한국응용과학기술학회지 (Journal of the Korean Applied Science and Technology)

  • 제25권4호
  • /
  • Pages.438-445
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  • 2008
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  • 1225-9098(pISSN)
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  • 2288-1069(eISSN)
한국응용과학기술학회 (The Korean Society of Applied Science and Technology)
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목표색상 재현을 위한 페인트 안료 배합비율의 예측

Recipe Prediction of Colorant Proportion for Target Color Reproduction

  • 황규석 (부산대학교 화학공학과) ;
  • 박창원 (부산대학교 화학공학과)
  • 발행 : 2008.12.31
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초록

For recipe prediction of colorant proportion showing nonlinear behavior, we modeled the effects of colorant proportion of basic colors on the target colors and predicted colorant proportion necessary for making target colors. First, colorant proportion of basic colors and color information indicated by the instrument was applied by a linear model and a multi-layer perceptrons model with back-propagation learning method. However, satisfactory results were not obtained because of nonlinear property of colors. Thus, in this study the neuro-fuzzy model with merit of artificial neural networks and fuzzy systems was presented. The proposed model was trained with test data and colorant proportion was predicted. The effectiveness of the proposed model was verified by evaluation of color difference(${\Delta}E$).

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참고문헌

  1. F. W. Billmeyer and M. Saltzman, 'Principle of Color Technology' , John Willey & Sons (1981)
  2. S. M. Courtney, L. H. Finkel, and Gershon. Buchsbaum, A multistage Neural Network for Color Constancy and Color Induction, IEEE Transactions on Neural Networks, 6(4), 972 (1995) https://doi.org/10.1109/72.392259
  3. J. Spehl and M. Wolker, Application of Back-propagation Nets for Color Recipe Prediction as a Nonlinear Approximation Problem, IEEE., 60 (1994)
  4. J. M. Bishop and M. J. Bushnell, Genetic optimisation of Neural Network Architectures for Colour Recipe Prediction, Proc. Int. Conf. Neural Networks and Genetic Algorithms, 726 (1993)
  5. J. J. Warren and T. K. Eva, Using Neural Networks to Predict Dye Concentrations in Multi-Dye Mixtures, Textile Res. J., 63(9), 545 (1993) https://doi.org/10.1177/004051759306300907
  6. J. M. Bishop, M. J. Bushnell, and S. Westland, 'Application of neural networks to Computer Recipe Prediction', John Willey & Sons, New York, NY(1991)
  7. V. Venkatasubramanian, R. Vaidyanathan, and Y. Yamamoto, Process Fault Detection and Diagnosis Using Neural Networks-I. Steady-State Processes, Comp. chem. Eng., 14(7), 699 (1990) https://doi.org/10.1016/0098-1354(90)87081-Y
  8. D. H. Brainard and B. Wandell, Analysis of The Retinex Theory of Color Vision, J. Optical Soc. Amer., 3, 1651 (1986) https://doi.org/10.1364/JOSAA.3.001651
  9. J. C. Hoskins and D. M. Himmelblau, Artificial Neural Network Models of Knowledge Representation in Chemical Engineering, Comput. chem. Engng., 12, 881 (1988) https://doi.org/10.1016/0098-1354(88)87015-7
  10. W. Y. Huang and R. P. Lippmann, Comparisons between Neural Net and Conventional Classifiers, Proc. IEEE First Int. Conf. on Neural Networks, 4, 485 (1987)
  11. S. B. Chen, L. Wu, and Q. L. Wang, Self-Learning Fuzzy Neural Network for Control of Uncertain Systems With Time Delays, IEEE Trans. S.M.C., 27, 1 (1997)
  12. C. W. Anderson, Learning to Control An Inverted Pendulum Using Neural Networks, IEEE Control Syst. Mag., 9, 31 (1989) https://doi.org/10.1109/37.24809
  13. D. J. Burr, Experiments on Neural Net Recognition of Spoken and Written Text, IEEE Trans. Acoust., Speech, Signal Processing, 36(7), 1162 (1988) https://doi.org/10.1109/29.1643
  14. S. Abdulkadir, Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification, Expert Systems with Applications, 34, 2120 (2008) https://doi.org/10.1016/j.eswa.2007.02.032
  15. M. E. Yuksel, A. Basturk, and E. Besdok, Detail-Preserving Restoration of Impulse Noise Corrupted Images by a Switching Median Filter Guided by a Simple Neuro-Fuzzy Network, EURASIP Journal on Applied Signal Processing, 16, 2451 (2004)
  16. S. G. Kandi and M. A. Tehran, Color recipe prediction by Genetic Algorithm, Dyes and Pigments, 74, 677 (2007) https://doi.org/10.1016/j.dyepig.2006.04.012
  17. B. Xu and S. Lin, Automatic color identification in printed fabric images by a fuzzy-neural network, AATCC Review, 2(9), 42 (2002)
  18. E. Mizutani and K. Nishio, Multi-Illuminant Color Reproduction for Electronic Cameras Via CANFIS Neuro-Fuzzy Modular Network Device Characterization, IEEE TRANSACTIONS ON NEURAL NETWORKS, 13(4), 1009 (2002) https://doi.org/10.1109/TNN.2002.1021900
  19. J. S. Wang and C. S. George Lee, Self-Adaptive Neuro-Fuzzy Inference Systems for Classification Applications, IEEE TRANSACTIONS ON　FUZZY SYSTEMS, 10(6), 790 (2002) https://doi.org/10.1109/TFUZZ.2002.805880
  20. W. Stephen, I. Laura, and M. B. John, Kubelka-Munk or Neural Networks for Computer Colorant Formulation, 9th Congress of the International Color Association, Proceedings of SPI, 4421, 745 (2002)