Experiments in Fluids

, Volume 53, Issue 5, pp 1251–1268

A vision-based hybrid particle tracking velocimetry (PTV) technique using a modified cascade correlation peak-finding method

  • Y.-C. Lei
  • W.-H. Tien
  • J. Duncan
  • M. Paul
  • N. Ponchaut
  • C. Mouton
  • D. Dabiri
  • T. Rösgen
  • J. Hove
Research Article
  • 611 Downloads

Abstract

A novel technique for particle tracking velocimetry is presented in this paper to overcome the issue of overlapping particle images encountered in the flows with high particle density or under volumetric illumination conditions. To achieve this goal, algorithms for particle identification and tracking are developed based on current methods and validated with both synthetic and experimental image sets. The results from synthetic image tests show that the particle identification algorithm is able to resolve overlapped particle images up to 50 % under noisy conditions, while keeping the root mean square peak location error under 0.07 pixels. The algorithm is also robust to the size changes up to a size ratio of 5. The tracking method developed from a classic computer vision matching algorithm is capable of capturing a velocity gradient up to 0.3 while maintaining the error under 0.2 pixels. Sensitivity tests were performed to describe the optimum conditions for the technique in terms of particle image density, particle image sizes and velocity gradients, also its sensitivity to errors of the PIV results that guide the tracking process. The comparison with other existing tracking techniques demonstrates that this technique is able to resolve more vectors out of a dense particle image field.

