Algorithm for predicting the length of each color fiber in mixed-wool fiber assemblies based on the transmission image

Spot color is widely applied to printing and packing in modern industry, which can satisfy the individualization requirements and express the emotion of products. Color prediction is the core technique for spot color restoration. In this paper, a method that combines the least squares method and gravitation search algorithm is proposed to address the color prediction by using the absorption spectrum. Firstly, the spectral transmittance of the thin film with high transmission and low reflectance characteristics is researched to find the absorbance. Secondly, the least squares method is used to ascertain the primary colors of the spot color. Thirdly, an enhanced quantum gravitation search algorithm is designed to predict the spot color. The predicted results on the 30 spot colors show that the proposed method has higher accuracy in comparison with the three existed methods. The color differences between the prepared spot colors and the reproduced spot colors are all less than 3, in which 75% of the color differences are less 1 and 35% of the color differences are less 0.1. All the results confirm that the proposed method can predict the spot color accurately.

Section: Absorption Spectrum-based Color Matching Modelmentioning

confidence: 99%

Section: Absorption Spectrum-based Color Matching Modelmentioning

confidence: 99%

An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

Gao

Zhang

Zhou

et al. 2020

“…11,12 The Lambert-Beer (L-B) law has been widely adopted since 1941 for measuring the thickness of transparent media materials, 13,14 and then applied to determine each material's thickness and surface density in a multilayer color specimen. 15,16 However, the medium described by the L-B law is expected to be transparent and uniform due to it ignoring scattered and reflected light. 17,18 Therefore, the poor prediction accuracy of the L-B law was commonly observed in fiber assemblies in the textile industry as the fibers are not fully opaque or transparent.…”

mentioning

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

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.

“…Subsequently, many researchers have conducted studies on this method. 26,29,[36][37][38] However, the applied L-B law only involves absorption light in optical density calculation, resulting in significant errors in colored and thicker fiber beards for the effects of nonnegligible scattered and reflected light.…”

mentioning

confidence: 99%

Reflectivity and fluffiness coupled optical algorithm for colored cotton fibrogram of random-beard image method

et al. 2023

Insufficient studies of reflectance and fluffiness effects on optical density caused large derivation and limited the application of fibrogram methods in colored cotton fiber length tests for decades. A coupled optical method was proposed here to solve this issue, named reflectivity and fluffiness coupled optical algorithm of the random-beard image method. In this algorithm, a defined theoretical reflectance was declared with an iteration method for optical linear density on the optical analysis of the derived Kubelka–Munk law, which satisfied its assumption of excluding reflected light at the interface with air. Meanwhile, an apparent polynomial relationship was revealed between the actual density and optical densities of fiber assemblies at different fluffiness. Its accuracy on weighted mean fiber length was demonstrated, referred to as advanced fiber information system, using 10 kinds of dyed cotton slivers and 12 kinds of naturally colored lint fibers whose mean and maximum absolute errors are 0.59 mm and 1.6 mm, respectively. Furthermore, their excellent agreement on fibrograms and the weighted mean length was confirmed by Bland–Altman analysis, correlation analysis and the T-test. Finally, an exceptionally high linear correlation (r2 = 0.93) was achieved on their weighted mean length and had no statistical difference with insignificant deviation under a significance level of a = 0.01. This new algorithm, featured fast and low cost, could provide accurate mean fiber length of colored or dark cotton, exhibiting valuable guidance in the potential application of the fiber trade and the quality control of colored cotton fibers or primaries in blending assemblies and yarn.