Research on colored yarns for  a full color phase mixing model  and the Stearns–Noechel color  prediction approach

Sun, Xianqiang; Xu, Qingchen; Shen, Jiajia; Xu, Zhiwu

doi:10.1177/00405175221115887

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…It is widely used to predict mixed colors of textile fiber materials. 16,17 Assuming that the reflectance values of the three colored fibers selected from the seven primary-color fibers are R k i α µ ( , , ), R k i β µ ( , , ) and R k i γ µ ( , , ), the mixing ratios are c i 1 ( , µ), c i 2 ( , µ) and c i 3 ( , µ), respectively, and the reflectance value of the formed mélange yarn is R k i b ( , , µ), the S-N color prediction model can be expressed as equation (11).…”

Section: Construction Of a Modified S-n Color Prediction Model Based ...mentioning

confidence: 99%

Construction of grid color mixture model of seven primary-color and modified Stearns-Noechel color matching algorithm for color prediction of full-color-gamut rotor melange yarn

Zhu,

Xue,

Chen

et al. 2023

Journal of Engineered Fibers and Fabrics

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In this paper, the full-color-gamut grid color mixture model containing 601 grid points is constructed by ternary double coupling blending of seven primary-color fibers, and the spinning method of full-color-gamut melange yarn is given by combining with three-channel NC rotor spinning technology. A modified S-N color prediction model was constructed by selecting 55 uniformly distributed grid points for yarn and fabric production from the full-color-gamut grid color mixture model as samples for solving the reflectance conversion coefficients. On this basis, the method of predicting the color value of a melange yarn based on its primary-color fiber composition and blending ratio and predicting the primary-color fiber composition and blending ratio based on the color value of a melange yarn using the parameters of the nearest sample grid point is proposed, and six samples with different blending ratios in six color mixing regions of the full-color-gamut grid color mixture model are designed for validation. The results showed that the average color difference between the predicted color and the actual color of the melange yarn is 1.15, the predicted primary-color fiber composition of the melange yarn is consistent with the actual composition, and the average error between the predicted blending ratio and the actual blending ratio is 3.95%. The method proposed in this paper can effectively predict the color value and blending ratio of melange yarn.

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Section: Construction Of a Modified S-n Color Prediction Model Based ...mentioning

confidence: 99%

Construction of grid color mixture model of seven primary-color and modified Stearns-Noechel color matching algorithm for color prediction of full-color-gamut rotor melange yarn

Zhu,

Xue,

Chen

et al. 2023

Journal of Engineered Fibers and Fabrics

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“…Research methods that can be integrated include deep learning and machine learning. Color, feature, and texture are important factors that play a role in the image retrieval process, while algorithm and model are key tools used in image retrieval technology [14]. The co-occurrence analysis of keywords in the field of color-based image retrieval is visualized in Fig.…”

Section: Abstract Co-occurrence Analysismentioning

confidence: 99%

Visual Analysis on the Research of Color-based Image Retrieval

Su,

2023

FHSS

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This study retrieved relevant literature on color-based image retrieval in the Web of Science core database, and used bibliometrics and CiteSpace 6.1.R2 software to visually analyze the authors, institutions, and keywords of literature published in this field from 2005 to 2023. This study summarized the current status and development trend of color-based image retrieval and combined information extraction to select and include 716 articles for analysis. The results showed that: (1) In the last 20 years, the number of publications in this field has gone through a slow budding phase, a fluctuating growth phase, and an explosive growth phase; research in this area has been highly active in recent three years. (2) The authors of this field have a relatively even distribution of publications and some authors have formed close collaborative groups. (3) The research institutions mainly include research institutes and comprehensive universities, which have close collaborations with each other. (4) Co-occurrence and clustering analysis of keywords showed that research in this field is mainly focused on the main paths, research methods, and technological tools of color-based image retrieval, as well as key objects and potential influencing factors in the technological process. (5) Emerging keywords analysis predicted that more precise color description and extraction technology will be the focus of future research. In conclusion, color-based image retrieval is still in a hot research and technology explosion phase, and cooperation between countries, institutions, and authors should be strengthened to conduct more in-depth research.

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“…In recent years, blending yarn has been of wide interest because of its green ecological production process, 1,2 rich color layers, and hazy three-dimensional effects. [3][4][5] Hence, its quality control and technical design in color and mechanical properties played vital roles and were mainly determined by the masses of primary fibers in mixed fiber assemblies, 6,7 especially in the processing of supplied yarns or fabrics.…”

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confidence: 99%

Modified addition theorem on Kubelka-Munk transmission law for mass prediction of each primary in two-mixed cotton fiber blends

Wu,

Lu,

et al. 2024

Textile Research Journal

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Unclear light propagation in mixed fibers has resulted in unachievable masses of primary fibers, in particular thin two-mixed fiber assemblies for decades. Hereby, a modified mixed medium addition theorem of the Kubelka-Munk transmission model is proposed to address this issue. This theorem was constructed by introducing a transfer function in the common ones on scattering coefficients, and such a transfer function takes both the mass proportion of each component and the reflectivity of mixed material into consideration. This opens up a new avenue for understanding the possible light mechanism of color mixed fiber assemblies. The proposed method demonstrated excellent accuracy in determining the primary masses of 48 specimens, with an average mean absolute error of only 2.23 mg. In comparison, the Lambert-Beer law yielded a much higher average mean absolute error of 8.66 for all types of blended samples. Such superiority was particularly obvious in white-black mixed samples, probably due to its comprised reflectance. In brief, this proposed theorem shows a high-quality prediction accuracy level in fiber mass and can be expanded to detect and control mixed fibers, as well as fiber length and uniformity detection through further study.

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Research on colored yarns for a full color phase mixing model and the Stearns–Noechel color prediction approach

Cited by 7 publications

References 23 publications

Construction of grid color mixture model of seven primary-color and modified Stearns-Noechel color matching algorithm for color prediction of full-color-gamut rotor melange yarn

Construction of grid color mixture model of seven primary-color and modified Stearns-Noechel color matching algorithm for color prediction of full-color-gamut rotor melange yarn

Visual Analysis on the Research of Color-based Image Retrieval

Modified addition theorem on Kubelka-Munk transmission law for mass prediction of each primary in two-mixed cotton fiber blends

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