossRef]
  24. J. Gao and H. Shang, “Deformation-pattern-based digital image correlation method and its application to residual stress measurement,” Appl. Opt. 48, 1371–1381 (2009).
    [CrossRef]
  25. P. Zhou and K. Goodson, “Subpixel displacement and deformation gradient measurement using digital image/speckle correlation,” Opt. Eng. 40, 1613–1620 (2001).
    [CrossRef]
  26. D. Tsai and C. Lin, “Fast normalized cross correlation for defect detection,” Pattern Recogn. Lett. 24, 2625–2631 (2003).
    [CrossRef]
  27. D. Chen, F. Chiang, Y. Tan, and H. Don, “Digital speckle-displacement measurement using a complex spectrum method,” Appl. Opt. 32, 1839–1849 (1993).
    [CrossRef]
  28. F. Hild, B. Raka, M. Baudequin, S. Roux, and F. Cantelaube, “Multiscale displacement field measurements of compressed mineral-wool samples by digital image correlation,” Appl. Opt. 41, 6815–6828 (2002).
    [CrossRef]
  29. Z. Zhang, Y. Kang, H. Wang, Q. Qin, Y. Qiu, and X. Li, “A novel coarse-fine search scheme for digital image correlation method,” Measurement 39, 710–718 (2006).
    [CrossRef]
  30. B. Pan, “Reliability-guided digital image correlation for image deformation measurement,” Appl. Opt. 48, 1535–1542(2009).
    [CrossRef]
  31. B. Pan, Z. Wang, and Z. Lu, “Genuine full-field deformation measurement of an object with complex shape using reliability-guided digital image correlation,” Opt. Express 18, 1011–1023 (2010).
    [CrossRef]
  32. B. Pan, H. Xie, and Z. Wang, “Equivalence of digital image correlation criteria for pattern matching,” Appl. Opt. 49, 5501–5509 (2010).
    [CrossRef]
  33. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of Alvey Vision Conference, Vol. 15 (British Machine Vision Association and Society for Pattern Recognition, 1988), p. 50.
  34. K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” Int. J. Comput. Vis. 60, 63–86 (2004).
    [CrossRef]
  35. D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
    [CrossRef]
  36. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Underst. 110, 346–359 (2008).
    [CrossRef]
  37. D. Lowe, “Demo software: Sift keypoint detector” (www.cs.ubc.ca/lowe/keypoints).
  38. M. Fischler and R. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24, 381–395 (1981).
    [CrossRef]
  39. B. Pan, W. Dafang, and X. Yong, “Incremental calculation for large deformation measurement using reliability-guided digital image correlation,” Opt. Lasers Eng. 50, 586–592 (2012).
    [CrossRef]
  40. Y. Zhou and Y. Chen, “Propagation function for accurate initialization and efficiency enhancement of digital image correlation,” Opt. Lasers Eng. 50, 1789–1797 (2012).
    [CrossRef]
  41. J. Nocedal and S. Wright, Numerical Optimization, 2nd ed. (Springer Verlag, 2006).
  42. B. Pan and K. Li, “A fast digital image correlation method for deformation measurement,” Opt. Lasers Eng. 49, 841–847 (2011).
    [CrossRef]

2012

B. Pan, W. Dafang, and X. Yong, “Incremental calculation for large deformation measurement using reliability-guided digital image correlation,” Opt. Lasers Eng. 50, 586–592 (2012).
[CrossRef]

Y. Zhou and Y. Chen, “Propagation function for accurate initialization and efficiency enhancement of digital image correlation,” Opt. Lasers Eng. 50, 1789–1797 (2012).
[CrossRef]

2011

B. Pan and K. Li, “A fast digital image correlation method for deformation measurement,” Opt. Lasers Eng. 49, 841–847 (2011).
[CrossRef]

2010

2009

J. Gao and H. Shang, “Deformation-pattern-based digital image correlation method and its application to residual stress measurement,” Appl. Opt. 48, 1371–1381 (2009).
[CrossRef]

B. Pan, “Reliability-guided digital image correlation for image deformation measurement,” Appl. Opt. 48, 1535–1542(2009).
[CrossRef]

B. Pan, A. Asundi, H. Xie, and J. Gao, “Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements,” Opt. Lasers Eng. 47, 865–874 (2009).
[CrossRef]

Y. Wang, M. Sutton, H. Bruck, and H. Schreier, “Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements,” Strain 45, 160–178 (2009).
[CrossRef]

J. Orteu, “3-D computer vision in experimental mechanics,” Opt. Lasers Eng. 47, 282–291 (2009).
[CrossRef]

B. Pan, K. Qian, H. Xie, and A. Asundi, “Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review,” Meas. Sci. Technol. 20, 062001 (2009).
[CrossRef]

2008

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Underst. 110, 346–359 (2008).
[CrossRef]

