Open Access

The Effect of Low-pass Pre-filtering on Subvoxel Registration Algorithms in Digital Volume Correlation: A revisited study


Cite

[1] Bay, B.K., Smith, T.S., Fyhrie, D.P., Saad, M. (1999). Digital volume correlation: Three-dimensional strain mapping using X-ray tomography. Experimental Mechanics, 39 (3), 217-226.10.1007/BF02323555Search in Google Scholar

[2] Pan, B., Wang, B. (2020). Some recent advances in digital volume correlation. Optics & Lasers in Engineering, https://doi.org/10.1016/j.optlaseng.2020.106189.10.1016/j.optlaseng.2020.106189Search in Google Scholar

[3] Bay, B.K. (2008). Methods and applications of digital volume correlation. The Journal of Strain Analysis for Engineering Design, 43 (8), 745-760.10.1243/03093247JSA436Search in Google Scholar

[4] Roux, S., Hild, F., Viot, P., Bernard, D. (2008). Three-dimensional image correlation from X-ray computed tomography of solid foam. Composites Part A: Applied Science & Manufacturing, 39 (8), 1253-1265.10.1016/j.compositesa.2007.11.011Search in Google Scholar

[5] Benoit, A., Guérard, S., Gillet, B., et al. (2009). 3D analysis from micro-MRI during in situ compression on cancellous bone. Journal of Biomechanics, 42 (14), 2381-2386.10.1016/j.jbiomech.2009.06.03419643419Search in Google Scholar

[6] Poinard, C., Piotrowska, E., Malecot, Y., Daudeville, L., Landis, E.N. (2012). Compression triaxial behavior of concrete: The role of the mesostructure by analysis of X-ray tomographic images. European Journal of Environmental and Civil Engineering, 16, 115-136.10.1080/19648189.2012.682458Search in Google Scholar

[7] Mao, L., Yuan, Z., Yang M., Liu, H., Chiang, F. (2019). 3D strain evolution in concrete using in situ X-ray computed tomography testing and digital volumetric speckle photography. Measurement, 133, 456-467.10.1016/j.measurement.2018.10.049Search in Google Scholar

[8] Wang, B., Pan, B., Lubineau, G. (2018). Morphological evolution and internal strain mapping of pomelo peel using X-ray computed tomography and digital volume correlation. Materials & Design, 137, 305-315.10.1016/j.matdes.2017.10.038Search in Google Scholar

[9] Mendoza, A., Schneider, J., Parra, E., Obert, E., Roux, S. (2019). Differentiating 3D textile composites: A novel field of application for Digital Volume Correlation. Composite Structures, 208, 735-743.10.1016/j.compstruct.2018.10.008Search in Google Scholar

[10] Shahbazi, M. (2013). Hybrid 3D dynamic measurement by particle swarm optimization and photogrammetric tracking. Measurement Science Review, 13 (6), 298-304.10.2478/msr-2013-0044Search in Google Scholar

[11] Pan, B. (2018). Digital image correlation for surface deformation measurement: Historical developments, recent advances and future goals. Measurement Science and Technology, 29 (8), 082001.10.1088/1361-6501/aac55bSearch in Google Scholar

[12] Bruck, H.A., McNeill, S.R., Sutton, M.A., Peters III, W.H. (1989). Digital image correlation using Newton-Raphson method of partial differential correction. Experimental Mechanics, 29 (3), 261-267.10.1007/BF02321405Search in Google Scholar

[13] Zhang, J., Jin, G., Ma, S., Meng, L. (2003). Application of an improved subpixel registration algorithm on digital speckle correlation measurement. Optics & Laser Technology, 35 (7), 533-542.10.1016/S0030-3992(03)00069-0Search in Google Scholar

[14] Hung, P.C., Voloshin, A.S. (2003). In-plane strain measurement by digital image correlation. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 25 (3), 215-221.10.1590/S1678-58782003000300001Search in Google Scholar

[15] Pan, B., Xie, H., Xu, B., Dai, F. (2006). Performance of sub-pixel registration algorithms in digital image correlation. Measurement Science and Technology, 17 (6), 1615.Search in Google Scholar

[16] 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. Optics & Lasers in Engineering, 47, 865-874.10.1016/j.optlaseng.2008.10.014Search in Google Scholar

[17] Pan, B., Li, K. (2011). A fast digital image correlation method for deformation measurement. Optics & Lasers in Engineering, 49 (7), 841-847.10.1016/j.optlaseng.2011.02.023Search in Google Scholar

[18] Huang, J., Pan, X., Li, S., Peng, X., Xiong, C., Fang, J. (2011). A digital volume correlation technique for 3-D deformation measurements of soft gels. International Journal of Applied Mechanics, 3 (2), 335-354.10.1142/S1758825111001019Search in Google Scholar

[19] Pan, B., Wu, D., Wang, Z. (2012). Internal displacement and strain measurement using digital volume correlation: A least-squares framework. Measurement Science and Technology, 23 (4), 045002.10.1088/0957-0233/23/4/045002Search in Google Scholar

[20] Pan, B., Li, K., Tong, W. (2013). Fast, robust and accurate digital image correlation calculation without redundant computations. Experimental Mechanics, 53 (7), 1277-1289.10.1007/s11340-013-9717-6Search in Google Scholar

