Optimal Learning Samples for Two-Constant Kubelka-Munk Theory to Match the Color of Pre-colored Fiber Blends

Junfeng Li; Dehong Xie; Miaoxin Li; Shiwei Liu; Chun'Ao Wei

doi:10.3389/fnins.2022.945454

Optimal Learning Samples for Two-Constant Kubelka-Munk Theory to Match the Color of Pre-colored Fiber Blends

Front Neurosci. 2022 Jul 1:16:945454. doi: 10.3389/fnins.2022.945454. eCollection 2022.

Authors

Junfeng Li¹, Dehong Xie², Miaoxin Li¹, Shiwei Liu¹, Chun'Ao Wei¹

Affiliations

¹ School of Packaging and Printing Engineering, Henan University of Animal Husbandry and Economy, Zhengzhou, China.
² College of Information Science and Technology, Nanjing Forestry University, Nanjing, China.

Abstract

Due to the dyeing process, learning samples used for color prediction of pre-colored fiber blends should be re-prepared once the batches of the fiber change. The preparation of the sample is time-consuming and leads to manpower and material waste. The two-constant Kubelka-Munk theory is selected in this article to investigate the feasibility to minimize and optimize the learning samples for the theory since it has the highest prediction accuracy and moderate learning sample size requirement among all the color prediction models. Results show that two samples, namely, a masstone obtained by 100% pre-colored fiber and a tint mixed by 40% pre-colored fiber and 60% white fiber, are enough to determine the absorption and scattering coefficients of a pre-colored fiber. In addition, the optimal sample for the single-constant Kubelka-Munk theory is also explored.

Keywords: Kubelka-Munk theory; absorption coefficient; color matching; fabric; mixing theory; pre-colored fiber; scattering coefficient.