B. Pan, H. Xie, Z. Wang, K. Qian, and Z. Wang, “Study on subset size selection in digital image correlation for speckle patterns,” Opt. Express 16, 7037–7048 (2008).
[CrossRef]

2007

Y. Sun and J. Pang, “Study of optimal subset size in digital image correlation of speckle pattern images,” Opt. Lasers Eng. 45, 967–974 (2007).
[CrossRef]

M. Sutton, J. Yan, X. Deng, C. Cheng, and P. Zavattieri, “Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading,” Opt. Eng. 46, 051003 (2007).
[CrossRef]

2006

B. Pan, H. Xie, B. Xu, and F. Dai, “Performance of sub-pixel registration algorithms in digital image correlation,” Meas. Sci. Technol. 17, 1615–1621 (2006).
[CrossRef]

Z. Zhang, Y. Kang, H. Wang, Q. Qin, Y. Qiu, and X. Li, “A novel coarse-fine search scheme for digital image correlation method,” Measurement 39, 710–718 (2006).
[CrossRef]

2004

K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” Int. J. Comput. Vis. 60, 63–86 (2004).
[CrossRef]

D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

2003

D. Tsai and C. Lin, “Fast normalized cross correlation for defect detection,” Pattern Recogn. Lett. 24, 2625–2631 (2003).
[CrossRef]

2002

F. Hild, B. Raka, M. Baudequin, S. Roux, and F. Cantelaube, “Multiscale displacement field measurements of compressed mineral-wool samples by digital image correlation,” Appl. Opt. 41, 6815–6828 (2002).
[CrossRef]

H. Schreier and M. Sutton, “Systematic errors in digital image correlation due to undermatched subset shape functions,” Exp. Mech. 42, 303–310 (2002).
[CrossRef]

2001

P. Zhou and K. Goodson, “Subpixel displacement and deformation gradient measurement using digital image/speckle correlation,” Opt. Eng. 40, 1613–1620 (2001).
[CrossRef]

2000

H. Schreier, J. Braasch, and M. Sutton, “Systematic errors in digital image correlation caused by intensity interpolation,” Opt. Eng. 39, 2915–2921 (2000).
[CrossRef]

H. Lu and P. Cary, “Deformation measurements by digital image correlation: implementation of a second-order displacement gradient,” Exp. Mech. 40, 393–400 (2000).
[CrossRef]

1998

G. Vendroux and W. Knauss, “Submicron deformation field measurements. Part 2. improved digital image correlation,” Exp. Mech. 38, 86–92 (1998).
[CrossRef]

1993

1990

Z. Kahn-Jetter and T. Chu, “Three-dimensional displacement measurements using digital image correlation and photogrammic analysis,” Exp. Mech. 30, 10–16 (1990).
[CrossRef]

1989

H. Bruck, S. McNeill, M. Sutton, and W. Peters, “Digital image correlation using Newton–Raphson method of partial differential correction,” Exp. Mech. 29, 261–267 (1989).
[CrossRef]

1986

M. Sutton, C. MingQi, W. Peters, Y. Chao, and S. McNeill, “Application of an optimized digital correlation method to planar deformation analysis,” Image Vis. Comput. 4, 143–150 (1986).
[CrossRef]

1985

T. Chu, W. Ranson, and M. Sutton, “Applications of digital-image-correlation techniques to experimental mechanics,” Exp. Mech. 25, 232–244 (1985).
[CrossRef]

1984

Z. He, M. Sutton, W. Ranson, and W. Peters, “Two-dimensional fluid-velocity measurements by use of digital-speckle correlation techniques,” Exp. Mech. 24, 117–121 (1984).
[CrossRef]

1983

W. Peters, W. Ranson, M. Sutton, T. Chu, and J. Anderson, “Application of digital correlation methods to rigid body mechanics,” Opt. Eng. 22, 738–742 (1983).

M. Sutton, W. Wolters, W. Peters, W. Ranson, and S. McNeill, “Determination of displacements using an improved digital correlation method,” Image Vis. Comput. 1, 133–139 (1983).
[CrossRef]

1982

W. Peters and W. Ranson, “Digital imaging techniques in experimental stress analysis,” Opt. Eng. 21, 427–431 (1982).

1981

M. Fischler and R. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24, 381–395 (1981).
[CrossRef]

Anderson, J.

W. Peters, W. Ranson, M. Sutton, T. Chu, and J. Anderson, “Application of digital correlation methods to rigid body mechanics,” Opt. Eng. 22, 738–742 (1983).

Asundi, A.

B. Pan, A. Asundi, H. Xie, and J. Gao, “Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements,” Opt. Lasers Eng. 47, 865–874 (2009).
[CrossRef]

B. Pan, K. Qian, H. Xie, and A. Asundi, “Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review,” Meas. Sci. Technol. 20, 062001 (2009).
[CrossRef]

Baudequin, M.

