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.
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References
Sun Y, Duthaler S, Nelson BJ (2004) Autofocusing in computer microscopy: selecting the optimal focus algorithm. Microsc Res Tech 65(3):139–149
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):1204–1210
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):805–811
Varaun AV (2011) Adaptive lighting for machine vision applications. In: Proceedings of IEEE CRV, pp 140–145
Schechner YY, Nayar SK, Belhumeur PN (2007) Multiplexing for optimal lighting. IEEE Trans Patt Anan Mach Int 29(8):1339–1354
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 technologies
Arecchi AV, Messadi T, Koshel RJ (2007) Field guide to illumination. SPIE Press, Washington
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 515–522
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):75–84
Esparza D Moreno I (2010) Color patterns in a tapered lightpipe with RGB LEDs. In: Proceedings of SPIE, vol 7786
Muschaweck J (2011) Randomized micro lens arrays for color mixing. In: Proceedings of SPIE, vol 7954
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 6670
Lee MH, Seo DK, Seo BK, Park JI (2011) Optimal illumination spectrum for endoscope. Korea-Japan Joint Workshop on frontiers of computer vision, pp 1–6
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):213–225
Kim HT, Kim ST, Kim JS (2013) Mixed-color illumination and quick optimum search for machine vision. Int J Optomecha 7(3):1–15
Kelley CT (1999) Iterative methods for optimization. SIAM, Philadelphia, pp 135–137
Jiang BC, Shiau MY (1990) A systematic methodology for determining/optimizing a machine vision system’s capability. Mach Vis Appl 3(3):169–182
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 125–129
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):706–719
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 20–25
Li M, Milor L, Yu W (1997) Developement of optimum annular illumination: a lithography-TCAD approach. In: Proceedings of Advanced Semiconductor Manufacturing, pp 317–321
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):323–330
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):8–14
Wu Y, Wu A (2000) Taguchi methods for robust design. ASME Press, New York, pp 3–16
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.
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© 2014 Springer International Publishing Switzerland
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Kim, H., Cho, K., Kim, S., Kim, J., Kang, S. (2014). Quick Light-Mixing for Image Inspection Using Simplex Search and Robust Parameter Design. In: Tutsch, R., Cho, YJ., Wang, WC., Cho, H. (eds) Progress in Optomechatronic Technologies. Lecture Notes in Electrical Engineering, vol 306. Springer, Cham. https://doi.org/10.1007/978-3-319-05711-8_5
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DOI: https://doi.org/10.1007/978-3-319-05711-8_5
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