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Abdominal-Deformation Measurement for a Shape-Flexible Mannequin Using the 3D Digital Image Correlation

  • Liu, Huan (Engineering Research Center of Digital Textile & Apparel Technology, Ministry of Education, College of Information Science and Technology, Donghua University) ;
  • Hao, Kuangrong (Engineering Research Center of Digital Textile & Apparel Technology, Ministry of Education, College of Information Science and Technology, Donghua University) ;
  • Ding, Yongsheng (Engineering Research Center of Digital Textile & Apparel Technology, Ministry of Education, College of Information Science and Technology, Donghua University)
  • Received : 2017.01.18
  • Accepted : 2017.09.08
  • Published : 2017.09.30

Abstract

In this paper, the abdominal-deformation measurement scheme is conducted on a shape-flexible mannequin using the DIC technique in a stereo-vision system. Firstly, during the integer-pixel displacement search, a novel fractal dimension based on an adaptive-ellipse subset area is developed to track an integer pixel between the reference and deformed images. Secondly, at the subpixel registration, a new mutual-learning adaptive particle swarm optimization (MLADPSO) algorithm is employed to locate the subpixel precisely. Dynamic adjustments of the particle flight velocities that are according to the deformation extent of each interest point are utilized for enhancing the accuracy of the subpixel registration. A test is performed on the abdominal-deformation measurement of the shape-flexible mannequin. The experiment results indicate that under the guarantee of its measurement accuracy without the cause of any loss, the time-consumption of the proposed scheme is significantly more efficient than that of the conventional method, particularly in the case of a large number of interest points.

