Research on Robustness of Color Device Characteristic Methods Based on Artificial Intelligence


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The nature of device color characteristic methods is the mutual conversion of device-dependent color space and device-independent color space. This paper does the comparative study on the robustness of some color space conversion methods which are based on fuzzy control, dynamic subspace divided BP neural network identification method, and fuzzy and neural identification method, by defining the robustness of color space conversion model and evaluation method. The result shows that the device color characteristic methods which are based on fuzzy and neural identification method can make the feature of BP neural network combine with fuzzy control to greatly improve the robustness of model.



Edited by:

Ouyang Yun, Xu Min, Yang Li and Liu Xunting




C. Zhi et al., "Research on Robustness of Color Device Characteristic Methods Based on Artificial Intelligence", Applied Mechanics and Materials, Vol. 262, pp. 65-68, 2013

Online since:

December 2012




[1] XU Yan-fang, LIU Wen-yao: Optics and Precision Engineering Vol. 12 (2004), pp.265-269.

[2] ZHANG Jiyan, LIN Haifeng: OPTICAL INSTRUMENT Vol. 30 (2008), pp.52-56.

[3] CAO Cong-jun, ZHOU Ming-quan: Computer Applications Vol. 28 (2008), pp.165-167.

[4] LIAO Ning-fan, YANG Wei-ping, ZENG Hua: Image and Graphic Vol. 5 (2000), pp.470-472.

[5] ZHI Chuan, SHI Yi: Journal of Xi'an University of Technology Vol. 25 (2005), p.338.

[6] Chuan Zhi, Ling Hua Guo: Advanced Materials Research Vol. 174 (2011), pp.97-100.

[7] Chuan Zhi, Shi-sheng Zhou, Yi Shi: Transactions of Beijing Institute of Technology Vol. 31 (2011), pp.722-726.

[8] Information on http: /baike. baidu. com/view/690876. htm.

[9] Huang Lin: Theoretical Basis of the Stability and Robustness (Science Press, China 2003).

[10] Information on http: /knowledgebase. datacolor. com/admin/attachments/color_differences. pdf.

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