Abstract
Through a combination of cross-correlation and optical flow method (OFM), a novel technique can benefit from the strengths of each method while mitigating the flaws each individual method contains. The hybrid Particle Image Velocimetry (PIV) method utilizes the state-of-the-art cross-correlation method to account for the relatively large displacements of particles and refine the flow field using the high-resolution analysis of OFM. Image processing techniques such as interpolation, image shifting, and Gaussian filtering are crucial for integrating the cross-correlation technique with optical flow analysis. The accuracy of the hybrid PIV method was validated using standard simulated PIV images that encompassed various parameters encountered in PIV measurements. Each set of images was analyzed by the hybrid method and three other widely used correlation techniques to verify the accuracy. Results confirmed that the hybrid method is consistently more accurate than the other methods in generating the flow vectors, especially near the boundaries. Additionally, for cases dealing with large-sized particles or small displacements, the hybrid PIV method also attains more accurate results.
Graphical Abstract
Similar content being viewed by others
References
Adrian RJ (1988) Statistical properties of particle image velocimetry measurements in turbulent flows. In: Adrian RJ et al (eds) Laser anemometry in fluid mechanics III. Springer, New York, pp 115–129
Adrian RJ (2005) Twenty years of particle image velocimetry. Exp Fluids 39:159–169
Adrian RJ, Westerweed J (2011) Particle image velocimetry. Cambridge University Press, Cambridge
Bastiaans RJ (2000) Cross-correlation PIV; theory, implementation and accuracy. Eindhoven University of Technology, Eindhoven
Bigun J, Granlund GH (1988) Optical flow based on the inertia matrix in the frequency domain. In: Proc. SSAB symposium on picture processing, Lund, Sweden
Billy F, David L, Pineau G (2004) Single pixel resolution correlation applied to unsteady flow measurements. Meas Sci Technol 15:1039–1045
Bruhn A, Weickert J, Schnorr C (2005) Lucas/Kanade Meets Horn/Schunck: combining local and global optic flow methods. Int J Comput Vis 61(3):211–231
Chen X, Zille P, Shao L, Corpetti T (2015) Optical flow for incompressible turbulence motion estimation. Exp Fluids 56(8):1–14
Corpetti T, Memin E, Perez P (2002) Dense estimation of fluid flows. IEEE Trans Pattern Anal Mach Intell 24:365–380
Corpetti T, Heitz D, Arroyo G, Mèmin E, Santa-Cruz A (2006) Fluid experimental flow estimation based on an optical-flow scheme. Exp Fluids 40:80–97
Hart DP (1999) Super-resolution PIV by recursive local-correlation. J Visual Jpn 10:1–10
Hèas P, Mèmin E, Papadakis N, Szantai A (2007) Layered estimation of atmospheric mesoscale dynamics from satellite imagery. IEEE T Geosci Remote 45(12):4087–4104
Heitz D, Mèmin E, Schnörr C (2010) Variational fluid flow measurements form image sequences: synopsis and perspectives. Exp Fluids 48:369–393
Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17:185–203
Hu H, Saga T, Kobayashi T, Okamoto K, Taniguchi N (1998) Evaluation of the Cross correlation method by using PIV standard images. J Visual Jpn 1(1):87–94
Liu T, Shen L (2008) Fluid flow and optical flow. J Fluid Mech 614:253–291
Liu T, Merat A, Makhmalbaf M, Fajardo C, Merati P (2015) Comparison between optical flow and cross-correlation methods for extraction of velocity fields from particle images. Exp Fluids 56(8):166
Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proc. seventh international joint conference on artificial intelligence, Vancouver, Canada, pp 674–679
Okamoto K, Nishio S, Saga T, Kobayashi T (2000) Standard images for particle-image velocimetry. Meas Sci Technol 11:685–691
Quènot GM, Pakleza J, Kowalewski TA (1998) Particle image velocimetry with optical flow. Exp Fluids 25:177–189
Raffel M, Willert CE, Wereley ST, Kompenhans J (2007) Particle image velocimetry: a practical guide, chapters 3–5, Springer, New York
Ruhnau P, Kohlberger T, Schnorr C, Nobach H (2005) Variational optical flow estimation for particle image velocimetry. Exp Fluids 38:21–32
Scarano F, Riethmuller ML (1999) Iterative multigrid approach in PIV image processing with discrete window offset. Exp Fluids 26(6):513–523
Scarano F, Riethmuller ML (2000) Advances in iterative multigrid PIV image processing. Exp Fluids 29(1):51–60
Stanislas M, Okamoto K, Kähler C (2003) Main results of the first international PIV challenge. Meas Sci Technol 14:R63–R89
Stanislas M, Okamoto K, Kähler C, Westerweel J (2005) Main results of the second international PIV challenge. Exp Fluids 39:170–191
Stanislas M, Okamoto K, Kähler C, Westerweel J, Scarano F (2008) Main results of the third international PIV challenge. Exp Fluids 45:27–71
Wang B, Cai Z, Shen L, Liu T (2014) An analysis of physics-based optical flow. J Comput Appl Math 276:62–80
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, Z., Johnson, M. Hybrid particle image velocimetry with the combination of cross-correlation and optical flow method. J Vis 20, 625–638 (2017). https://doi.org/10.1007/s12650-017-0417-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12650-017-0417-7