Bay, H.

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Underst. 110, 346–359 (2008).
[CrossRef]

Bolles, R.

M. Fischler and R. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24, 381–395 (1981).
[CrossRef]

Braasch, J.

H. Schreier, J. Braasch, and M. Sutton, “Systematic errors in digital image correlation caused by intensity interpolation,” Opt. Eng. 39, 2915–2921 (2000).
[CrossRef]

Bruck, H.

Y. Wang, M. Sutton, H. Bruck, and H. Schreier, “Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements,” Strain 45, 160–178 (2009).
[CrossRef]

H. Bruck, S. McNeill, M. Sutton, and W. Peters, “Digital image correlation using Newton–Raphson method of partial differential correction,” Exp. Mech. 29, 261–267 (1989).
[CrossRef]

Cantelaube, F.

Cary, P.

H. Lu and P. Cary, “Deformation measurements by digital image correlation: implementation of a second-order displacement gradient,” Exp. Mech. 40, 393–400 (2000).
[CrossRef]

Chao, Y.

M. Sutton, C. MingQi, W. Peters, Y. Chao, and S. McNeill, “Application of an optimized digital correlation method to planar deformation analysis,” Image Vis. Comput. 4, 143–150 (1986).
[CrossRef]

Chen, D.

Chen, Y.

Y. Zhou and Y. Chen, “Propagation function for accurate initialization and efficiency enhancement of digital image correlation,” Opt. Lasers Eng. 50, 1789–1797 (2012).
[CrossRef]

Cheng, C.

M. Sutton, J. Yan, X. Deng, C. Cheng, and P. Zavattieri, “Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading,” Opt. Eng. 46, 051003 (2007).
[CrossRef]

Chiang, F.

Chu, T.

Z. Kahn-Jetter and T. Chu, “Three-dimensional displacement measurements using digital image correlation and photogrammic analysis,” Exp. Mech. 30, 10–16 (1990).
[CrossRef]

T. Chu, W. Ranson, and M. Sutton, “Applications of digital-image-correlation techniques to experimental mechanics,” Exp. Mech. 25, 232–244 (1985).
[CrossRef]

W. Peters, W. Ranson, M. Sutton, T. Chu, and J. Anderson, “Application of digital correlation methods to rigid body mechanics,” Opt. Eng. 22, 738–742 (1983).

Dafang, W.

B. Pan, W. Dafang, and X. Yong, “Incremental calculation for large deformation measurement using reliability-guided digital image correlation,” Opt. Lasers Eng. 50, 586–592 (2012).
[CrossRef]

Dai, F.

B. Pan, H. Xie, B. Xu, and F. Dai, “Performance of sub-pixel registration algorithms in digital image correlation,” Meas. Sci. Technol. 17, 1615–1621 (2006).
[CrossRef]

Deng, X.

M. Sutton, J. Yan, X. Deng, C. Cheng, and P. Zavattieri, “Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading,” Opt. Eng. 46, 051003 (2007).
[CrossRef]

Don, H.

Ess, A.

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Underst. 110, 346–359 (2008).
[CrossRef]

Fischler, M.

M. Fischler and R. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24, 381–395 (1981).
[CrossRef]

Gao, J.

B. Pan, A. Asundi, H. Xie, and J. Gao, “Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements,” Opt. Lasers Eng. 47, 865–874 (2009).
[CrossRef]

J. Gao and H. Shang, “Deformation-pattern-based digital image correlation method and its application to residual stress measurement,” Appl. Opt. 48, 1371–1381 (2009).
[CrossRef]

Goodson, K.

P. Zhou and K. Goodson, “Subpixel displacement and deformation gradient measurement using digital image/speckle correlation,” Opt. Eng. 40, 1613–1620 (2001).
[CrossRef]

Harris, C.

C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of Alvey Vision Conference, Vol. 15 (British Machine Vision Association and Society for Pattern Recognition, 1988), p. 50.

He, Z.

Z. He, M. Sutton, W. Ranson, and W. Peters, “Two-dimensional fluid-velocity measurements by use of digital-speckle correlation techniques,” Exp. Mech. 24, 117–121 (1984).
[CrossRef]

Hild, F.

Hu, Z.

Hua, T.

Kahn-Jetter, Z.

Z. Kahn-Jetter and T. Chu, “Three-dimensional displacement measurements using digital image correlation and photogrammic analysis,” Exp. Mech. 30, 10–16 (1990).
[CrossRef]

Kang, Y.

Z. Zhang, Y. Kang, H. Wang, Q. Qin, Y. Qiu, and X. Li, “A novel coarse-fine search scheme for digital image correlation method,” Measurement 39, 710–718 (2006).
[CrossRef]

Knauss, W.

