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4 results on '"Chun-ao Wei"'

1. Adaptive superpixel segmentation and pigment identification of colored relics based on visible spectral images

Author

Shiwei Liu, Chun-ao Wei, Miaoxin Li, Xinyu Cui, and Junfeng Li

Subjects
Visible spectral image, Colored relics, Adaptive superpixel segmentation, Pigment identification, Standard reference database, Visible spectral reflectance, Fine Arts, Analytical chemistry, QD71-142
Abstract

Abstract This work explores the extraction of the spatial distribution and chemical composition information of pigments in colored relics through visible spectral images. An adaptive superpixel segmentation method is proposed to extract the spatial distribution information of pigments. Quadtree decomposition is applied to generate nonuniform initial seed points based on the image homogeneity. These seed points are used as the initial cluster centers in an extended simple linear iterative clustering (SLIC) algorithm for visible spectral images to create superpixels of varying sizes that reflect the homogeneity. Each superpixel is subsequently treated as an individual area in the colored relics, and a pigment identification method based on the visible spectral reflectance is proposed to identify the pigments in these areas. A standard reference database is constructed using samples that simulate the painting process of ancient wall paintings in the Mogao Grottoes. Geometric features, which are characterized by the linear combination of the normalized visible spectral reflectance, its slope and its curvature, are designed to represent the chemical composition of pigments. The geometric features of the superpixels are compared with those of the pigments in the database using the Euclidean distance to determine the pigments in each area of the colored relics. This work is expected to provide scientific guidance for pigment selection in the color restoration of colored relics.

Published
2024
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2. Applications of visible spectral imaging technology for pigment identification of colored relics

Author

Chun-ao Wei, Junfeng Li, and Shiwei Liu

Subjects
Visible spectral image, Colored relics, Pigment identification, Image segmentation, Pigment proportion prediction, Particle size, Fine Arts, Analytical chemistry, QD71-142
Abstract

Abstract Identifying pigments from colored relics is essential for their color restoration and for facsimile creation. A workflow for identifying pigment information is constructed based on visible spectral imaging technology, aligned with the drawing process of colored relics. This workflow includes three steps: boundary extraction, material identification and prediction of mixture proportions. The methods for segmenting visible spectral images, identifying chemical compositions, and predicting mixture proportions of pigments are extensively reviewed. Future research trends of these methods are also analyzed. The influence of the pigment particle size is currently underexplored but can be accomplished by multidisciplinary research.

Published
2024
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3. A modified single-constant Kubelka–Munk model for color prediction of pre-colored fiber blends

Author

Chun-ao Wei, Xiaoxia Wan, and Junfeng Li

Subjects
Work (thermodynamics), Polymers and Plastics, Color difference, Linearity, Kubelka munk, 02 engineering and technology, 021001 nanoscience & nanotechnology, 01 natural sciences, 010309 optics, Colored, Additive function, 0103 physical sciences, Fiber, 0210 nano-technology, Constant (mathematics), Biological system, Mathematics
Abstract

The goal of this work is to propose a modified single-constant Kubelka–Munk model for color prediction of pre-colored fiber blends. The original single-constant Kubelka–Munk model follows the Duncan’s additivity theorem, assuming that the optical coefficients of individual components in a turbid media are linear to their respective proportions. However, the linear assumption is invalid for the media of fiber blends due to the interactions between primary fibers, causing inaccurate color prediction of the model. Aiming at improving the accuracy, the single-constant Kubelka–Munk model was modified by employing a new additivity formula. The new additivity formula was established to achieve good linearity of the optical coefficients by modeling interactions between primary fibers as configurations. Cotton fibers blending samples were prepared to assess the color prediction accuracy. The average color difference of the proposed model was 0.91 CIEDE2000 unit, which was significantly better than that of the original model (~ 5.48). The results indicate the proposed model is much more suitable for color prediction of pre-colored fiber blends.

Published
2018
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4. Color prediction model for pre-colored fiber blends based on modified Stearns-Noechel function

Author

Junfeng Li, Chun-ao Wei, and Xiaoxia Wan

Subjects
010407 polymers, Color difference, Process Chemistry and Technology, General Chemical Engineering, Additive color, Linearity, 02 engineering and technology, Function (mathematics), 021001 nanoscience & nanotechnology, 01 natural sciences, Transfer function, 0104 chemical sciences, Colored, Fiber, 0210 nano-technology, Additive model, Algorithm, Mathematics
Abstract

The goal of this work is to propose a color prediction model for pre-colored fiber blends with high accuracy. The transfer function plays a vital role in an additive color mixing model. The better linearity between the transfer function and mass proportion, the higher accuracy of the model. However, the well-known Stearns-Noechel transfer function does not always hold good linearity, causing inaccurate color matching in many cases. Aiming at compensating the poor linearity, a new transfer function was established by minimizing the linear deviation. The proposed transfer function was applied to the additive model for color prediction of pre-colored fiber blends. The prediction accuracy of the proposed model was assessed by 44 samples. The average color difference was 0.63 CIEDE2000 unit, which was significantly better than the results of the Stearns-Noechel model (∼1.23) and the two-constant Kubelka-Munk model (∼1.11). These results indicate the proposed model has higher color prediction accuracy and can better satisfy the color requirement of practical production.

Published
2017
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