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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 20, Iss. 1 — Jan. 1, 2003
  • pp: 106–115

Optimized two-frequency phase-measuring-profilometry light-sensor temporal-noise sensitivity

Jielin Li, Laurence G. Hassebrook, and Chun Guan  »View Author Affiliations


JOSA A, Vol. 20, Issue 1, pp. 106-115 (2003)
http://dx.doi.org/10.1364/JOSAA.20.000106


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Abstract

Temporal frame-to-frame noise in multipattern structured light projection can significantly corrupt depth measurement repeatability. We present a rigorous stochastic analysis of phase-measuring-profilometry temporal noise as a function of the pattern parameters and the reconstruction coefficients. The analysis is used to optimize the two-frequency phase measurement technique. In phase-measuring profilometry, a sequence of phase-shifted sine-wave patterns is projected onto a surface. In two-frequency phase measurement, two sets of pattern sequences are used. The first, low-frequency set establishes a nonambiguous depth estimate, and the second, high-frequency set is unwrapped, based on the low-frequency estimate, to obtain an accurate depth estimate. If the second frequency is too low, then depth error is caused directly by temporal noise in the phase measurement. If the second frequency is too high, temporal noise triggers ambiguous unwrapping, resulting in depth measurement error. We present a solution for finding the second frequency, where intensity noise variance is at its minimum.

© 2003 Optical Society of America

OCIS Codes
(110.6880) Imaging systems : Three-dimensional image acquisition
(120.2650) Instrumentation, measurement, and metrology : Fringe analysis
(120.4290) Instrumentation, measurement, and metrology : Nondestructive testing
(120.5800) Instrumentation, measurement, and metrology : Scanners
(150.5670) Machine vision : Range finding

Citation
Jielin Li, Laurence G. Hassebrook, and Chun Guan, "Optimized two-frequency phase-measuring-profilometry light-sensor temporal-noise sensitivity," J. Opt. Soc. Am. A 20, 106-115 (2003)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-20-1-106


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References

  1. F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000).
  2. J. Batlle, E. Mouaddib, and J. Salvi, “Recent progress in coded structured light as a technique to solve the correspondence problem: a survey,” Pattern Recogn. 31, 963–982 (1998).
  3. X. Y. Su and W. S. Zhou, “Complex object profilometry and its application for dentistry,” in Clinical Applications of Modern Imaging Technology II, L. J. Cerullo, K. S. Heiferman, Hong Liu, H. Podbielska, A. O. Wist, and L. J. Eamorano, eds., Proc. SPIE 2132, 484–489 (1994).
  4. G. Sansoni, F. Docchio, U. Minoni, and L. Biancardi, “Adaptive profilometry for industrial applications,” in Laser Applications to Mechanical Industry, S. Martellucci and A. N. Chester, eds. (Kluwer Academic, Norwell, Mass., 1993), pp. 351–365.
  5. R. Raskar, G. Welch, M. Cutts, A. Lake, L. Stesin, and H. Fuchs, “The office of the future: a unified approach to image-based modeling and spatially immersive displays,” presented at SIGGRAPH 98, Orlando, Fla., July 19–24, 1998.
  6. G. Schmaltz, “A method for presenting the profile curves of rough surfaces,” Naturwissenschaften 18, 315–316 (1932).
  7. Y. Shirai and M. Suwa, “Recognition of polyhedrons with a range finder,” in Proceeding of the International Joint Conference on Artificial Intelligence (Morgan Kaufman, San Francisco, Calif., 1971), pp. 80–87.
  8. P. M. Will and K. S. Pennington, “Grid coding: a preprocessing technique for robot and machine vision,” Artif. Intell. 2, 319–329 (1971).
  9. B. Carrihill and R. Hummel, “Experiments with intensity ratio depth sensor,” Comput. Vision Graph. Image Process. 32, 337–358 (1985).
  10. D. S. Goodman and L. G. Hassebrook, “Surface contour measuring instrument,” IBM Tech. Discl. Bull. 27 (4B), 2671–2673 (1984).
  11. J. L. Posdamer and M. D. Altschuler, “Surface measurement by space-encoded projected beam systems,” Comput. Vision Graph. Image Process. 18, 1–17 (1982).
  12. D. M. Meadows, W. O. Johnson, and J. B. Allen, “Generation of surface contours by moire patterns,” Appl. Opt. 9, 942 (1970).
  13. G. Goli, Chun Guan, L. G. Hassebrook, and D. L. Lau, “Video rate three dimensional data acquisition using composite light structure patterns,” Univ. of Kentucky ECE Tech. Rep. CSP-02–002 (May 30, 2002).
  14. V. Srinivasan, H. C. Liu, and M. Halioua, “Automated phase measuring profilometry: a phase mapping approach,” Appl. Opt. 24, 185–188 (1985).
  15. K. L. Boyer and A. C. Kak, “Colored-encoded structured light for rapid active ranging,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-9, 14–28 (1987).
  16. L. G. Hassebrook, R. C. Daley, and W. Chimitt, “Application of communication theory to high speed structured light il-lumination,” in Three-Dimensional Imaging and Laser-Based Systems for Metrology and Inspection III, K. G. Harding and D. J. Svetproff, eds., Proc. SPIE 3204, 102–113 (1997).
  17. J. M. Huntley and H. O. Saldner, “Shape measurement by temporal phase unwrapping: comparison of unwrapping algorithms,” Meas. Sci. Technol. 8, 986–992 (1997).
  18. H. Zhao, W. Chen, and Y. Tan, “Phase-unwrapping algorithm for the measurement of three-dimensional object shapes,” Appl. Opt. 33, 4497–4500 (1994).
  19. M. Trobina, “Error model of a coded-light range sensor,” Tech. Rep. BIWI-TR-164, ETH-Zentrum (September 21, 1995), pp. 1–35.
  20. R. C. Daley and L. G. Hassebrook, “Channel capacity model of binary encoded structured light-stripe illumination,” Appl. Opt. 37, 3689–3696 (1998).
  21. O. D. Faugeras and G. Toscani, “The calibration problem for stereo,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition ’86 (Institute of Electrical and Electronics Engineers, New York, 1986), pp. 15–20 (1986).
  22. R. Y. Tsai, “A versatile camera calibration technique forhigh accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE Trans. Rob. Autom. RA-3, 323–344 (1987).
  23. R. J. Valkenburg and A. M. McIvor, “Accurate 3D measurement using a structured light system,” Image Vision Comput. 16, 99–110 (1998).
  24. R. W. DePiero and M. M. Trivedi, “3-D computer vision using structured light: design, calibration and implementation issues,” Adv. Comput. 43, 243–278 (1996).
  25. Behrooz Kamgar-parsi and Behzad Kamgar-parsi, “Evaluation of quantization error in computer vision,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 929–939 (1989).
  26. W. S. Zhou and X. Y. Su, “A direct mapping algorithm for phase-measuring profilometry,” J. Mod. Opt., 41, 89–94 (1994).
  27. F. L. Pedrotti and L. S. Pedrotti, Introduction to Optics, 2nd ed. (Prentice-Hall, Englewood Cliffs, N.J., 1993).
  28. J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).
  29. E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision (Prentice-Hall, Englewood Cliffs, N.J., 1998), Chap. 6, pp. 123–138.

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