Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
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Development of a Camera Self-calibration Method for 10-parameter Mapping Function

  • Received : 2021.01.12
  • Accepted : 2021.05.12
  • Published : 2021.06.30
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Abstract

Tomographic particle image velocimetry (PIV) is a widely used method that measures a three-dimensional (3D) flow field by reconstructing camera images into voxel images. In 3D measurements, the setting and calibration of the camera's mapping function significantly impact the obtained results. In this study, a camera self-calibration technique is applied to tomographic PIV to reduce the occurrence of errors arising from such functions. The measured 3D particles are superimposed on the image to create a disparity map. Camera self-calibration is performed by reflecting the error of the disparity map to the center value of the particles. Vortex ring synthetic images are generated and the developed algorithm is applied. The optimal result is obtained by applying self-calibration once when the center error is less than 1 pixel and by applying self-calibration 2-3 times when it was more than 1 pixel; the maximum recovery ratio is 96%. Further self-correlation did not improve the results. The algorithm is evaluated by performing an actual rotational flow experiment, and the optimal result was obtained when self-calibration was applied once, as shown in the virtual image result. Therefore, the developed algorithm is expected to be utilized for the performance improvement of 3D flow measurements.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea Government (No. 2018R1A2B6009387, 2021R1I1A1A01054535).

References

  1. Arroy, M.P., & Greated, C.A. (1991). Stereoscopic Particle Image Velocimetry. Measurement Science and Technology, 2(12), 1181-1186. https://doi.org/10.1088/0957-0233/2/12/0122
  2. Andersen, A.H., & Kak, A.C. (1984). Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the ART Algorithm. Ultrason Imaging, 6, 81-94. https://doi.org/10.1016/0161-7346(84)90008-7
  3. Atkinson, C., & Soria, J. (2009). An Efficient Simultaneous Reconstruction Technique for Tomographic Particle Image Velocimetry. Experiments in Fluids, 47, 553. https://doi.org/10.1007/s00348-009-0728-0
  4. Byrne, C.L. (1993). Iterative Image Reconstruction Algorithms Based on Cross-entropy Minimization. IEEE Transactions on Image Processing, 2(1), 96-103. https://doi.org/10.1109/83.210869
  5. Doh, D.H., Lee, C.J., Cho, G.R., & Moon, K.R. (2012a). Performances of Volume-PTV and Tomo-PIV. Open Journal of Fluid Dynamics, 2, 368-374. https://doi.org/10.4236/OJFD.2012.24A047
  6. Doh, D.H., Cho, G.R., & Kim, Y.H. (2012b). Development of a Tomographic PTV. Journal of Mechanical Science and Technology, 26, 3811-3819. https://doi.org/10.1007/s12206-012-1007-1
  7. Elsinga, G., Scarano, F., Wieneke, B., & van Oudheusden, B.W. (2006). Tomographic particle image velocimetry. Experiments in Fluids, 41, 933-947. https://doi.org/10.1007/s00348-006-0212-z
  8. Herman, G.T., & Lent, A. (1976). Iterative Reconstruction Algorithms. Computers in Biology and Medicine, 6(4), 273-294. https://doi.org/10.1016/0010-4825(76)90066-4
  9. Hinsch, K.D. (2002). Holographic Particle Image Velocimetry. Measurement Science and Technology, 13(7), R61-R72. https://doi.org/10.1088/0957-0233/13/7/201
  10. Hong, J.W., Jeong, S.W., & Ahn B.K. (2019). PIV Measurements of Non-caviating Flow in Wake of Two-dimensional Wedge-shaped Submerged Body. Journal of Ocean Engineering and Technology, 33(1), 26-32. https://doi.org/10.26748/KSOE.2018.066
  11. Jo, H.J., Lee, E.J., & Doh, D.H. (2009). A Study on Flow Structure of Breaking Wave through PIV Analysis. Journal of Ocean Engineering and Technology, 23(1), 43-47. https://www.joet.org/journal/view.php?number=2089
  12. Lynch, K.P., & Scarano, F. (2014). Experimental Determination of Tomographic PIV Accuracy by a 12-camera System. Measurement Science and Technology, 25(8),1-10. https://doi.org/10.1088/0957-0233/25/8/084003
  13. Prasad, A. (2000). Stereoscopic Particle Image Velocimetry. Experiments in Fluids, 29, 103-116. https://doi.org/10.1007/s003480000143
  14. Scarano, F. (2013). Tomographic PIV: Principles and Practice. Measurement Science and Technology, 24(1), 012001. https://doi.org/10.1088/0957-0233/24/1/012001
  15. Soloff, S.M., Adrian, R.J., & Liu, Z.C. (1997). Distortion Compensation for Generalized Stereoscopic Particle Image Veclocimetry. Measurement Science and Technology, 8(12), 1441-1454. https://doi.org/10.1088/0957-0233/8/12/008
  16. Wieneke, B. (2008). Volume Self-calibration for 3D Particle Image Velocimetry. Experiments in Fluids, 45, 549-556. https://doi.org/10.1007/s00348-008-0521-5
  17. Worth N.A., Nickels T.B., & Swaminathan N. (2010). A Tomographic PIV Resolution Study Based on Homogeneous Isotropic Turbulence DNS Data. Experiments in Fluids, 49, 637-656. https://doi.org/10.1007/s00348-010-0840-1