OSA's Digital Library

Applied Optics

Applied Optics


  • Vol. 44, Iss. 13 — May. 1, 2005
  • pp: 2530–2540

Virtually calibrated projection moire interferometry

Mark Kimber and Jonathan Blotter  »View Author Affiliations

Applied Optics, Vol. 44, Issue 13, pp. 2530-2540 (2005)

View Full Text Article

Enhanced HTML    Acrobat PDF (2473 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Projection moire interferometry (PMI) is an out-of-plane displacement measurement technique that consists of differencing reference and deformed images of a grid pattern projected onto the test object. In conventional PMI, a tedious process of computing the fringe sensitivity coefficient (FSC), which requires moving the test object or the reference plane to known displacements, is used. We present a new technique for computing the FSC values that is called virtually calibrated projection moire interferometry (VCPMI). VCPMI is based on computer simulations of the conventional PMI process and does not require moving the actual test object or reference plane. We validate the VCPMI approach by comparing results for a flat plate and an airfoil with those made by use of other measurement methods.

© 2005 Optical Society of America

OCIS Codes
(120.2650) Instrumentation, measurement, and metrology : Fringe analysis
(120.4120) Instrumentation, measurement, and metrology : Moire' techniques

Original Manuscript: September 13, 2004
Revised Manuscript: November 16, 2004
Manuscript Accepted: November 24, 2004
Published: May 1, 2005

Mark Kimber and Jonathan Blotter, "Virtually calibrated projection moire interferometry," Appl. Opt. 44, 2530-2540 (2005)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. A. Mikheev, “Simultaneous velocity and temperature field measurement of high-temperature flows,” Deutsche schungsanstalt Luft und Raumfahrt Mitteilung 40, 377–381 (2001).
  2. G. M. Quenot, J. Pakleza, T. A. Kowalewski, “Particle image velocimetry with optical flow,” Exp. Fluids 25, 177–189 (1998). [CrossRef]
  3. F. Chen, G. Brown, M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000). [CrossRef]
  4. R. Rodriguez-Vera, D. Kerr, “Displacement and shape information using electronic speckle contouring,” in Interferometry VI: Applications, R. J. Pryputniewicz, G. M. Brown, W. P. Jueptner, eds., Proc. SPIE2004, 52–62 (1994). [CrossRef]
  5. J. Kozlowski, P. Boccardi, M. Fiore, “Novel interferometric method for contour mapping of optically rough surfaces,” Opt. Lasers Eng. 31, 41–50 (1999). [CrossRef]
  6. B. F. Andresen, M. Strojnik, eds., “Infrared Technology XXI,” Proc. SPIE2552 (1995).
  7. A. Szwedowski, M. Lesniewski, “Hybrid analysis of optical elements by interferometry and thermography,” in Interferometry '99: Applications, W. P. Jueptner, K. Patorski, eds., Proc. SPIE3745, 78–85 (1999). [CrossRef]
  8. G. A. Fleming, H. L. Soto, B. W. South, S. M. Bartram, “Advances in projection moiré interferometry development for large wind tunnel applications,” presented at the World Aviation Conference, San Francisco, Calif., 19–21 October 1999.
  9. G. A. Fleming, S. A. Gorton, “Measurement of rotorcraft blade deformation using projection moiré interferometry,” J. Shock Vib. 7, 149–165 (2000). [CrossRef]
  10. L. Pirrodda, “Shadow and projection moiré techniques for absolute or relative mapping of surface shapes,” Opt. Eng. 21, 640–649 (1982).
  11. D. Mishra, J. Blotter, “Comparison of reference image generation techniques for projection moiré interferometry,” Appl. Opt. 40, 5624–5631 (2001). [CrossRef]
  12. D. Mishra, J. Blotter, “Image dewarping and region of interest detection for processing moiré images,” Opt. Commun. 213, 39–47 (2002). [CrossRef]
  13. J. Heikkila, “Geometric camera calibration using circular control points,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1066–1077 (2000). [CrossRef]
  14. Q. Long, “Self-calibration of an affine camera from multiple views,” Int. J. Comput. Vision 19, 93–105 (1996). [CrossRef]
  15. L. Robert, “Camera calibration without feature extraction,” Comput. Vis. Image Underst. 63, 314–325 (1996). [CrossRef]
  16. M. Ito, “Robot vision modeling. Camera modeling and camera calibration,” Advanced Robotics 5, 321–325 (1991). [CrossRef]
  17. J. Bouguet, Matlab camera calibration toolbox: http://www.vision.caltech.edu/bouguetj/calib_doc .
  18. D. Forsyth, J. Ponce, Computer Vision: A Modern Approach (Pearson, 2003).
  19. J. H. Matthews, K. D. Fink, Numerical Methods Using MATLAB (Pearson, 2004).
  20. Software developed by the Zemax Corporation, San Diego, Calif.
  21. User-defined scattering in Zemax User Manual (Zemax Corporation, San Diego, Calif., 12November2003), p. 334.

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited