Skip to main content
SpringerLink
Log in

A field-segmentation-based variational optical flow method for PIV measurements of nonuniform flows

  • Research Article
  • Published:
Experiments in Fluids Aims and scope Submit manuscript

Abstract

Air flow fields have nonuniform velocity distributions because of the compressibility of air. Current optical flow-based particle image velocimetry (PIV) methods cannot thoroughly solve these distributions because of the global optical flow formulation. In this study, we propose a novel field-segmentation-based variational optical flow (FS-VOF) method for preserving the spatially discontinuous characteristics of nonuniform flow fields. The proposed method is designed based on the level-set segmentation method, in which the particle image is segmented according to the velocity distribution of the fluid flow. Subsequently, a segmented smoothness constraint term built from the assumption that velocity varies continuously in each segmented particle image region and the discontinuous structures of the velocity field are preserved at the boundaries of the segmented regions. In addition, the data term of the proposed method is based on the segmented regions and derived from the physics-based optical flow equation. The proposed method is evaluated over synthetic and experimental images, and the comparison with advanced variational optical flow algorithms demonstrates the effectiveness of our method in preserving the spatially discontinuous characteristics of nonuniform flow fields.

Graphic abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Adrian RJ (1997) Dynamic ranges of velocity and spatial resolution of particle image velocimetry. Meas Sci Technol 8(12):1393

    Article  Google Scholar 

  • Adrian RJ, Westerweel J (2011) Particle image velocimetry. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Astarita T (2009) Adaptive space resolution for PIV. Exp Fluids 46(6):1115

    Article  Google Scholar 

  • Becker F, Wieneke B, Petra S, Schröder A, Schnörr C (2012) Variational adaptive correlation method for flow estimation. IEEE Trans Image Process 21(6):3053–3065

    Article  MathSciNet  Google Scholar 

  • Becker F, Petra S, Schnörr C (2015) Optical flow. In: Scherzer O (ed) Handbook of mathematical methods in imaging, 2nd edn. Springer, Berlin, pp 1945–2004

    Chapter  Google Scholar 

  • Bruhn A, Weickert J, Schnörr C (2005) Lucas/kanade meets horn/schunck: combining local and global optic flow methods. Int J Comput Vis 61(3):211–231

    Article  Google Scholar 

  • Bruhn A, Weickert J, Kohlberger T, Schnörr C (2006) A multigrid platform for real-time motion computation with discontinuity-preserving variational methods. Int J Comput Vis 70(3):257–277

    Article  Google Scholar 

  • Carlier J, Wieneke B (2005) Report 1 on production and diffusion of fluid mechanics images and data. fluid project deliverable 1.2. European ProjectFluid image analisys and description(FLUID)–http://www.fluid.irisa.fr, p 47

  • Cavazzini G (2012) The particle image velocimetry: characteristics limits and possibile applications. InTech, London

    Book  Google Scholar 

  • Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  Google Scholar 

  • Chen F, Liu H, Rong Z (2012) Development and application of nanoparticle tracers for piv in supersonic and hypersonic flows. In: 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, p 36

  • Chen X, Zillé P, Shao L, Corpetti T (2015) Optical flow for incompressible turbulence motion estimation. Exp Fluids 56(1):8

    Article  Google Scholar 

  • Corpetti T, Mémin E, Pérez P (2000) Estimating fluid optical flow. In: Pattern Recognition, 2000. Proceedings. 15th International Conference on, IEEE, vol 3, pp 1033–1036

  • Corpetti T, Mémin É, Perez P (2002) Dense estimation of fluid flows. IEEE Trans Pattern Anal Mach Intell 24(3):365–380

    Article  Google Scholar 

  • Corpetti T, Heitz D, Arroyo G, Memin E, Santa-Cruz A (2006) Fluid experimental flow estimation based on an optical-flow scheme. Exp Fluids 40(1):80–97

    Article  Google Scholar 

  • Cremers D, Rousson M, Deriche R (2007) A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int J Comput Vis 72(2):195–215

    Article  Google Scholar 

  • Faisal M, Barron J (2007) High accuracy optical flow method based on a theory for warping: Implementation and qualitative/quantitative evaluation. In: International Conference Image Analysis and Recognition, Springer, pp 513–525

