Abstract
Obtaining a fine image is one of the major issues in industrial vision, and light mixing techniques are one of the alternatives. Auto-lighting using a multiple color mixer requires iterative actions. Random search shows high efficiency in finding the optimal illumination. However, random search is one of the numerical algorithms to find local minimum, so the algorithm parameters affect the performance of auto-lighting. The relation between the light mixing and the image fineness is mathematically nonlinear, and it is difficult to tune the parameters reliably. This study proposes a method to determine reliable parameters in random search for optimal illumination in image inspection using a color mixer. The Taguchi method was applied to maximize the image fineness and minimize iterations. The parameters selected for Taguchi analysis were the initial voltage, initial variance, and convergence constant. An L 25 (55) orthogonal array was constructed in consideration of the 5 parameters and 5 levels. The determined parameters were applied to retests, which showed fewer iterations and the acquired image was close to the best case.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arecchi, A.V., Messadi, T., Koshel, R.J.: Fieldguide to illimination, pp. 110–115. SPIE Press, Washington (2007)
Bueno-lbarra, M.A., Alvarez-Borrego, J., Acho, L., Chavez-Sanchez, M.C.: Fast autofocus algorithm for automated microscopes. Optic. Eng. 44(6), 063601 (2005)
Chern, N.K., Neow, P.A., Ang, M.H.: Practical issues in pixel-based autofocusing for machine vision. In: Proceeding of International Conference on Robotics and Automation, pp. 2791–2796 (2001)
Chi, C., Yoo, H.J., Ben-Ezra, M.: Multi-spectral imaging by optimized wide band illumination. Int. J. of Comp. Vis. 86, 140–151 (2010)
Chian, T.W., Kok, S.S., Lee, S.G., Wei, O.K.: Gesture based control of mobile robots. In: Proceeding of the Innovative Technogies in Intelligent System and Industrial Application, pp. 20–25 (2008)
Esparza, D., Moreno, I.: Color patterns in a tapered lightpipe with RGB LEDs. In: Proceeding of SPIE, vol. 7786 (2010)
Ding, F.J., Chen, Y.R., Chao, K.L.: Application of color mixing for safety and quality inspection of agricultural products. In: Proceeding of SPIE, vol. 5996, p. 59960R (2005)
Ding, F.J., Chen, Y.R., Chao, K.L., Kim, M.S.: Three-color mixing for classifying agricultural products for safety and quality. App. Opt. 45(15), 3516–3526 (2006)
Groen, F.C.A., Young, I.T., Ligthart, G.: A Comparison of different focus functions for use in autofocus algorithms. Cytometry 6(2), 81–91 (1985)
Ho, S.Y., Huang, H.L.: Facial modeling from an uncalibrated face image using flexible generic parameterized facial models. IEEE Trans. on Sys. Man and Cyber. - Part B 31(5), 706–719 (2001)
Kehtarnavaz, N., Oh, H.J.: Development and real-time implementation of a rulebased auto-focus algorithm. Real-Time Im. 9, 197–203 (2003)
Kim, H.T., Kang, S.B., Kang, H.S., Cho, Y.J., Kim, J.O.: Optical distance control for a multi focus image in camera phone module assembly. Int. J. Prec. Eng. and Manuf. 12(5), 805–811 (2011)
Kim, H.T., Kim, S.T., Cho, Y.J.: A review of light intensity control and quick optimum search in machine vision. In: Proceeding on International Symposium of Optomechatronic Technologies (2012)
Kim, H.T., Kim, S.T., Cho, Y.J.: An optical mixer and RGB control for fine images using grey scale distribution. J. of Optomech. 6(3), 213–225 (2012)
Kim, H.T., Kim, S.T., Kim, J.S.: Mixed-color illumination and quick optimum search for machine vision. J. of Optomech. 7(3), 208–222 (2013)
Knuth, D.E.: The art of computer programming, 2nd edn., vol. 2, pp. 116–122. Addison-Wesley, Massachusetts (1981)
Krotkov, E.P.: Active computer vision by cooperative focus and stereo. Springer, New York (1989)
