[2] Pataky G J, Sehitoglu H. Experimentalmethodology for studying strain heterogeneity with microstructural data from high temperature deformation[J]. Experimental Mechanics, 2015, 55(1): 53-63.
[3] O′Connor S J, Nowell D, Dragnevski K I. Measurement of fatigue crack deformation on the macro- and micro-scale: uniform and non-uniform loading[J]. International Journal of Fatigue, 2016, 89:66-76.
[4] Shan B H, Huo X Y, Liu Y. Astereovision measurement method using epipolar constraint to correct digital image correlation matching[J]. Chinese Journal of Lasers, 2017, 44(8): 0804003.
[5] Jerlhag E, Weinland C, Porcu P, et al. On the use of the digital image correlation method for heterogeneous deformation measurement of porous solids[J]. Optics & Lasers in Engineering, 2011, 49(2): 200-209.
[6] Sun Y F, Pang J H L. Study of optimal subset size in digital image correlation of speckle pattern images[J]. Optics and Lasers in Engineering, 2007, 45(9): 967-974.
[7] Pan B, Xie H, Wang Z, et al. Study on subset size selection in digital image correlation for speckle patterns[J]. Optics Express, 2008, 16(10): 7037-7048.
[8] Pan B, Wu D F, Xie H M, et al. Spatial-gradient-based digital volume correlation technique for internal deformation measurement[J]. Acta Optica Sinica, 2011, 31(6): 120-126.
[9] Hassan G M, Macnish C, Dyskin A, et al. Digital image correlation with dynamic subset selection[J]. Optics and Lasers in Engineering, 2016, 84: 1-9.
[10] Zhan Q, Yuan Y, Fan X T, et al. Digital image correlation involves an inverse problem: A regularization scheme based on subset size constraint[J]. Optics and Lasers in Engineering, 2016, 81: 54-62.
[11] Yuan Y, Huang J Y, Peng X L, et al. Accurate displacement measurement via a self-adaptive digital image correlation method based on a weighted ZNSSD criterion[J]. Optics and Lasers in Engineering, 2014, 52(1): 75-85.
[12] Wittevrongel L, Debruyne D, Lomov S V, et al. Implementation of convergence in adaptive global digital image correlation[J]. Experimental Mechanics, 2016, 56(5): 797-811.
[13] Wittevrongel L, Lava P, Lomov S V, et al. A self adaptive global digital image correlation algorithm[J]. Experimental Mechanics, 2015, 55(2): 361-378.
[14] Huang J, Pan X, Peng X, et al. Digital image correlation with self-adaptive Gaussian windows[J]. Experimental Mechanics, 2013, 53(3): 505-512.
[15] Lowe D G. Distinctiveimage features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[16] Sutton M A, Mingqi C Q, Peters W H, et al. Application of an optimized digital correlation method to planar deformation analysis[J]. Image and Vision Computing, 1986, 4(3): 143-150.
[17] Yu Z J, Wang S B. Improve PCA-SIFT algorithm for matching stereo system[J]. Laser & Optoelectronics Progress, 2016, 53(3): 031501.
[19] Mao J G, Zhang P Z, Shen H, et al. Reverse mapping for generating simulated deformed speckle patterns[J]. Journal of Optoelectronics·Laser, 2015, 26(12): 2433-2439.
[20] Cofaru C, Philips W, Paepegem W V. A novel speckle pattern—adaptive digital image correlation approach with robust strain calculation[J]. Optics and Lasers in Engineering, 2012, 50(2): 187-198.
[21] Wu R, Kong C, Li K, et al. Real-time digital image correlation for dynamic strain measurement[J]. Experimental Mechanics, 2016, 56(5): 1-11.