OSA's Digital Library

Applied Optics

Applied Optics


  • Vol. 31, Iss. 11 — Apr. 10, 1992
  • pp: 1871–1877

Fluorescence imaging for machine vision

Jie Chen and Oleh J. Tretiak  »View Author Affiliations

Applied Optics, Vol. 31, Issue 11, pp. 1871-1877 (1992)

View Full Text Article

Enhanced HTML    Acrobat PDF (1682 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Suitable illumination is a crucial aspect in the successful solution of machine vision problems. In this research we used objective image evaluation techniques and found that fluorescence imaging is superior to conventional illumination for acquiring images of integrated circuit lead bonds. This is an interesting and surprising finding, since there was no a priori reason to expect that any part of the bond would contain fluorescent components. Consequently, fluorescence imaging should be considered as an option in designing machine vision systems, especially if conventional illumination systems do not produce images of adequate quality. In this research we discovered a novel and effective method for threshold selection.

© 1992 Optical Society of America

Original Manuscript: February 6, 1991
Published: April 10, 1992

Jie Chen and Oleh J. Tretiak, "Fluorescence imaging for machine vision," Appl. Opt. 31, 1871-1877 (1992)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. R. T. Chin, C. A. Harlow, “Automatic visual inspection: a survey.” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 557–573 (1982). [CrossRef]
  2. D. Domres, J. MacFarlane, “Automatic optical inspection techniques for PWB’s—image acquisition and analysis remain areas of challenge,” Test Meas. World 3, 235–239 (1983).
  3. S. Mersch, “Polarized lighting for machine vision applications” in Proceedings of RI/SME Third Annual Conference on Applied Machine Vision (Robotics International, Society of Manufacturing Engineers, Schaumburg, Ill., 1984), pp. 687–691.
  4. A. Pugh, “Robot sensors—a personal view,” in Robot Sensors, A. Pugh ed. (Springer-Verlag, New York, 1986), Vol. 1, pp. 3–14.
  5. M. Born, E. Wolf, Principles of Optics, 2nd ed. (Macmillan, New York, 1964), pp. 264–265.
  6. D. C. Pritchard, Lighting, 2nd ed. (Longmans, London, 1978), pp. 27–30.
  7. R. S. Longhurst, Geometrical and Physical Optics, 2nd ed. (Wiley, New York, 1967), Chap. 20, p. 450.
  8. J. P. Frier, M. E. Frier, Industrial Lighting Systems (McGraw-Hall, New York, 1980), pp. 149–155.
  9. M. I. Sobel, Light (U. Chicago Press, Chicago, Ill., 1987), pp. 229–230.
  10. D. D. Zimmerman, J. R. Dinitto, Hybrid Microcircuit Design Guide (Noyes, Park Ridge, N.J., 1982).
  11. P. Burggraaf, “Inspection trends in IC assembly,” Semicond. Int. 8, 76–81 (1985).
  12. R. Kingslake, Optical Instruments, Vol. 4 of Applied Optics and Optical Engineering, R. Kingslake, ed. (Academic, New York, 1967), pp. 84–88.
  13. R. Kohler, “A segmentation system based on thresholding,” Comput. Graph. Image Processing 15, 319–338 (1981). [CrossRef]
  14. J. Chen, “Knowledge-directed lead bond inspection,” Ph.D. dissertation (Drexel University, Philadelphia, Pa., 1989).
  15. O. Tretiak, G. Y. Yu, “Curve-fitting method for measurement of the resolution of digital image input devices,” Opt. Eng. 25, 1312–1315 (1986).

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

OSA is a member of CrossRef.

CrossCheck Deposited