G. Vendroux and W. Knauss, “Submicron deformation field measurements. Part 2. improved digital image correlation,” Exp. Mech. 38, 86–92 (1998).
[CrossRef]

Li, K.

B. Pan and K. Li, “A fast digital image correlation method for deformation measurement,” Opt. Lasers Eng. 49, 841–847 (2011).
[CrossRef]

Li, X.

Z. Zhang, Y. Kang, H. Wang, Q. Qin, Y. Qiu, and X. Li, “A novel coarse-fine search scheme for digital image correlation method,” Measurement 39, 710–718 (2006).
[CrossRef]

Lin, C.

D. Tsai and C. Lin, “Fast normalized cross correlation for defect detection,” Pattern Recogn. Lett. 24, 2625–2631 (2003).
[CrossRef]

Lowe, D.

D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

Lu, H.

H. Lu and P. Cary, “Deformation measurements by digital image correlation: implementation of a second-order displacement gradient,” Exp. Mech. 40, 393–400 (2000).
[CrossRef]

Lu, J.

Lu, Z.

B. Pan, Z. Wang, and Z. Lu, “Genuine full-field deformation measurement of an object with complex shape using reliability-guided digital image correlation,” Opt. Express 18, 1011–1023 (2010).
[CrossRef]

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,” Opt. Lasers Eng. 48, 469–477 (2010).
[CrossRef]

McNeill, S.

H. Bruck, S. McNeill, M. Sutton, and W. Peters, “Digital image correlation using Newton–Raphson method of partial differential correction,” Exp. Mech. 29, 261–267 (1989).
[CrossRef]

M. Sutton, C. MingQi, W. Peters, Y. Chao, and S. McNeill, “Application of an optimized digital correlation method to planar deformation analysis,” Image Vis. Comput. 4, 143–150 (1986).
[CrossRef]

M. Sutton, W. Wolters, W. Peters, W. Ranson, and S. McNeill, “Determination of displacements using an improved digital correlation method,” Image Vis. Comput. 1, 133–139 (1983).
[CrossRef]

Mikolajczyk, K.

K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” Int. J. Comput. Vis. 60, 63–86 (2004).
[CrossRef]

MingQi, C.

M. Sutton, C. MingQi, W. Peters, Y. Chao, and S. McNeill, “Application of an optimized digital correlation method to planar deformation analysis,” Image Vis. Comput. 4, 143–150 (1986).
[CrossRef]

Nocedal, J.

J. Nocedal and S. Wright, Numerical Optimization, 2nd ed. (Springer Verlag, 2006).

Orteu, J.

J. Orteu, “3-D computer vision in experimental mechanics,” Opt. Lasers Eng. 47, 282–291 (2009).
[CrossRef]

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

Pan, B.

B. Pan, W. Dafang, and X. Yong, “Incremental calculation for large deformation measurement using reliability-guided digital image correlation,” Opt. Lasers Eng. 50, 586–592 (2012).
[CrossRef]

B. Pan and K. Li, “A fast digital image correlation method for deformation measurement,” Opt. Lasers Eng. 49, 841–847 (2011).
[CrossRef]

B. Pan, H. Xie, and Z. Wang, “Equivalence of digital image correlation criteria for pattern matching,” Appl. Opt. 49, 5501–5509 (2010).
[CrossRef]

B. Pan, Z. Wang, and Z. Lu, “Genuine full-field deformation measurement of an object with complex shape using reliability-guided digital image correlation,” Opt. Express 18, 1011–1023 (2010).
[CrossRef]

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,” Opt. Lasers Eng. 48, 469–477 (2010).
[CrossRef]

B. Pan, “Reliability-guided digital image correlation for image deformation measurement,” Appl. Opt. 48, 1535–1542(2009).
[CrossRef]

B. Pan, A. Asundi, H. Xie, and J. Gao, “Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements,” Opt. Lasers Eng. 47, 865–874 (2009).
[CrossRef]

B. Pan, K. Qian, H. Xie, and A. Asundi, “Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review,” Meas. Sci. Technol. 20, 062001 (2009).
[CrossRef]

B. Pan, H. Xie, Z. Wang, K. Qian, and Z. Wang, “Study on subset size selection in digital image correlation for speckle patterns,” Opt. Express 16, 7037–7048 (2008).
[CrossRef]

B. Pan, H. Xie, B. Xu, and F. Dai, “Performance of sub-pixel registration algorithms in digital image correlation,” Meas. Sci. Technol. 17, 1615–1621 (2006).
[CrossRef]

Pang, J.

Y. Sun and J. Pang, “Study of optimal subset size in digital image correlation of speckle pattern images,” Opt. Lasers Eng. 45, 967–974 (2007).
[CrossRef]

Peters, W.