References

  1. Adrian RJ, Yao CS (1985) Pulsed laser technique application to liquid and gaseous flows and the scattering power of seed materials. Appl Opt 24:44–52CrossRefGoogle Scholar
  2. Agui JC, Jimenez J (1987) On the performance of particle tracking. J Fluid Mech 185:447–468CrossRefGoogle Scholar
  3. Angarita-Jaimes NC, Roca MG, Towers CE, Read ND, Towers DP (2009) Algorithms for the automated analysis of cellular dynamics within living fungal colonies. Cytom A 75(9):768–780CrossRefGoogle Scholar
  4. Baek SJ, Lee SJ (1996) A new two-frame particle tracking algorithm using match probability. Exp Fluids 22(1):23–32CrossRefGoogle Scholar
  5. Brady MR, Raben G, Vlachos PP (2009) Methods for Digital Particle Image Sizing (DPIS): Comparisons and improvements. Flow Meas Instrum 20(6):207–219Google Scholar
  6. Brevis W, Nino Y, Jirka GH (2011) Integrating cross-correlation and relaxation algorithms for particle tracking velocimetry. Exp Fluids 50(1):135–147 Google Scholar
  7. Cowen EA, Monismith SG (1997) A hybrid digital particle tracking velocimetry technique. Exp Fluids 22(3):199–211CrossRefGoogle Scholar
  8. Dabiri D (2003) On the interaction of a vertical shear layer with a free surface. J Fluid Mech 480:217–232Google Scholar
  9. Duncan J, Dabiri D, Hove J, Gharib M (2010) Universal outlier detection for particle image velocimetry (PIV) and particle tracking velocimetry (PTV) data. Meas Sci Technol 21(5):057002CrossRefGoogle Scholar
  10. Elghobashi S (1994) On predicting particle-laden turbulent flows. Appl Sci Res 52(4):309–329CrossRefGoogle Scholar
  11. Gharib M, Kremers D, Koochesfahani MM, Kemp M (2002) Leonardo’s vision of flow visualization. Exp Fluids 33(1):219–223Google Scholar
  12. Gunes H, Sirisup S, Karniadakis GE (2006) Gappy data: to Krig or not to Krig? J Comput Phys 212:358–382MATHCrossRefGoogle Scholar
  13. Keane RD, Adrian RJ, Zhang Y (1995) Super-resolution particle imaging velocimetry. Meas Sci Technol 6(6):754–768CrossRefGoogle Scholar
  14. Kim HB, Lee SJ (2002) Performance improvement of two-frame particle tracking velocimetry using a hybrid adaptive scheme. Meas Sci Technol 13(4):573–582CrossRefGoogle Scholar
  15. Liao Q, Cowen EA (2005) An efficient anti-aliasing spectral continuous window shifting technique for PIV. Exp Fluids 38(2):197–208CrossRefGoogle Scholar
  16. Luo B, Hancock ER (2002) Iterative procrustes alignment with the EM algorithm. Image Vis Comput 20:367–369CrossRefGoogle Scholar
  17. Marxen M, Sullivan PE, Loewen MR, Jahne B (2000) Comparison of Gaussian particle center estimators and the achievable measurement density for particle tracking velocimetry. Exp Fluids 29(2):145–153CrossRefGoogle Scholar
  18. Mikheev AV, Zubtsov VM (2008) Enhanced particle-tracking velocimetry (EPTV) with a combined two-component pair-matching algorithm. Meas Sci Technol 19(8):085401CrossRefGoogle Scholar
  19. Nogueira J, Lecuona A, Rodriguez PA (2005) Limits on the resolution of correlation PIV iterative methods. Fundamentals. Exp Fluids 39(2):305–313Google Scholar
  20. Nogueira J, Lecuona A, Rodriguez PA (2001a) Identification of a new source of peak locking, analysis and its removal in conventional and super-resolution PIV techniques. Exp Fluids 30(3):309–316CrossRefGoogle Scholar
  21. Nogueira J, Lecuona A, Rodríguez PA (2001b) Local field correction PIV, implemented by means of simple algorithms, and multigrid versions. Meas Sci Technol 12(11):1911–1921CrossRefGoogle Scholar
  22. Ohmi K, Li HY (2000) Particle-tracking velocimetry with new algorithms. Meas Sci Technol 11(6):603–616CrossRefGoogle Scholar
  23. Okamoto K, Nishio S, Saga T, Kobayashi T (2000) Standard images for particle–image velocimetry. Meas Sci Technol 11(6):685–691CrossRefGoogle Scholar
  24. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66CrossRefGoogle Scholar
  25. Panday SP, Ohmi K, Nose K (2011) An ant colony optimization based stereoscopic particle pairing algorithm for three-dimensional particle tracking velocimetry. Flow Meas Instrum 22(1):86–95CrossRefGoogle Scholar
  26. Pilu M (1997) A direct method for stereo correspondence based on singular value decomposition. In: IEEE computer vision and pattern recognition conference, San Juan, Puerto Rico, pp 261–266Google Scholar
  27. Ponchaut N (2005) Part I: 3DPTV—advances and error analysis; part II: extension of Guderley’s solution for converging shock waves. PhD thesis, California Institute of TechnologyGoogle Scholar
  28. Ponchaut N, Mouton C (2005). 3-D particle tracking velocimetry method: advance and error analysis. GALCIT report FM2005.004Google Scholar
  29. Raffel M, Willert C, Kompenhans J (1998) Particle image velocimetry: a practical guide. Springer, BerlinGoogle Scholar
  30. Ruhnau P, Guetter C, Putze T, Schnorr C (2005) A variational approach for particle tracking velocimetry. Meas Sci Technol 16(7):1449–1458CrossRefGoogle Scholar
  31. Saga T, Kobayashi T, Segawa S (2003) Development and evaluation of an improved correlation based PTV method. In: 6th international symposium on fluid control, measurement and visualization, Sherbrooke, CanadaGoogle Scholar
  32. Scarano F (2003) Theory of non-isotropic spatial resolution in PIV. Exp Fluids 35(3):268–277CrossRefGoogle Scholar
  33. Schonemann PH (1966) A generalized solution of the orthogonal procrustes problem. Psychometrika 31:1–10MathSciNetCrossRefGoogle Scholar
  34. Scott G, Longuet-Higgins H (1991) An algorithm for associating the features of two images. Biol Sci 244:21–26CrossRefGoogle Scholar
  35. Shindler L, Moroni M, Cenedese A (2011) Spatial–temporal improvements of a two-frame particle-tracking algorithm. Meas Sci Technol 21(11):115401Google Scholar
  36. Song X, Yamamoto F, Iguchi M (1999) A new tracking algorithm and removal of spurious vectors using Delaunay tesselation. Exp Fluids 26(4):371–380CrossRefGoogle Scholar
  37. Stellmacher M, Obermayer K (2000) A new particle tracking algorithm based on deterministic annealing and alternative distance measures. Exp Fluids 28(6):506–518CrossRefGoogle Scholar
  38. Takehara K, Adrian RJ, Etoh GT (2000) A Kalman tracker for super-resolution PIV. Exp Fluids 29(7):s034–s041CrossRefGoogle Scholar
  39. Takehara K, Etoh T (1999) A study on particle identification in PTV- particle mask correlation method. J Vis 1(3):313–323Google Scholar
  40. Uemura T, Yamamoto F, Ohmi K (1989) A high speed algorithm of image analysis for real time measurement of two-dimensional velocity distribution. ASME FED 85:129–134Google Scholar
  41. Ullman S (1979) The interpretation of visual motion. MIT Press, CambridgeGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Y.-C. Lei
    • 1
    • 8
  • W.-H. Tien
    • 1
  • J. Duncan
    • 1
    • 7
  • M. Paul
    • 1
    • 6
  • N. Ponchaut
    • 2
  • C. Mouton
    • 3
  • D. Dabiri
    • 1
  • T. Rösgen
    • 4
  • J. Hove
    • 5
  1. 1.Department of Aeronautics and AstronauticsUniversity of WashingtonSeattleUSA
  2. 2.ExponentNatickUSA
  3. 3.RAND CorporationSanta MonicaUSA
  4. 4.Institute of Fluid DynamicsETH ZurichZurichSwitzerland
  5. 5.Molecular and Cellular PhysiologyUniversity of Cincinnati College of MedicineCincinnatiUSA
  6. 6.Eglin Air Force BaseUSA
  7. 7.Edwards Air Force BaseUSA
  8. 8.Performance Group DivisionCanadian Aviation Electronics Inc.QuebecCanada

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