Keywords

References

  1. H. Liu, K. R. Hao, and Y. S. Ding, "New anti-blur and illumination-robust combined invariant for stereo vision in human belly reconstruction," The Imaging Science Journal, vol. 62, no. 5, pp. 251-264, 2014. https://doi.org/10.1179/1743131X13Y.0000000061
  2. H. Liu, Y. Xiao, W. D. Tang, and Y. H. Zhou, "Illuminationrobust and anti-blur feature descriptors for image matching in abdomen reconstruction," International Journal of Automation and Computing, vol. 11, no. 5, pp. 469-479, 2014. https://doi.org/10.1007/s11633-014-0829-y
  3. J. Park, S. Yoon, T. H. Kwon, and K. Park, "Assessment of speckle-pattern quality in digital image correlation based on gray intensity and speckle morphology," Optics and Lasers in Engineering, vol. 91, no. 1, pp. 62-72, 2016.
  4. G. F. Bomarito, J. D. Hochhalter, T. J. Ruggles, and A. H. Cannon, "Increasing accuracy and precision of digital image correlation through pattern optimization," Optics and Lasers in Engineering, vol. 91, pp. 73-85, 2017. https://doi.org/10.1016/j.optlaseng.2016.11.005
  5. W. H. Peter and W. F. Ranson, "Digital imaging techniques in experimental stress analysis," Optical Engineering, vol. 21, no. 3, pp. 427-431, 1982.
  6. M. C. Weng, S. H. Tung, and M. H. Shih, "Microscopic characteristics of problematic tertiary sandstone as revealed by grain-wide local deformation," International Journal of Rock Mechanics and Mining Science, vol. 46, no. 7, pp. 1243-1251, 2009. https://doi.org/10.1016/j.ijrmms.2009.04.005
  7. Z. G. Tang, J. Liang, Z. Z. Xiao, and C. Guo, "Large deformation measurement scheme for 3D digital image correlation method," Optics and Lasers in Engineering, vol. 50, no. 2, pp. 122-130, 2012. https://doi.org/10.1016/j.optlaseng.2011.09.018
  8. B. Pan, D. F. Wu, H. M. Xie, and Z. X. Hu, "Spatialgradient-based digital volume correlation technique for internal deformation measurement," Acta Optica Sinica, vol. 31, no. 6, article ID. 0612005, 2011.
  9. B. Pan and B. Wang, "Digital image correlation with enhanced accuracy and efficiency: a comparison of two subpixel registration algorithms," Experimental Mechanics, vol. 56, no. 8, pp. 1395-1409, 2016. https://doi.org/10.1007/s11340-016-0180-z
  10. M. Kimura, M. Mochimaru, and T. Kanade, "3D measurement of feature cross-sections of foot while walking," Machine Vision and Applications, vol. 22, no. 2, pp. 377-388, 2011. https://doi.org/10.1007/s00138-009-0238-3
  11. M. Bornert, F. Bremand, P. Doumalin, J. C. Dupre, M. Fazzini, M. Grediac, et al., "Assessment of digital image correlation measurement errors: methodology and results," Experimental Mechanics, vol. 49, no. 3, pp. 353-370, 2009. https://doi.org/10.1007/s11340-008-9204-7
  12. H. W. Schreier and M. A. Sutton, "Systematic errors in digital image correlation due to undermatched subset shape functions," Experimental Mechanics, vol. 42, no. 3, pp. 303-310, 2002. https://doi.org/10.1007/BF02410987
  13. B. Pan, H. M. Xie, Z. Y. Wang, K. M. Qian, and Z. Wang, "Study on subset size selection in digital image correlation for speckle patterns," Optics Express, vol. 16, no. 10, pp. 7037-7048, 2008. https://doi.org/10.1364/OE.16.007037
  14. B. Pan, H. M. Xie, B. Q. Xu, and F. L. Dai, "Performance of sub-pixel registration algorithms in digital image correlation," Measurement Science and Technology, vol. 17, no. 6, pp. 1615-1621, 2006. https://doi.org/10.1088/0957-0233/17/6/045
  15. B. Pan, A. Asundi, H. M. Xie, and J. X. Gao, "Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements," Optics and Lasers in Engineering, vol. 47, no. 7-8, pp. 865-874, 2009. https://doi.org/10.1016/j.optlaseng.2008.10.014
  16. B. Pan, K. M. Qian, H. M. Xie, and A. Asundi, "Twodimensional digital image correlation for in-plane displacement and strain measurement: a review," Measurement Science and Technology, vol. 20, no. 6, article ID. 062001, 2009.
  17. Q. Li, G. Zhou, and T. Xiao, "Research on high accuracy registration of dual energy CT images in synchrotron radiation," Acta Optica Sinica, vol. 36, no. 4, pp. 116-123, 2016.
  18. J. Q. Zhao, P. Zeng, L. P. Lei, and Y. Ma, "Initial guess by improved population-based intelligent algorithm for large inter-frame deformation measurement using digital image correlation", Optics and Lasers in Engineering, vol. 50, no. 3, pp. 473-490, 2012. https://doi.org/10.1016/j.optlaseng.2011.10.005
  19. W. Gao, C. Shao, and Q. Gao, "An optimization algorithm with novel flexible grid: applications to parameter decision in LS-SVM," Journal of Computing Science and Engineering, vol. 9, no. 2, pp. 39-50, 2015. https://doi.org/10.5626/JCSE.2015.9.2.39
  20. Z. F. Zhang, Y. L. Kang, H. W. Wang, Q. H. Qin, Y. Qin, and X. Q. Li, "A novel coarse-fine search scheme for digital image correlation method," Measurement, vol. 39, no. 8, pp. 710-718, 2006. https://doi.org/10.1016/j.measurement.2006.03.008
  21. A. R. Backes and O. M. Bruno, "Texture analysis using volume-radius fractal dimension," Applied Mathematics and Computation, vol. 219, no. 11, pp. 5870-5875, 2013. https://doi.org/10.1016/j.amc.2012.11.092
  22. Z. Shen, X. Chen, X. Tang, and H. Zhang, "Road damage feature extraction in image based on fractal dimension," Applied Mechanics and Materials, vol. 256-259, no. 1, pp. 2971-2975, 2013.
  23. W. K. Chen, J. C. Lee, W. Y. Han, C. K. Shih, and K. C. Chang, "Iris recognition based on bidimensional empirical mode decomposition and fractal dimension," Information Sciences, vol. 221, pp. 439-451, 2013. https://doi.org/10.1016/j.ins.2012.09.021
  24. J. B. Florindo and O. M. Bruno, "Local fractal dimension and binary pattern in texture recognition," Pattern Recognition Letters, vol. 78, no. 15, pp. 22-27, 2016. https://doi.org/10.1016/j.patrec.2016.03.025
  25. X. Wang, N. D. Georganas, and E. M. Petriu, "Fabric texture analysis using computer vision techniques," IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 1, pp. 44-56, 2011. https://doi.org/10.1109/TIM.2010.2069850
  26. A. D. C. Kamila, A. Juliana, A. P. Thais, and R. D. O. H. Luis, "3-D reconstruction by extended depth-of-field in failure analysis. Case study II: Fractal analysis of interlaminar fracture in carbon/epoxy composites," Engineering Failure Analysis, vo. 25, no. 1, pp. 271-279, 2012. https://doi.org/10.1016/j.engfailanal.2012.05.015
  27. B. Pan and K. A. Li, "A fast digital image correlation method for deformation measurement," Optics and Lasers in Engineering, vol. 49, no. 7, pp. 841-847, 2011. https://doi.org/10.1016/j.optlaseng.2011.02.023
  28. Y. F. Hu, "Research on a three-dimensional reconstruction method based on the feature matching algorithm of a scaleinvariant feature transform," Mathematical and Computer Modelling, vol. 54, no. 3-4, pp. 919-923, 2011. https://doi.org/10.1016/j.mcm.2010.11.016
  29. R. Cheng, Y. Zhao, Z. Li, W. Jiang, X. A. Wang, and Y. Xu, "Panorama parking assistant system with improved particle swarm optimization method," Journal of Electronic Imaging, vol. 22, no. 4, pp. 451-459, 2013.
  30. L. C. Geng, S. Z. Su, and D. B. Cao, "Perspective-invariant image matching framework with binary feature descriptor and APSO," International Journal of Pattern Recognition and Artificial Intelligence, vol. 28, no. 8, article ID. 1455011, 2014.

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