H. Bruck, S. McNeill, M. Sutton, and W. Peters, “Digital image correlation using Newton–Raphson method of partial differential correction,” Exp. Mech. 29, 261–267 (1989).
[CrossRef]

M. Sutton, C. MingQi, W. Peters, Y. Chao, and S. McNeill, “Application of an optimized digital correlation method to planar deformation analysis,” Image Vis. Comput. 4, 143–150 (1986).
[CrossRef]

Z. He, M. Sutton, W. Ranson, and W. Peters, “Two-dimensional fluid-velocity measurements by use of digital-speckle correlation techniques,” Exp. Mech. 24, 117–121 (1984).
[CrossRef]

W. Peters, W. Ranson, M. Sutton, T. Chu, and J. Anderson, “Application of digital correlation methods to rigid body mechanics,” Opt. Eng. 22, 738–742 (1983).

M. Sutton, W. Wolters, W. Peters, W. Ranson, and S. McNeill, “Determination of displacements using an improved digital correlation method,” Image Vis. Comput. 1, 133–139 (1983).
[CrossRef]

W. Peters and W. Ranson, “Digital imaging techniques in experimental stress analysis,” Opt. Eng. 21, 427–431 (1982).

Qian, K.

B. Pan, K. Qian, H. Xie, and A. Asundi, “Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review,” Meas. Sci. Technol. 20, 062001 (2009).
[CrossRef]

B. Pan, H. Xie, Z. Wang, K. Qian, and Z. Wang, “Study on subset size selection in digital image correlation for speckle patterns,” Opt. Express 16, 7037–7048 (2008).
[CrossRef]

Qin, Q.

Z. Zhang, Y. Kang, H. Wang, Q. Qin, Y. Qiu, and X. Li, “A novel coarse-fine search scheme for digital image correlation method,” Measurement 39, 710–718 (2006).
[CrossRef]

Qiu, Y.

Z. Zhang, Y. Kang, H. Wang, Q. Qin, Y. Qiu, and X. Li, “A novel coarse-fine search scheme for digital image correlation method,” Measurement 39, 710–718 (2006).
[CrossRef]

Raka, B.

Ranson, W.

T. Chu, W. Ranson, and M. Sutton, “Applications of digital-image-correlation techniques to experimental mechanics,” Exp. Mech. 25, 232–244 (1985).
[CrossRef]

Z. He, M. Sutton, W. Ranson, and W. Peters, “Two-dimensional fluid-velocity measurements by use of digital-speckle correlation techniques,” Exp. Mech. 24, 117–121 (1984).
[CrossRef]

W. Peters, W. Ranson, M. Sutton, T. Chu, and J. Anderson, “Application of digital correlation methods to rigid body mechanics,” Opt. Eng. 22, 738–742 (1983).

M. Sutton, W. Wolters, W. Peters, W. Ranson, and S. McNeill, “Determination of displacements using an improved digital correlation method,” Image Vis. Comput. 1, 133–139 (1983).
[CrossRef]

W. Peters and W. Ranson, “Digital imaging techniques in experimental stress analysis,” Opt. Eng. 21, 427–431 (1982).

Roux, S.

Schmid, C.

K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” Int. J. Comput. Vis. 60, 63–86 (2004).
[CrossRef]

Schreier, H.

Y. Wang, M. Sutton, H. Bruck, and H. Schreier, “Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements,” Strain 45, 160–178 (2009).
[CrossRef]

H. Schreier and M. Sutton, “Systematic errors in digital image correlation due to undermatched subset shape functions,” Exp. Mech. 42, 303–310 (2002).
[CrossRef]

H. Schreier, J. Braasch, and M. Sutton, “Systematic errors in digital image correlation caused by intensity interpolation,” Opt. Eng. 39, 2915–2921 (2000).
[CrossRef]

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

Shang, H.

Stephens, M.

C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of Alvey Vision Conference, Vol. 15 (British Machine Vision Association and Society for Pattern Recognition, 1988), p. 50.

Sun, Y.

Y. Sun and J. Pang, “Study of optimal subset size in digital image correlation of speckle pattern images,” Opt. Lasers Eng. 45, 967–974 (2007).
[CrossRef]

Sutton, M.

Y. Wang, M. Sutton, H. Bruck, and H. Schreier, “Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements,” Strain 45, 160–178 (2009).
[CrossRef]

M. Sutton, J. Yan, X. Deng, C. Cheng, and P. Zavattieri, “Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading,” Opt. Eng. 46, 051003 (2007).
[CrossRef]

H. Schreier and M. Sutton, “Systematic errors in digital image correlation due to undermatched subset shape functions,” Exp. Mech. 42, 303–310 (2002).
[CrossRef]

H. Schreier, J. Braasch, and M. Sutton, “Systematic errors in digital image correlation caused by intensity interpolation,” Opt. Eng. 39, 2915–2921 (2000).
[CrossRef]

