Measuring and Analysis Colour of Birch Wood in Digital Picture

Hrčková, Mária; Koleda, Pavol; Koleda, Peter

doi:10.2478/mspe-2020-0024

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…The comparison of the scanned wood grain images before and after the correction is shown in Figure 6. According to the assessment guidelines on the classification of the overall color change used by Hrčková et al [19], the color change is visible by medium quality filter when the value of * is between 3 and 6. Since less than half of the wood specimens showed a change over 3 of * after the correction, the color difference in Figure 6 was not obvious for most of them.…”

Section: Color Comparison After the Chromatic Aberration Correctionmentioning

confidence: 99%

“…Nasir et al [18] used the L * a * b * color measuring system to develop a machine learning model for predicting the mechanical properties of artificially weathered fir, alder, oak, and poplar wood. Studies on the digital wood color such as the color analysis of digital Birch wood after the heat treatment [19] and those about the color image itself, such as the perceptually optimized enhancement of color in images using JND (Just Noticeable Difference) transform and color constancy [20] and the novel method on color image enhancement [21] have also been widely reported.…”

Section: Introductionmentioning

confidence: 99%

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A Modeling Approach on the Correction Model of the Chromatic Aberration of Scanned Wood Grain Images

Mao

Feng

2022

Coatings

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There always exists subjective and objective color differences between digital wood grain and real wood grain, making it difficult to replicate the color of natural timber. Therefore, we described a novel method of correcting the chromatic aberration of scanned wood grain to maximally restore the objective color information of the real wood grain. A point-to-point correction model of chromatic aberration between the scanned wood grain and the measured wood grain was established based on Circle 1 by adjusting the three channels (sR, sG, and sB) of the scanned images. A conversion of the color space was conducted using the mutual conversion formulas. The color change of the scanned images before and after the correction was evaluated through the L* a* b* color-mode-based ΔE* and the lαβ color-model-based CIQI (Color Image Quality Index) and CQE (Color Quality Enhancement). The experimental results showed that the chromatic aberration ΔE* between the scanned wood grain and the measured wood grain decreased and the colorfulness index CIQI of the scanned wood grain increased for most wood specimens after the correction. The values of ΔE* of the twenty kinds of wood specimens decreased by an average of 3.1 in Circle 1 and 2.3 in Circle 2, thus the correction model established based on Circle 1 was effective. The color of the scanned wood grain was more consistent with that of the originals after the correction, which would provide a more accurate color information for the reproductions of wood grain and had an important practical significance.

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Section: Color Comparison After the Chromatic Aberration Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Modeling Approach on the Correction Model of the Chromatic Aberration of Scanned Wood Grain Images

Mao

Feng

2022

Coatings

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“…The results obtained with the new method showed a real improvement in the recognition rate compared to a Bayesian classifier. In a study by Mária [7], a new wood color measurement method was verified using digital images in CIE L* a* b* color space birch color measurement and analysis. Syafinaz [8] studied the relationship between wood color and formaldehyde emissions from plywood of seven tropical hardwood species.…”

Section: Introductionmentioning

confidence: 99%

Color Classification and Texture Recognition System of Solid Wood Panels

Wang

Zhuang

Liu

et al. 2021

Forests

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Solid wood panels are widely used in the wood flooring and furniture industries, and paneling is an excellent material for indoor decoration. The classification of colors helps to improve the appearance of wood products assembled from multiple panels due to the differences in surface colors of solid wood panels. Traditional wood surface color classification mainly depends on workers’ visual observations, and manual color classification is prone to visual fatigue and quality instability. In order to reduce labor costs of sorting and to improve production efficiency, in this study, we introduced machine vision technology and an unsupervised learning technique. First-order color moments, second-order color moments, and color histogram peaks were selected to extract feature vectors and to realize data dimension reduction. The feature vector set was divided into different clusters by the K-means algorithm to achieve color classification and, thus, the solid wood panels with similar surface color were classified into one category. Furthermore, during twice clustering based on second-order color moment, texture recognition was realized on the basis of color classification. A sample of beech wood was selected as the research object, not only was color classification completed, but texture recognition was also realized. The experimental results verified the effectiveness of the technical proposal.

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Measuring and Analysis Colour of Birch Wood in Digital Picture

Cited by 2 publications

References 10 publications

A Modeling Approach on the Correction Model of the Chromatic Aberration of Scanned Wood Grain Images

A Modeling Approach on the Correction Model of the Chromatic Aberration of Scanned Wood Grain Images

Color Classification and Texture Recognition System of Solid Wood Panels

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