Cathode-Ray Tube Color Perceptible Color Difference Threshold Evaluation in Visual Color Matching

2008 ◽  
Vol 28 (3) ◽  
pp. 599-603 ◽  
Author(s):  
黄敏 Huang Min ◽  
廖宁放 Liao Ningfang ◽  
刘浩学 Liu Haoxue ◽  
徐艳芳 Xu Yanfang
1976 ◽  
Vol 20 (2) ◽  
pp. 269-274 ◽  
Author(s):  
Masumi Ibusuki ◽  
Aiko Kato
Keyword(s):  

2021 ◽  
pp. 004051752110408
Author(s):  
Ruihua Yang ◽  
Chuang He ◽  
Bo Pan ◽  
Zhuo Wang

The color-matching model is conducive to expanding the scope of application of colorful fabrics and can speed up the achievement of intelligent production. To solve the problem in which the existing color-matching system of intelligent colored spun yarn cannot be applied to the digital rotor-spinning products of dope dyed viscose fiber, 66 types of mélange yarn were spun with a digital rotor-spinning frame using red, yellow, and blue dope dyed viscose fibers at a ratio gradient of 10%. Furthermore, the knitted fabric samples were produced using a circular machine. Meanwhile, a Datacolor 650 spectrophotometer was used for color testing, and the experimental results were recorded. Based on the color-matching model of the Kubelka–Munk theory, a color-matching model is built based on the experimental results. In addition, the accuracy of the model was analyzed and verified using the least-squares and relative value methods. The results show that, compared with the relative value method, the color-matching model constructed using the absorption coefficient K value and scattering coefficient S value calculated based on the least-squares approach is more accurate. The error between the predicted ratio of the test sample and the actual ratio was only 0.0979, the average color difference was only 0.465, and there were no visible differences between the predicted color of the sample and the actual color.


2004 ◽  
Vol 22 (3) ◽  
pp. 534-537 ◽  
Author(s):  
Andrés F. López Camelo ◽  
Perla A. Gómez

Color in tomato is the most important external characteristic to assess ripeness and postharvest life, and is a major factor in the consumer's purchase decision. Degree of ripening is usually estimated by color charts. Colorimeters, on the other hand, express colors in numerical terms along the L*, a* and b* axes (from white to black, green to red and blue to yellow, respectively) within the CIELAB color sphere which are usually mathematically combined to calculate the color indexes. Color indexes and their relationship to the visual color classification of tomato fruits vine ripened were compared. L*, a* and b* data (175 observations from eleven cultivars) from visually classified fruits at harvest in six ripening stages according to the USDA were used to calculate hue, chroma, color index, color difference with pure red, a*/b* and (a*/b*)². ANOVA analysis were performed and means compared by Duncan's MRT. Color changes throughout tomato ripening were the result of significant changes in the values of L*, a* and b*. Under the conditions of this study, hue, color index, color difference and a*/b* expressed essentially the same, and the color categories were significantly different in terms of human perception, with hue showing higher range of values. Chroma was not a good parameter to express tomato ripeness, but could be used as a good indicator of consumer acceptance when tomatoes are fully ripened. The (a*/b*)² relationship had the same limitations as chroma. For vine ripened fruits, hue, color index, color difference and a*/b* could be used as objective ripening indexes. It would be interesting to find out what the best index would be if ripening took place under inadequate conditions of temperature and ilumination.


2002 ◽  
Vol 27 (6) ◽  
pp. 399-420 ◽  
Author(s):  
Hitoshi Komatsubara ◽  
Shinji Kobayashi ◽  
Nobuyuki Nasuno ◽  
Yasushi Nakajima ◽  
Shuichi Kumada
Keyword(s):  

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