Kuo, C.F.J., Chiu, C.S.: Improved auto-focus search algorithms for CMOS image-sensing module. J. of Infor. Sci. and Eng. 27, 1377–1393 (2011)
Lee, M.H., Seo, D.K., Seo, B.K., Park, J.I.: Optimal illumination stectrum for endoscope. In: Korea-Japan Joint Workshop on Frontiers of Computer Vision (2011)
Li, M., Milor, L., Yu, W.: Development of optimum annular illumination: A lithography-TCAD approach. In: Proceeding of Advanced Semiconductor Manufacturing Conference and Workshop, pp. 317–321 (1997)
Lin, C.F., Wu, C.C., Yang, P.H., Kuo, T.Y.: Application of Taguchi method in light-emitting diode backlight design for wide color gamut displays. J. of Disp. Tech. 5(8), 323–330 (2009)
Muruganantham, C., Jawahar, N., Ramamoorthy, B., Giridhar, D.: Optimal settings for vision camera calibration. Int. J. of Manuf. Tech. 42, 736–748 (2009)
Muschaweck, J.: Randomized micro lens arrays for color mixing. In: Proceeding of SPIE, vol. 7954 (2011)
Muthu, S., Gaines, J.: Red, Green and blue LED-based white light source: Implementation challenges and control design. In: Proceeding on the IEEE Industry Application Conference, vol. 1, pp. 515–522 (2003)
Proefschrift: Chemometrics in multispectral imaging for quality inspection of postharvest products. Thesis on Ph.D., aan de Radboud Universiteit Nijmegen (2005)
Santos, A., De Solorzano, C.O., Vaquero, J.J., Pena, J.M., Malpica, N., Pozo, F.D.: Evaluation of autofocus functions in molecular cytogenetic analysis. J. of Micros. 188, 264–272 (1997)
Su, T.L., Chen, H.W., Hong, G.B., Ma, C.M.: Automatic inspection system for defects classification of stretch kintted fabrics. In: Proceeding of Wavelet Analysis and Pattern Recognition, pp. 125–129 (2010)
Subbarao, M., Choi, T., Nikzad, A.: Focusing techniques, Tech. Report 92.09.04. State University of New York at Stony Brook (1992)
Sun, Y., Duthaler, S., Nelson, B.J.: Autofocusing in computer microscopy: Selecting the optimal focus algorithm. Micros. Res. and Tech. 65(3), 139–149 (2004)
Sun, C.C., Moreno, I., Lo, Y.C., Chiu, B.C., Chien, W.T.: Collimating lamp with well color mixing of red/green/blue LEDs. Opt. Exp. 20(S1), 75–84 (2011)
Tian, L., Slaughter, D.C., Norris, R.F.: Outdoor Field Machine Vision identification of tomato seedlings for automated weed control. Transactions of the ASAE 40(6), 1761–1768 (1997)
van Gorkom, R.P., van As, M.A., Verbeek, G.M., Hoelen, C.G.A., Alferink, R.G., Mutsaers, C.A., Cooijmans, H.: Etendue conserved color mixing. In: Proceeding of SPIE, vol. 6670 (2007)
Vriesenga, M., Healey, G., Sklansky, J.: Colored illumination for enhancing discriminability in machine vision. J. of Vis. Comm. and Im. Rep. 6(3), 244–255 (1995)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical recipes in C, 2nd edn., pp. 289–290. Cambridge University Press, New York (1992)
He, X.F., Fang, F.: Flat-Panel Color Filter Inspection. Vis. Sys. Des., 20–22 (May 2011)
Yang, G., Nelson, B.J.: Micromanipulation contact transition control by selective focusing and microforce control. In: Proceeding of IEEE International Conference on Robotics and Automation, pp. 3200–3206 (2003)
Yap, P.T., Raveendran, P.: Image focus measure based on Chebyshev moments. Proceeding of Vision, Image and Signal Processing 151(2), 128–136 (2004)
Zabinsky, Z.B.: Random search algorithms. The Wiley Ency. of Oper. Res. and Manag. Sci. (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kim, H., Cho, K., Kim, S., Kim, J. (2014). Optimal RGB Light-Mixing for Image Acquisition Using Random Search and Robust Parameter Design. In: Barneva, R.P., Brimkov, V.E., Šlapal, J. (eds) Combinatorial Image Analysis. IWCIA 2014. Lecture Notes in Computer Science, vol 8466. Springer, Cham. https://doi.org/10.1007/978-3-319-07148-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-07148-0_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07147-3
Online ISBN: 978-3-319-07148-0
eBook Packages: Computer ScienceComputer Science (R0)