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Applied Optics

Applied Optics


  • Editor: James C. Wyant
  • Vol. 46, Iss. 8 — Mar. 10, 2007
  • pp: 1233–1243

Neural network digital fringe calibration technique for structured light profilometers

Matthew J. Baker, Jiangtao Xi, and Joe F. Chicharo  »View Author Affiliations

Applied Optics, Vol. 46, Issue 8, pp. 1233-1243 (2007)

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We present a novel neural network signal calibration technique to improve the performance of triangulation-based structured light profilometers based on digital projection. The performance of such profilometers is often hindered by the capture of aberrated pattern intensity distributions, and hence we address this problem by employing neural networks in a signal mapping approach. We exploit the generalization and interpolation capabilities of a feed-forward backpropagation neural network to map from distorted fringe data to nondistorted data. The performance of the calibration technique is gauged both through simulation and experimentation, with simulation results indicating that accuracy can be improved by more than 80%. The technique requires just one image cross section for calibration and hence is ideal for rapid profiling applications.

© 2007 Optical Society of America

OCIS Codes
(120.2650) Instrumentation, measurement, and metrology : Fringe analysis
(120.6650) Instrumentation, measurement, and metrology : Surface measurements, figure
(150.6910) Machine vision : Three-dimensional sensing

Original Manuscript: July 24, 2006
Manuscript Accepted: September 25, 2006
Published: February 20, 2007

Matthew J. Baker, Jiangtao Xi, and Joe F. Chicharo, "Neural network digital fringe calibration technique for structured light profilometers," Appl. Opt. 46, 1233-1243 (2007)

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  1. V. Srinivasan, H. C. Lui, and M. Halioua, "Automated phase-measuring profilometry of 3D diffuse objects," Appl. Opt. 23, 3105-3108 (1984). [CrossRef] [PubMed]
  2. X. Su and W. Chen, "Fourier transform profilometry: a review," Opt. Lasers Eng. 35, 263-284 (2001). [CrossRef]
  3. J. Xu, Y. Wang, S. Si, C. Gao, and D. Yun, "Research on application of special filter in projecting grating profilometry," in Optical Technology and Image Processing for Fluids and Solids Diagnostics, Proc. SPIE 5058, 532-536 (2003). [CrossRef]
  4. J. Villa, M. Servin, and L. Castillo, "Profilometry for the measurement of 3D object shapes based on regularized filters," Opt. Commun. 161, 13-18 (1999). [CrossRef]
  5. H. Zhi and R. B. Johansson, "Adaptive filter for enhancement of fringe patterns," Opt. Lasers Eng. 15, 241-251 (1991). [CrossRef]
  6. S. Ryoo and T. Choi, "3D profilometry by analysis of noisy white-light interferograms," in Three-dimensional and Multidimensional Microscopy: Image Acquisition Processing VII,Proc. SPIE 3919, 152-160 (2000). [CrossRef]
  7. L. Kinell, "Multichannel method for absolute shape measurement using projected fringes," Opt. Lasers Eng. 41, 57-71 (2004). [CrossRef]
  8. C. R. Coggrave and J. M. Huntley, "High-speed surface profilometer based on a spatial light modulator and pipeline image processor," Optical Engineering 38, 1573-1581 (1999). [CrossRef]
  9. S. Kakunai, T. Sakamoto, and K. Iwata, "Profile measurement taken with liquid-crystal grating," Appl. Opt. 38, 2824-2828 (1999). [CrossRef]
  10. H. Guo, H. He, and M. Chen, "Gamma correction for digital fringe projection profilometry," Appl. Opt. 43, 2906-2914 (2004). [CrossRef] [PubMed]
  11. H. Farid, "Blind inverse gamma correction," IEEE Trans. Image Process. 10, 1428-1433 (2001). [CrossRef]
  12. F. J. Cuevas, M. Servin, O. N. Stavroudis, and R. Rodriguez-Vera, "Multilayer neural network applied to phase and depth recovery from fringe patterns," Opt. Commun. 181, 239-259 (2000). [CrossRef]
  13. H. Mills, D. R. Burton, and M. J. Lalor, "Applying backpropagation neural networks to fringe analysis," Opt. Lasers Eng. 23, 331-341 (1995). [CrossRef]
  14. D. Ganotra, J. Joseph, and K. Singh, "Profilometry for the measurement of three-dimensional object shape using radial basis function, and multilayer perceptron neural networks," Opt. Commun. 209, 291-301 (2002). [CrossRef]
  15. D. Ganotra, J. Joseph, and K. Singh, "Second- and first-order phase locked loops in fringe profilometry and application of neural networks for phase-to-depth conversion," Opt. Commun. 217, 85-96 (2003). [CrossRef]
  16. D. Ganotra, J. Joseph, and K. Singh, "Object reconstruction in multilayer neural network based profilometry using grating structure comprising two regions with different spatial periods," Opt. Lasers Eng. 42, 179-192 (2004). [CrossRef]
  17. G. Zhang and Z. Wei, "A novel calibration approach to structured light 3D vision inspection," Opt. Laser Technol. 34, 373-380 (2002). [CrossRef]
  18. F. J. Cuevas, M. Servin, and R. Rodriguez-Vera, "Depth object recovery using radial basis functions," Opt. Commun. 163, 270-277 (1999). [CrossRef]
  19. M. Chang and W. Tai, "360-deg profile noncontact measurement using a neural network," Opt. Eng. 34, 3572-3576 (1995). [CrossRef]
  20. S. Toyooka, Y. Iwaasa, "Automatic profilometry of 3-D diffuse objects by spatial phase detection," Appl. Opt. 25, 1630-1633 (1986). [CrossRef] [PubMed]
  21. M. J. Baker, J. Xi, J. Chicharo, and E. Li, "A contrast between dlp and lcd digital projection technology for triangulation-based phase measuring optical profilometers," in Two- and Three-Dimensional Methods for Inspection and Metrology III, K. Harding, ed., Proc. SPIE 600C, 151-162 (2005).
  22. R. Y. Tsai, "A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf tv cameras and lenses," IEEE J. Rob. Autom. RA-3, 323-344 (1987). [CrossRef]
  23. R. M. Haralick and L. G. Shapiro, Computer and Robot Vision (Addison-Wesley, 1992), Vol. 1.
  24. E. R. Davies, Machine Vision: Theory Algorithms Practicalities, 3rd ed. (Elsevier, 2005).
  25. G. Horvath, Neural Networks for Instrumentation, Measurement and Related Industrial Applications (IOS Press, 2003).

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