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Classification of Color Matching Functions with the Method of Cluster Analysis |
HUANG Min, GUO Chun-li, HE Rui-li, XI Yong-hui |
School of Printing and Packaging Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China |
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Abstract Larger color discrimination difference exits among observers with normal color vision, especially those of different ages. In order to classify the cone fundamentals among normal color observers, the 108 color matching functions (CMFs) including 47 Stiles&Burch CMFs and 61 CMFs computed by CIE2006 model were respectively classified into 5 categories in (λ), (λ), (λ) three channels by using the k-medoids algorithm of clustering analysis method as well as the square of Euclidean distance and a total of 5×5×5=125 categories were generated. Taking the 108 CMFs as 108 “individual observers”, the 17 color centers recommended by CIE were displayed on the center of iPad and 108 CMFs were compared with 125 categorical functions, then the average of 17 colors’ CIEDE2000 color differences were calculated. Finally 10 categorical CMFs were selected from those 125 categories to represent the spectral response of human cone cells. The results indicated that 77.8% from the 108 “real observers” were satisfied, which regarded the obtained minimum color difference as the objective function. As the target colors, CIE recommended 5 colors (gray, red, yellow, green, blue) were presented on the iPad and 30 young observers aged 20 to 25 and 17 old observers aged 61 to 74 were organized to match 5 color centers correspondingly on Quato display. Therefore, 158 groups, 790 color data (each group includes 5 color centers) were obtained and then categorized by computing CIEDE2000 color difference using 10 categorical CMFs. The CMFs possessing the minimum color difference value were assigned as the corresponding classification of 158 observers and finally 8 out of 10 categories were selected and named BIGC-1, BIGC-2, …, BIGC-8, which were used to test the results of paired comparison experiment based on metameric color samples in our previous study. The obtained results show that BIGC-3 CMFs worked well for young observers while BIGC-5 CMFs were suitable to old observers. Additionally, the calculated results of STRESS value were also lower than the results computed by other CMFs.
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Received: 2018-12-21
Accepted: 2019-04-28
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[1] CIE. CIE Publication 15: 2004. Central Bureau of the CIE, Vienna, 2004.
[2] CIE. CIE Technical Report. 2006. 170.
[3] Burgos-Fernández F J, Vilaseca M, Perales E, et al. Opt. Appl.,2016, 46:117.
[4] Asano Y, Fairchild M D, Blondé L,et al. Color Res. Appl.,2016, 41:530.
[5] HUANG Min, HE Rui-li, SHI Chun-jie, et al(黄 敏, 何瑞丽, 史春洁,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析). 2018, 38(7): 2241.
[6] Asano Y, Fairchild MD, Blonde L, et al. Proceedings of the IS&T/SID Color Imaging Conference, Society for Imaging Science and Technology, Boston, MA,2014. 1.
[7] HUANG Min, HE Rui-li, GUO Chun-li, et al(黄 敏, 何瑞丽, 郭春丽,等). Acta Optica Sinica(光学学报),2019,39(1): 0133001.
[8] Abhijit Sarkar. Identification and Assignment of Colorimetric Observer Categories and Their Applications in Color and Vision Science. Rennes, France Université De Nantes,2011.
[9] Stiles W S, Burch J M. Journal of Modern Optics,1959,6: 1.
[10] Johnson R, Wichern D. Handbook of Applied Multivariate Statistical Analysis (Pearson Education International, NJ), USA,2007, 6th ed.
[11] Witt K. Color Research & Application,1995, 20(6): 399.
[12] HUANG Min, HE Rui-li, GUO Chun-li, et al(黄 敏, 何瑞丽, 郭春丽,等). Acta Optica Sinica(光学学报),2018, 38(3): 033301.
[13] Garcia P A, Huertas R, Melgosa M, et al. J. Opt. Soc. Am. A,2007, 7:1823. |
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