[21] Shao, X., Dai, X., He, X. (2015). Noise robustness and parallel computation of the inverse compositional Gauss–Newton algorithm in digital image correlation. Optics & Lasers in Engineering, 71, 9-19.10.1016/j.optlaseng.2015.03.005Search in Google Scholar

[22] Pan, B., Wang, B. (2016). Digital image correlation with enhanced accuracy and efficiency: A comparison of two subpixel registration algorithms. Experimental Mechanics, 56 (8), 1395-1409.10.1007/s11340-016-0180-zSearch in Google Scholar

[23] Jiang, Z., Kemao, Q., Miao, H., Yang, J., Tang, L. (2015). Path-independent digital image correlation with high accuracy, speed and robustness. Optics & Lasers in Engineering, 65, 93-102.10.1016/j.optlaseng.2014.06.011Search in Google Scholar

[24] Li, W., Li, Y., Liang, J. (2019). Enhanced feature-based path-independent initial value estimation for robust point-wise digital image correlation. Optics and Lasers & Engineering, 121, 189-202.10.1016/j.optlaseng.2019.04.016Search in Google Scholar

[25] Schreier, H.W., Sutton, M.A. (2002). Systematic errors in digital image correlation due to undermatched subset shape functions. Experimental Mechanics, 42 (3), 303-310.10.1007/BF02410987Search in Google Scholar

[26] Schreier, H.W., Braasch, J.R., Sutton, M.A. (2000). Systematic errors in digital image correlation caused by intensity interpolation. Optical Engineering, 39 (11), 2915-2921.10.1117/1.1314593Search in Google Scholar

[27] Tong, W. (2011). Subpixel image registration with reduced bias. Optics letters, 36 (5), 763-765.10.1364/OL.36.00076321368975Search in Google Scholar

[28] Bay, B.K., Smith, T.S., Fyhrie, D.P. (1999). Digital volume correlation: Three-dimensional strain mapping using X-ray tomography. Experimental Mechanics, 39 (3), 217-226.10.1007/BF02323555Search in Google Scholar

[29] Pan, B. (2013). Bias error reduction of digital image correlation using Gaussian pre-filtering. Optics & Lasers in Engineering, 51 (10), 1161-1167.10.1016/j.optlaseng.2013.04.009Search in Google Scholar

[30] Shahbazi, M. (2013). Hybrid 3D dynamic measurement by particle swarm optimization and photogrammetric tracking. Measurement Science Review, 13 (6), 298-304.10.2478/msr-2013-0044Search in Google Scholar

[31] Wang, B., Pan, B. (2019). Self-adaptive digital volume correlation for unknown deformation fields. Experimental Mechanics, 59 (2), 149-162.10.1007/s11340-018-00455-2Search in Google Scholar

[32] Pan, B., Zou, X. (2020). Quasi-Gauss point digital image/volume correlation: A simple approach for reducing systematic errors due to undermatched shape functions. Experimental Mechanics, 1-12.10.1007/s11340-020-00588-3Search in Google Scholar

[33] Su, Y., Zhang, Q., Xu, X., Gao, Z., Wu, S. (2018). Interpolation bias for the inverse compositional Gauss– Newton algorithm in digital image correlation. Optics & Lasers in Engineering, 100, 267-278.10.1016/j.optlaseng.2017.09.013Search in Google Scholar

[34] Pan, B., Wang, B., Wu, D., Lubineau, G. (2014). An efficient and accurate 3D displacements tracking strategy for digital volume correlation. Optics & Lasers in Engineering, 58, 126-135.10.1016/j.optlaseng.2014.02.003Search in Google Scholar

[35] Limodin, N., Réthoré, J., Adrien, J., et al. (2011). Analysis and artifact correction for volume correlation measurements using tomographic images from a laboratory X-ray source. Experimental Mechanics, 51 (6), 959-970.10.1007/s11340-010-9397-4Search in Google Scholar

[36] Baker, S., Matthews, I. (2001). Equivalence and efficiency of image alignment algorithms. In 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001). IEEE.10.1109/CVPR.2001.990652Search in Google Scholar

[37] Wu, T.Y., Lin, S.F. (2013). A method for extracting suspected parotid lesions in CT images using feature-based segmentation and active contours based on stationary wavelet transform. Measurement Science Review, 13 (5), 237-247.10.2478/msr-2013-0036Search in Google Scholar

[38] Pan, B., Xie, H., Wang, Z., Qian, K., Wang, Z. (2008). Study on subset size selection in digital image correlation for speckle patterns. Optics Express, 16 (10), 7037-7048.10.1364/OE.16.00703718545407Search in Google Scholar

[39] Lava, P., Cooreman, S., Coppieters, S., De Strycker, M., Debruyne, D. (2009). Assessment of measuring errors in DIC using deformation fields generated by plastic FEA. Optics & Lasers in Engineering, 47 (7-8), 747-753.10.1016/j.optlaseng.2009.03.007Search in Google Scholar

eISSN:
1335-8871
Language:
English
Publication timeframe:
6 times per year
Journal Subjects:
Engineering, Electrical Engineering, Control Engineering, Metrology and Testing