Advertisement

Experiments in Fluids

, Volume 46, Issue 3, pp 467–476 | Cite as

An evaluation of optical flow algorithms for background oriented schlieren imaging

  • Bradley Atcheson
  • Wolfgang Heidrich
  • Ivo Ihrke
Research Article

Abstract

The background oriented schlieren method (BOS) allows for accurate flow measurements with a simple experimental configuration. To estimate per-pixel displacement vectors between two images, BOS systems traditionally borrow window-based algorithms from particle image velocimetry. In this paper, we evaluate the performance of more recent optical flow methods in BOS settings. We also analyze the impact of different background patterns, suggesting the use of a pattern with detail at many scales. Experiments with both synthetic and real datasets show that the performance of BOS systems can be significantly improved through a combination of optical flow algorithms and multiscale background.

Keywords

Particle Imaging Velocimetry Optical Flow Noise Pattern Image Pyramid Background Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Adelson E, Anderson C, Bergen J, Burt P, Ogden J (1984) Pyramid methods in image processing. RCA Eng 29(6):33–41Google Scholar
  2. Anandan P (1989) A computational framework and an algorithm for the measurement of visual motion. IJCV 2(3):283–310CrossRefGoogle Scholar
  3. Atcheson B, Ihrke I, Heidrich W, Tevs A, Bradley D, Magnor M, Seidel HP (2008) Time-resolved 3D capture of non-stationary gas flows. ACM Trans Graph 27(5):132:1–132:9Google Scholar
  4. Barron J, Fleet D, Beauchemin S (1994) Performance of optical flow techniques. IJCV 12(1):43–77CrossRefGoogle Scholar
  5. Bridson R, Houriham J, Nordenstam M (2007) Curl-noise for procedural fluid flow. ACM Trans Graph 26(3):46:1–3CrossRefGoogle Scholar
  6. Brox T, Bruhn A, Papenberg N, Weickert J (2004) High accuracy optical flow estimation based on a theory for warping. In: Proceedings of the 8th ECCV, Prague, Czech Republic, pp 25–36Google Scholar
  7. Cabral B, Leedom LC (1993) Imaging vector fields using line integral convolution. In: SIGGRAPH ’93: Proceedings of the 20th annual conference on computer Graphics and Interactive Techniques, ACM, New York, pp 263–270Google Scholar
  8. Cook R, DeRose T (2005) Wavelet noise. ACM Trans Graph 24(3):803–811CrossRefGoogle Scholar
  9. Corpetti T, Heitz D, Arroyo G, Mémin E, Santa-Cruz A (2006) Fluid experimental flow estimation based on an optical-flow scheme. Exp Fluids 40(1):80–97CrossRefGoogle Scholar
  10. Davies E (2004) Machine vision: theory, algorithms, practicalities, chap 18.2. Morgan Kaufmann, San Francisco, pp 505–509Google Scholar
  11. Elsinga G, van Oudheusden B, Scarano F, Watt D (2004) Assessment and application of quantitative schlieren methods: calibrated color schlieren and background oriented schlieren. Exp Fluids 36(2):309–325CrossRefGoogle Scholar
  12. Goldhahn E, Seume J (2007) The background oriented schlieren technique: sensitivity, accuracy, resolution and application to a three-dimensional density field. Exp Fluids 43(2–3):241–249CrossRefGoogle Scholar
  13. Horn B, Schunck B (1981) Determining optical flow. Artif Intell 17:185–203CrossRefGoogle Scholar
  14. Jensen O, Kunsch J, Rösgen T (2005) Optical density and velocity measurements in cryogenic gas flows. Exp Fluids 39(1):48–55CrossRefGoogle Scholar
  15. Kindler K, Goldhahn E, Leopold F, Raffel M (2007) Recent developments in background oriented schlieren methods for rotor blade tip vortex measurements. Exp Fluids 43(2–3):233–240CrossRefGoogle Scholar
  16. Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th international joint conference on artificial intelligence, Vancouver, Canada, pp 674–679Google Scholar
  17. Meier G (2002) Computerized background-oriented schlieren. Exp Fluids 33(1):181–187Google Scholar
  18. Papenberg N, Bruhn A, Brox T, Didas S, Weickert J (2006) Highly accurate optic flow computation with theoretically justified warping. IJCV 67(2):141–158CrossRefGoogle Scholar
  19. Raffel M, Richard H, Meier G (2000) On the applicability of background oriented optical tomography for large scale aerodynamic investigations. Exp Fluids 28(5):477–481CrossRefGoogle Scholar
  20. Richard H, Raffel M (2001) Principle and applications of the background oriented schlieren (BOS) method. Meas Sci Technol 12(9):1576–1585CrossRefGoogle Scholar
  21. Ruhnau P, Schnörr C (2007) Optical stokes flow estimation: an imaging-based control approach. Exp Fluids 42(1):61–78CrossRefGoogle Scholar
  22. Ruhnau P, Kohlberger T, Schnörr C, Nobach H (2005) Variational optical flow estimation for particle image velocimetry. Exp Fluids 38(1):21–32CrossRefGoogle Scholar
  23. Scarano F (2002) Iterative image deformation methods in PIV. Meas Sci Tech 13(1):R1–R19CrossRefGoogle Scholar
  24. Scarano F, Riethmuller M (1999) Iterative multigrid approach in PIV processing with discrete window offset. Exp Fluids 26(6):513–523CrossRefGoogle Scholar
  25. Sveen J (2004) An introduction to MatPIV v.1.6.1. Eprint no. 2, ISSN 0809-4403, Department of Mathematics, University of Oslo, http://www.math.uio.no/~jks/matpiv
  26. Venkatakrishnan L, Meier G (2004) Density measurements using the background oriented schlieren technique. Exp Fluids 37(2):237–247CrossRefGoogle Scholar
  27. Westerweel J (1997) Fundamentals of digital particle image velocimetry. Meas Sci Tech 8(12):1379–1392CrossRefGoogle Scholar
  28. Westerweel J (2000) Theoretical analysis of the measurement precision in particle image velocimetry. Exp Fluids 29(7):S003–S012CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Bradley Atcheson
    • 1
  • Wolfgang Heidrich
    • 1
  • Ivo Ihrke
    • 1
  1. 1.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada

Personalised recommendations