H. Bruck, S. McNeill, M. Sutton, and W. Peters, “Digital image correlation using Newton–Raphson method of partial differential correction,” Exp. Mech. 29, 261–267 (1989).
[CrossRef]

M. Sutton, C. MingQi, W. Peters, Y. Chao, and S. McNeill, “Application of an optimized digital correlation method to planar deformation analysis,” Image Vis. Comput. 4, 143–150 (1986).
[CrossRef]

T. Chu, W. Ranson, and M. Sutton, “Applications of digital-image-correlation techniques to experimental mechanics,” Exp. Mech. 25, 232–244 (1985).
[CrossRef]

Z. He, M. Sutton, W. Ranson, and W. Peters, “Two-dimensional fluid-velocity measurements by use of digital-speckle correlation techniques,” Exp. Mech. 24, 117–121 (1984).
[CrossRef]

W. Peters, W. Ranson, M. Sutton, T. Chu, and J. Anderson, “Application of digital correlation methods to rigid body mechanics,” Opt. Eng. 22, 738–742 (1983).

M. Sutton, W. Wolters, W. Peters, W. Ranson, and S. McNeill, “Determination of displacements using an improved digital correlation method,” Image Vis. Comput. 1, 133–139 (1983).
[CrossRef]

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

Tan, Y.

Tsai, D.

D. Tsai and C. Lin, “Fast normalized cross correlation for defect detection,” Pattern Recogn. Lett. 24, 2625–2631 (2003).
[CrossRef]

Tuytelaars, T.

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Underst. 110, 346–359 (2008).
[CrossRef]

Van Gool, L.

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Underst. 110, 346–359 (2008).
[CrossRef]

Vendroux, G.

G. Vendroux and W. Knauss, “Submicron deformation field measurements. Part 2. improved digital image correlation,” Exp. Mech. 38, 86–92 (1998).
[CrossRef]

Wang, H.

Z. Zhang, Y. Kang, H. Wang, Q. Qin, Y. Qiu, and X. Li, “A novel coarse-fine search scheme for digital image correlation method,” Measurement 39, 710–718 (2006).
[CrossRef]

Wang, Y.

Y. Wang, M. Sutton, H. Bruck, and H. Schreier, “Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements,” Strain 45, 160–178 (2009).
[CrossRef]

Wang, Z.

Wolters, W.

M. Sutton, W. Wolters, W. Peters, W. Ranson, and S. McNeill, “Determination of displacements using an improved digital correlation method,” Image Vis. Comput. 1, 133–139 (1983).
[CrossRef]

Wright, S.

J. Nocedal and S. Wright, Numerical Optimization, 2nd ed. (Springer Verlag, 2006).

Xie, H.

B. Pan, H. Xie, and Z. Wang, “Equivalence of digital image correlation criteria for pattern matching,” Appl. Opt. 49, 5501–5509 (2010).
[CrossRef]

Z. Hu, H. Xie, J. Lu, T. Hua, and J. Zhu, “Study of the performance of different subpixel image correlation methods in 3D digital image correlation,” Appl. Opt. 49, 4044–4051 (2010).
[CrossRef]

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,” Opt. Lasers Eng. 48, 469–477 (2010).
[CrossRef]

B. Pan, A. Asundi, H. Xie, and J. Gao, “Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements,” Opt. Lasers Eng. 47, 865–874 (2009).
[CrossRef]

B. Pan, K. Qian, H. Xie, and A. Asundi, “Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review,” Meas. Sci. Technol. 20, 062001 (2009).
[CrossRef]

B. Pan, H. Xie, Z. Wang, K. Qian, and Z. Wang, “Study on subset size selection in digital image correlation for speckle patterns,” Opt. Express 16, 7037–7048 (2008).
[CrossRef]

B. Pan, H. Xie, B. Xu, and F. Dai, “Performance of sub-pixel registration algorithms in digital image correlation,” Meas. Sci. Technol. 17, 1615–1621 (2006).
[CrossRef]

Xu, B.

B. Pan, H. Xie, B. Xu, and F. Dai, “Performance of sub-pixel registration algorithms in digital image correlation,” Meas. Sci. Technol. 17, 1615–1621 (2006).
[CrossRef]

Yan, J.

M. Sutton, J. Yan, X. Deng, C. Cheng, and P. Zavattieri, “Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading,” Opt. Eng. 46, 051003 (2007).
[CrossRef]

Yong, X.

B. Pan, W. Dafang, and X. Yong, “Incremental calculation for large deformation measurement using reliability-guided digital image correlation,” Opt. Lasers Eng. 50, 586–592 (2012).
[CrossRef]

Zavattieri, P.

M. Sutton, J. Yan, X. Deng, C. Cheng, and P. Zavattieri, “Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading,” Opt. Eng. 46, 051003 (2007).
[CrossRef]

Zhang, Z.

