Skip to main content
Log in

An Algorithm for Compensating the Effect of Deformations When Using the Shadow Background Method

  • Published:
Measurement Techniques Aims and scope

An algorithm for compensating image distortions under the influence of the surface deformation of the background screen when using the shadow background method. The efficiency of the algorithm is confirmed. We experimentally determined the optimal marker to be used for searching on the image. We examined the capabilities of the algorithm in compensating for the shift and rotation of the surface of the background screen with an mean square deviation of not more than 0.43 pixels, determined by cross-correlation processing.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.

Similar content being viewed by others

References

  1. G. E. A. Meier, “Computerized background-oriented schlieren,” Experiments in Fluids, 33, 181–187 (2002).

    Article  ADS  Google Scholar 

  2. M. Raffel, C. Willert, and J. Kompenhans, Particle Image Velocimetry. A Practical Guide, Springer, Berlin (2007).

    Google Scholar 

  3. F. Boden, T. Kirmse, and H. Jentink, “Image pattern correlation technique (IPCT),” Advanced Flight Testing Workshop AIM-2: Handbook of Advanced in-Flight Meas. Techniq., BoD-Books on Demand, Norderstedt (2013), pp. 63–85.

  4. N. M. Skornyakova, “Shadow background method,” Modern Optical Methods of Flow Studies, B. S. Rinkevichius (ed.), Overlei, Moscow (2011), pp. 93–107.

    Google Scholar 

  5. F. Boden, H. Jentink, and C. Petit, “Wing deformation measures on a large transport aircraft (IPCT),” Advanced In-Flight Measurement Techniques, Springer, Berlin, Heidelberg (2013), pp. 93–115.

  6. A. Yu. Poroykov and N. M. Skornyakova, “Analysis of the method of correlation of background images for measuring the bending of a metal surface,” Izmer. Tekhn., № 10, 43–46 (2010).

  7. A. Yu. Poroykov, “Restoration of the 3D-profile of a deformed metal plate by the correlation method of background images,” Izmer. Tekhn., № 14, 15–19 (2014).

  8. F. Boden, T. Kirmse, A. Yu. Poroykov, et al., “Accuracy of measurement of dynamic surface deformations by the image pattern correlation technique,” Optoelectr., Instrum. Data Proc., 50, No. 5, 474–481 (2014).

    Article  Google Scholar 

  9. T. Kirmse, A. Gardner, and C. Krombholz, “Investigation of aero-optical effects in model deformation measurements in a transonic flow,” Investig. Aero-Opt. Effects in Model Deform. Meas. Transonic Flow, 121, 665–672 (2013).

    Google Scholar 

  10. R. D. Keane and R. J. Adrian, “Theory of cross-correlation analysis of PIV images,” Flow Visualization and Image Analysis (1993), pp. 1–25.

  11. R. Gonzalez and R. Woods, Digital Processing of Images, TEKhNOSFERA, Moscow (2005).

    Google Scholar 

  12. M. Frigo and S. G. Johnson, “The design and implementation of FFTW3,” Proc. IEEE, 93, No. 2, 216–231 (2005).

    Article  Google Scholar 

  13. J. P. Lewis, “Fast normalized cross-correlation,” Proc. Vision Interface (1995), pp. 120–123.

  14. M. R. Haralick and G. L. Shapiro, Computer and Robot Vision. II., Addison-Wesley, New York (1992), pp. 316–317.

    Google Scholar 

  15. Z. L. Shapiro and J. Stockman, Computer Vision, Binom Lab. Znanii, Moscow (2009).

    Google Scholar 

  16. V. P. Kulesh, “Videogrammetric system for measuring deformations of a large-scale model in the stream of an aerodynamic pipe,” Datch. Sistemy, No. 8 (171), 7–12 (2013).

  17. Yu. V. Vizilter, S. Yu. Zheltov, A. V. Bondarenko, et al., Processing and Analysis of Images in Computer Vision Problems, Fizmatkniga, Moscow (2010).

    Google Scholar 

Download references

The study was supported by the Russian Foundation for Basic Research (Project No. 1637-60026 mol_a_dk).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Yu. Poroikov.

Additional information

Translated from Izmeritel’naya Tekhnika, No. 10, pp. 37–41, October, 2017.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Poroikov, A.Y., Evtikhieva, O.A. & Pavlov, I.N. An Algorithm for Compensating the Effect of Deformations When Using the Shadow Background Method. Meas Tech 60, 1022–1027 (2018). https://doi.org/10.1007/s11018-018-1311-y

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11018-018-1311-y

Keywords

Navigation