  • Heitz D, Héas P, Mémin E, Carlier J (2008) Dynamic consistent correlation-variational approach for robust optical flow estimation. Exp Fluids 45(4):595–608

    Article  Google Scholar 

  • Heitz D, Mémin E, Schnörr C (2010) Variational fluid flow measurements from image sequences: synopsis and perspectives. Exp Fluids 48(3):369–393

    Article  Google Scholar 

  • Horn BK, Schunck BG (1981) Determining optical flow. Artif Intell 17(1–3):185–203

    Article  Google Scholar 

  • Kähler CJ, Scharnowski S, Cierpka C (2012) On the resolution limit of digital particle image velocimetry. Exp Fluids 52(6):1629–1639

    Article  Google Scholar 

  • Li C, Kao CY, Gore JC, Ding Z (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10):1940–1949

    Article  MathSciNet  Google Scholar 

  • Liu T (2017) Openopticalflow: an open source program for extraction of velocity fields from flow visualization images. J Open Res Softw 5:1

    Article  Google Scholar 

  • Liu T, Shen L (2008) Fluid flow and optical flow. J Fluid Mech 614:253–291

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • Okamoto K, Nishio S, Saga T, Kobayashi T (2000) Standard images for particle-image velocimetry. Meas Sci Technol 11(6):685

    Article  Google Scholar 

  • Papenberg N, Bruhn A, Brox T, Didas S, Weickert J (2006) Highly accurate optic flow computation with theoretically justified warping. Int J Comput Vis 67(2):141–158

    Article  Google Scholar 

  • Quénot GM (2000) Performance evaluation of an optical flow technique applied to particle image velocimetry using the vsj standard images. J Visual 3(2):125–133

    Article  Google Scholar 

  • Quénot GM, Pakleza J, Kowalewski TA (1998) Particle image velocimetry with optical flow. Exp Fluids 25(3):177–189

    Article  Google Scholar 

  • Raffel M, Willert CE, Scarano F, Kähler CJ, Wereley ST, Kompenhans J (2018) Particle image velocimetry: a practical guide. Springer, Berlin

    Book  Google Scholar 

  • Ruhnau P, Kohlberger T, Schnörr C, Nobach H (2005) Variational optical flow estimation for particle image velocimetry. Exp Fluids 38(1):21–32

    Article  Google Scholar 

  • Scarano F (2001) Iterative image deformation methods in PIV. Meas Sci Technol 13(1):R1

    Article  Google Scholar 

  • Seerha GK, Kaur R (2013) Review on recent image segmentation techniques. Int J Comput Sci Eng 5(2):109

    Google Scholar 

  • Theunissen R, Scarano F, Riethmuller M (2006) An adaptive sampling and windowing interrogation method in PIV. Meas Sci Technol 18(1):275

    Article  Google Scholar 

  • Theunissen R, Scarano F, Riethmuller ML (2010) Spatially adaptive PIV interrogation based on data ensemble. Exp Fluids 48(5):875–887

    Article  Google Scholar 

  • Vese LA, Chan TF (2002) A multiphase level set framework for image segmentation using the mumford and shah model. Int J Comput Vis 50(3):271–293

    Article  Google Scholar 

  • Wang L, He L, Mishra A, Li C (2009) Active contours driven by local gaussian distribution fitting energy. Signal Process 89(12):2435–2447

    Article  Google Scholar 

  • Yang SQ, Chow AT (2008) Turbulence structures in non-uniform flows. Adv Water Res 31(10):1344–1351

    Article  Google Scholar 

  • Zhong Q, Yang H, Yin Z (2017) An optical flow algorithm based on gradient constancy assumption for PIV image processing. Meas Sci Technol 28(5):055,208

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China under Grant nos 51875228, 51475193, and 51327801.

Author information

Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jin Lu, Hua Yang & Zhouping Yin

  2. Hypervelocity Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China

    Qinghu Zhang

Authors
  1. Jin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hua Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qinghu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhouping Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hua Yang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, J., Yang, H., Zhang, Q. et al. A field-segmentation-based variational optical flow method for PIV measurements of nonuniform flows. Exp Fluids 60, 142 (2019). https://doi.org/10.1007/s00348-019-2787-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00348-019-2787-1

Search

Navigation