Z. Zhang, Y. Kang, H. Wang, Q. Qin, Y. Qiu, and X. Li, “A novel coarse-fine search scheme for digital image correlation method,” Measurement 39, 710–718 (2006).
[CrossRef]

Zhou, P.

P. Zhou and K. Goodson, “Subpixel displacement and deformation gradient measurement using digital image/speckle correlation,” Opt. Eng. 40, 1613–1620 (2001).
[CrossRef]

Zhou, Y.

Y. Zhou and Y. Chen, “Propagation function for accurate initialization and efficiency enhancement of digital image correlation,” Opt. Lasers Eng. 50, 1789–1797 (2012).
[CrossRef]

Zhu, J.

Appl. Opt.

Commun. ACM

M. Fischler and R. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24, 381–395 (1981).
[CrossRef]

Comput. Vis. Image Underst.

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Underst. 110, 346–359 (2008).
[CrossRef]

Exp. Mech.

H. Bruck, S. McNeill, M. Sutton, and W. Peters, “Digital image correlation using Newton–Raphson method of partial differential correction,” Exp. Mech. 29, 261–267 (1989).
[CrossRef]

G. Vendroux and W. Knauss, “Submicron deformation field measurements. Part 2. improved digital image correlation,” Exp. Mech. 38, 86–92 (1998).
[CrossRef]

H. Lu and P. Cary, “Deformation measurements by digital image correlation: implementation of a second-order displacement gradient,” Exp. Mech. 40, 393–400 (2000).
[CrossRef]

H. Schreier and M. Sutton, “Systematic errors in digital image correlation due to undermatched subset shape functions,” Exp. Mech. 42, 303–310 (2002).
[CrossRef]

Z. He, M. Sutton, W. Ranson, and W. Peters, “Two-dimensional fluid-velocity measurements by use of digital-speckle correlation techniques,” Exp. Mech. 24, 117–121 (1984).
[CrossRef]

T. Chu, W. Ranson, and M. Sutton, “Applications of digital-image-correlation techniques to experimental mechanics,” Exp. Mech. 25, 232–244 (1985).
[CrossRef]

Z. Kahn-Jetter and T. Chu, “Three-dimensional displacement measurements using digital image correlation and photogrammic analysis,” Exp. Mech. 30, 10–16 (1990).
[CrossRef]

Image Vis. Comput.

M. Sutton, W. Wolters, W. Peters, W. Ranson, and S. McNeill, “Determination of displacements using an improved digital correlation method,” Image Vis. Comput. 1, 133–139 (1983).
[CrossRef]

M. Sutton, C. MingQi, W. Peters, Y. Chao, and S. McNeill, “Application of an optimized digital correlation method to planar deformation analysis,” Image Vis. Comput. 4, 143–150 (1986).
[CrossRef]

Int. J. Comput. Vis.

K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” Int. J. Comput. Vis. 60, 63–86 (2004).
[CrossRef]

D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

Meas. Sci. Technol.

B. Pan, H. Xie, B. Xu, and F. Dai, “Performance of sub-pixel registration algorithms in digital image correlation,” Meas. Sci. Technol. 17, 1615–1621 (2006).
[CrossRef]

B. Pan, K. Qian, H. Xie, and A. Asundi, “Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review,” Meas. Sci. Technol. 20, 062001 (2009).
[CrossRef]

Measurement

Z. Zhang, Y. Kang, H. Wang, Q. Qin, Y. Qiu, and X. Li, “A novel coarse-fine search scheme for digital image correlation method,” Measurement 39, 710–718 (2006).
[CrossRef]

Opt. Eng.

P. Zhou and K. Goodson, “Subpixel displacement and deformation gradient measurement using digital image/speckle correlation,” Opt. Eng. 40, 1613–1620 (2001).
[CrossRef]

W. Peters and W. Ranson, “Digital imaging techniques in experimental stress analysis,” Opt. Eng. 21, 427–431 (1982).

W. Peters, W. Ranson, M. Sutton, T. Chu, and J. Anderson, “Application of digital correlation methods to rigid body mechanics,” Opt. Eng. 22, 738–742 (1983).

M. Sutton, J. Yan, X. Deng, C. Cheng, and P. Zavattieri, “Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading,” Opt. Eng. 46, 051003 (2007).
[CrossRef]

H. Schreier, J. Braasch, and M. Sutton, “Systematic errors in digital image correlation caused by intensity interpolation,” Opt. Eng. 39, 2915–2921 (2000).
[CrossRef]

Opt. Express

Opt. Lasers Eng.

