Quick Light-Mixing for Image Inspection Using Simplex Search and Robust Parameter Design

  • HyungTae Kim
  • KyeongYong Cho
  • SeungTaek Kim
  • Jongseok Kim
  • Sungbok Kang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 306)

Abstract

Finding the optimal illumination conditions for industrial machine vision is iterative and time-consuming work. This study discusses simplex search for automatic illumination and the Taguchi method for algorithm tuning. The simplex search could find the illumination inputs to obtain a fine image, but the degree of fineness and processing time varied according to the algorithm parameters. The mechanism from the inputs and the degree of fineness were complex and nonlinear, so it was hard to find the best parameters through conventional experiments. To address these issues, the Taguchi method was applied to the simplex search and used to figure out the best parameter combination with a minimum number of experiments. The targets of Taguchi analysis were finer images and fewer iterations. An L 25(55) orthogonal array was constructed for 5 parameters and 5 levels, and was filled with the experimental results. A new combination of parameters determined by Taguchi analysis was applied to retests. The retest results showed fewer iterations with a fine image that was close to the best case. The Taguchi method can reduce the amount of work required to set parameters for optimal illumination.

Keywords

Color mixer Optimal illumination Simplex search Robust design of experiments Taguchi method 

Notes

Acknowledgments

This work was funded and supported by the Korea Institute of Industrial Technology. The authors are grateful to AM Technology (http://www.amtechnology.co.kr) for supplying RGB mixable color sources.

References

  1. 1.
    Sun Y, Duthaler S, Nelson BJ (2004) Autofocusing in computer microscopy: selecting the optimal focus algorithm. Microsc Res Tech 65(3):139–149CrossRefGoogle Scholar
  2. 2.
    Jeon J, Yoon I, Kim D, Lee J, Paik J (2010) Fully digital auto-focusing system with automatic focusing region selection and point spread function estimation. IEEE Trans Cons Elec 56(3):1204–1210CrossRefGoogle Scholar
  3. 3.
    Kim HT, Kang SB, Kang HS, Cho YJ, Kim JO (2011) Optical distance control for a multi focus image in camera phone module assembly. Int J Precis Eng Manuf 12(5):805–811CrossRefGoogle Scholar
  4. 4.
    Varaun AV (2011) Adaptive lighting for machine vision applications. In: Proceedings of IEEE CRV, pp 140–145Google Scholar
  5. 5.
    Schechner YY, Nayar SK, Belhumeur PN (2007) Multiplexing for optimal lighting. IEEE Trans Patt Anan Mach Int 29(8):1339–1354CrossRefGoogle Scholar
  6. 6.
    Kim HT, Kim ST, Cho YJ (2012) A review of light intensity control and quick optimum search in machine vision. In: Proceedings on international symposium optomechatronic technologiesGoogle Scholar
  7. 7.
    Arecchi AV, Messadi T, Koshel RJ (2007) Field guide to illumination. SPIE Press, WashingtonCrossRefGoogle Scholar
  8. 8.
    Muthu S, Gaines J (2003) Red, green and blue LED-based white light source: implementation challenges and control design. In: Proceedings on the IEEE industry applications conference, vol 1. pp 515–522Google Scholar
  9. 9.
    Sun CC, Moreno I, Lo YC, Chiu BC, Chien WT (2011) Collimating lamp with well color mixing of red/green/blue LEDs. Opt Express 20(S1):75–84CrossRefGoogle Scholar
  10. 10.
    Esparza D Moreno I (2010) Color patterns in a tapered lightpipe with RGB LEDs. In: Proceedings of SPIE, vol 7786Google Scholar
  11. 11.
    Muschaweck J (2011) Randomized micro lens arrays for color mixing. In: Proceedings of SPIE, vol 7954Google Scholar
  12. 12.
    van Gorkom RP, van AS, MA, Verbeek GM, Hoelen CGA, Alferink RG, Mutsaers CA, Cooijmans H (2007) Etendue conserved color mixing. In: Proceedings of SPIE, vol 6670Google Scholar
  13. 13.
    Lee MH, Seo DK, Seo BK, Park JI (2011) Optimal illumination spectrum for endoscope. Korea-Japan Joint Workshop on frontiers of computer vision, pp 1–6Google Scholar
  14. 14.
    Kim HT, Kim ST, Cho YJ (2012) An optical mixer and RGB control for fine images using grey scale distribution. Int J Optomecha 6(3):213–225CrossRefGoogle Scholar
  15. 15.
    Kim HT, Kim ST, Kim JS (2013) Mixed-color illumination and quick optimum search for machine vision. Int J Optomecha 7(3):1–15CrossRefGoogle Scholar
  16. 16.
    Kelley CT (1999) Iterative methods for optimization. SIAM, Philadelphia, pp 135–137CrossRefMATHGoogle Scholar
  17. 17.
    Jiang BC, Shiau MY (1990) A systematic methodology for determining/optimizing a machine vision system’s capability. Mach Vis Appl 3(3):169–182CrossRefGoogle Scholar
  18. 18.
    Su TL, Chen HW, Hong GB, Ma CM (2010) Automatic inspection system for defects classification of stretch kintted fabrics. In: Proceedings of Wavelet Analysis and Pattern Recognition, pp 125–129Google Scholar
  19. 19.
    Ho SY, Huang HL (2001) Facial modeling from an uncalibrated face image using flexible generic parameterized facial models. IEEE.Trans Syst Man Cybern B 31(5):706–719CrossRefGoogle Scholar
  20. 20.
    Chian TW, Kok SS, Lee SG, Wei OK (2008) Gesture based control of mobile robots. In: Proceedings of the Innovative Technologies in Intelligent Systems and Industrial Applications, pp 20–25Google Scholar
  21. 21.
    Li M, Milor L, Yu W (1997) Developement of optimum annular illumination: a lithography-TCAD approach. In: Proceedings of Advanced Semiconductor Manufacturing, pp 317–321Google Scholar
  22. 22.
    Lin CF, Wu CC, Yang PH, Kuo TY (2009) Application of Taguchi method in light-emitting diode backlight design for wide color gamut displays. J. Disp Tech 5(8):323–330CrossRefGoogle Scholar
  23. 23.
    Yoo WS, Jin QQ, Chung YB (2007) A study on the optimization for the blasting process of glass by Taguchi method. J. Soc Korea Ind Sys. Eng 30(2):8–14CrossRefGoogle Scholar
  24. 24.
    Wu Y, Wu A (2000) Taguchi methods for robust design. ASME Press, New York, pp 3–16Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • HyungTae Kim
    • 1
  • KyeongYong Cho
    • 2
  • SeungTaek Kim
    • 1
  • Jongseok Kim
    • 1
  • Sungbok Kang
    • 1
  1. 1.Manufacturing System R&D GroupKITECHChenAnSouth Korea
  2. 2.UTRCKAISTYuSeong, DaeJeonSouth Korea

Personalised recommendations