Experiments in Fluids

, Volume 39, Issue 3, pp 630–635 | Cite as

Accuracy of correlation-based image registration for pressure-sensitive paint

  • Hyung Jin SungEmail author
  • Sang-Hyun Park
  • Moo Sang Kim


The accuracy of correlation-based image registration (CBIR) in the analysis of pressure-sensitive paints (PSP) was investigated. CBIR has been developed to perform accurate image registration without the need for control points, even for model motions containing nonlinear local deformations. In the present study, the influence of displacement errors and their sensitivity on the accuracy of pressure measurement was examined by uncertainty analysis. The error sources in image registration were classified and several factors affecting the accuracy of image registration were examined. The performances of image registration were evaluated under several artificial model motions. Local intensity variations due to speckles, which enhance the image correlation in CBIR, may act as a source of image noise. The local pressure sensitivity in the presence and absence of speckles was investigated through pixel-by-pixel calibration. A spatial filtering was employed to reduce the local intensity variations. It was found that application of a median filter decreased the fluctuations in the local pressure sensitivity and significantly reduced the sensitivity of the intensity error to misregistration.


Image Registration Median Filter Interrogation Window Wind Tunnel Test Displacement Error 
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.


A, B

Stern–Volmer constant


Diameter of speckle [pixels]


Luminescent intensity [ADU]


Density of speckle [unit/window]


Pressure [Pa]


Temperature [K]


Uncertainty in intensity


Threshold level of median filter


Image plane

x, y

Image plane coordinates

Δx, Δy

Local displacements of the model

σx, σy

Residual displacement errors followed by image registration


  1. Bell JH, McLachlan BG (1996) Image registration for pressure-sensitive paint applications. Exp Fluids 22:78–86Google Scholar
  2. Brown OC (2000) Low-speed pressure measurements using a luminescent coating system. PhD thesis, Stanford University, Stanford, pp 124–137Google Scholar
  3. Donovan JF, Morris MJ, Pal A, Benne ME, Crites RC (1993) Data analysis techniques for pressure and temperature-sensitive paint. In: Proceedings of the 31st Aerospace Sciences Meeting and Exhibit, Reno, Nevada, January 1993, AIAA paper 93-0176Google Scholar
  4. Gonzalez RC, Woods RE (1992) Digital image processing. Addison Wesley, Longman, Edinburgh, UKGoogle Scholar
  5. Keane RD, Adrian RJ (1992) Theory of cross-correlation analysis of PIV images. Appl Sci Res 49:191–215CrossRefGoogle Scholar
  6. Lehmann TM, Gönner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Trans Med Imaging 18:1049–1075CrossRefPubMedGoogle Scholar
  7. Liu T, Guille M, Sullivan JP (2001) Accuracy of pressure sensitive paint. AIAA J 39(1):103–112Google Scholar
  8. Mendoza DR (1997) An analysis of CCD camera noise and its effect on pressure sensitive paint instrumentation system signal-to-noise ratio. In: Proceedings of the 17th International Congress on Instrumentation in Aerospace Simulation Facilities, Pacific Grove, California, September/October 1997, pp 22–29Google Scholar
  9. Park S-H, Sung HJ (2005) Correlation-based image registration for applications using pressure-sensitive paint. AIAA J 43(3):472–478Google Scholar
  10. Sajben M (1993) Uncertainty estimates for pressure sensitive paint measurements. AIAA J 31(11):2105–2110Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Hyung Jin Sung
    • 1
    Email author
  • Sang-Hyun Park
    • 2
  • Moo Sang Kim
    • 2
  1. 1.Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyYuseong-guRepublic of Korea
  2. 2.Hyundai Motor CompanyHwaseong-siRepublic of Korea

Personalised recommendations