B. Pan and K. Li, “A fast digital image correlation method for deformation measurement,” Opt. Lasers Eng. 49, 841–847 (2011).
[CrossRef]

B. Pan, A. Asundi, H. Xie, and J. Gao, “Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements,” Opt. Lasers Eng. 47, 865–874 (2009).
[CrossRef]

B. Pan, W. Dafang, and X. Yong, “Incremental calculation for large deformation measurement using reliability-guided digital image correlation,” Opt. Lasers Eng. 50, 586–592 (2012).
[CrossRef]

Y. Zhou and Y. Chen, “Propagation function for accurate initialization and efficiency enhancement of digital image correlation,” Opt. Lasers Eng. 50, 1789–1797 (2012).
[CrossRef]

Y. Sun and J. Pang, “Study of optimal subset size in digital image correlation of speckle pattern images,” Opt. Lasers Eng. 45, 967–974 (2007).
[CrossRef]

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,” Opt. Lasers Eng. 48, 469–477 (2010).
[CrossRef]

J. Orteu, “3-D computer vision in experimental mechanics,” Opt. Lasers Eng. 47, 282–291 (2009).
[CrossRef]

Pattern Recogn. Lett.

D. Tsai and C. Lin, “Fast normalized cross correlation for defect detection,” Pattern Recogn. Lett. 24, 2625–2631 (2003).
[CrossRef]

Strain

Y. Wang, M. Sutton, H. Bruck, and H. Schreier, “Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements,” Strain 45, 160–178 (2009).
[CrossRef]

Other

D. Lowe, “Demo software: Sift keypoint detector” (www.cs.ubc.ca/lowe/keypoints).

J. Nocedal and S. Wright, Numerical Optimization, 2nd ed. (Springer Verlag, 2006).

C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of Alvey Vision Conference, Vol. 15 (British Machine Vision Association and Society for Pattern Recognition, 1988), p. 50.

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

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Figures (6)

Fig. 1.
Fig. 1.

SIFT keypoint detection and matching between a reference speckle image and a deformed image after clockwise rotation of 20 deg.

Fig. 2.
Fig. 2.

Estimating deformation of seed point from matched keypoints.

Fig. 3.
Fig. 3.

Simulated speckle pattern using N = 3500 , r = 2.5 , and I max = 127.5 : (a) image and ROI and (b) histogram.

Fig. 4.
Fig. 4.

Comparison of seed point initialization without and with RANSAC. (a), (b) Six nearest-matched keypoints in reference and deformed image. Correct matches identified by RANSAC are labeled as “ o ” and false matches as “ x .” (c), (d) Initialization without and with RANSAC. Prescribed value is shown in yellow for comparison.

Fig. 5.
Fig. 5.

Speckle images of the test foam specimen: (a) reference image and (b)–(d) deformed image with compression of 12%, 24%, and 36%, respectively. ROI as well as seed point and its subset are also shown in (a).

Fig. 6.
Fig. 6.

Computed v -displacement field (in pixels) of the foam compression experiment.

Tables (5)

Tables Icon

Table 1. Results on Rigid Body Translation in x - and y -Direction: Prescribed Translation, Success Rate, RMSE of Initial u - / v - Displacement of Seed Point and Parameter Transfer, RMSE of Optimized u - / v - Displacement

Tables Icon

Table 2. Results on Rigid Body Rotation: Prescribed Rotation, Success Rate, RMSE of Initial u - / v - Displacement of Seed Point and Parameter Transfer, RMSE of Optimized u - / v - Displacement

Tables Icon

Table 3. Results on Rigid Body Rotation Combined with Uniform Strain: Prescribed Rotation, Success Rate, RMSE of Initial u - / v - Displacement of Seed Point and Parameter Transfer, RMSE of Optimized u - / v - Displacement

Tables Icon

Table 4. Comparison of Seed Point Computation without and with RANSAC on Uniform Strain Combined with Rotation of 20 deg

Tables Icon

Table 5. Results of Seed Point Computation on Foam Compression Experiment

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

C ZNCC ( p ) = Ω ( F ( x , y ) F ¯ ) ( G ( x * , y * ) G ¯ ) Ω ( F ( x , y ) F ¯ ) 2 Ω ( G ( x * , y * ) G ¯ ) 2 .
C ZNSSD ( p ) = Ω [ F ( x , y ) F ¯ Ω ( F ( x , y ) F ¯ ) 2 G ( x * , y * ) G ¯ Ω ( G ( x * , y * ) G ¯ ) 2 ] 2 .
{ x * = x + u + u x ( x x 0 ) + u y ( y y 0 ) y * = y + v + v x ( x x 0 ) + v y ( y y 0 ) ,
{ a 1 + ( 1 + a 2 ) x 1 + a 3 y 1 = x 1 * a 4 + a 5 x 1 + ( 1 + a 6 ) y 1 = y 1 * ,
{ u * = u + u x ( x 0 * x 0 ) + u y ( y 0 * y 0 ) v * = v + v x ( x 0 * x 0 ) + v y ( y 0 * y 0 ) ,
F ( x , y ) = i = 1 N I i · exp ( ( x x i ) 2 + ( y y i ) 2 r 2 ) .

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