Photo-scanning colorimetry of wood and transparent wood coatings

Forsthuber, Boris; Illy, Andreas; Grüll, Gerhard

doi:10.1007/s00107-014-0808-6

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…Additionally, the system can leverage advances in computer vision due to its use of a standard digital camera. For example, objects in raw image often need to be segmented before color inspection or classification; rice seeds need to be separated from its background before color identification [20], and the colors of early-and latewood of wood panels needs to be identified separately [4]. Furthermore, other visual inspection procedures, such as identifying defects, can be easily added to the same setup [26]- [28].…”

Section: Related Workmentioning

confidence: 99%

“…In the case of manufactured goods, there is a need to inspect their products to ensure the colors are even throughout and that there is minimal variation between batches [1], [2]. For natural products that are mined or harvested, such as ceramic tiles [3], wood panels [4], [5], producers often need to classify and sort them based on their color. Furthermore, color is one of the main attributes used by consumers when evaluating the safety and quality of food products [6]- [9].…”

mentioning

confidence: 99%

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Extraction and Classification of Surface Color Information Using Selective LED Illumination and a Statistical Neural Network

Eguchi,

Yamada,

Yeoh

et al. 2024

IEEE Access

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The color of a product is one of the main factors that affect consumer choices. However, it can be difficult to discern the subtle differences in color in order to inspect or classify products without expensive and specialized equipment. In this study, we proposed an approach where standard off-the-shelf components (digital camera and RGB LED lights) can be used to robustly classify products based on their color. By varying the color of the illumination, environment-independent information of the color of the object can be obtained. Then, using a Log-Linearized Gaussian Mixture Neural Network (LLGMN), a neural network based on a statistical model, the objects can be effectively and robustly classified based on their color without requiring a large number of training data. An experiment was performed to verify the effectiveness of this approach. 8 samples with only a subtle difference in color were prepared, and a prototype was developed to be used to classify these 8 samples. We demonstrate that we are able to classify the 8 samples an accuracy of 100% when there was no ambient light and were still able to maintain an accuracy of 94.1% when there was ambient light in the room.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Extraction and Classification of Surface Color Information Using Selective LED Illumination and a Statistical Neural Network

Eguchi,

Yamada,

Yeoh

et al. 2024

IEEE Access

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“…Flatbed scanners are commonly available at low cost [8]. Prior to the scanning, two identical circles with a diameter of about 32 mm were drawn on the wood surface according to the area covered by the colorimeter when measuring colors.…”

Section: Pretreatment and Scanning Of Wood Grainmentioning

confidence: 99%

“…The total color difference of the 8 mm circles between the digital wood grain and the original wood grain was calculated using the Equation ( 12) [8,26]:…”

Section: Calculation Of Chromatic Aberrationmentioning

confidence: 99%

“…Nordvik et al [7] studied the experienced difference between wood seen physically and the picture of it on a computer screen. Forsthuber et al [8] proved that there was a strong correlation between the values of the relative color change obtained by a spectrophotometer and by a calibrated photo-scanner, while only a moderate correlation was obtained comparing the absolute colorimetric values. In fact, it is because the median filter denoising conducted by the authors during the image analysis and the denoising mainly works on the blue channel.…”

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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Applications of IR Spectrum Measurement in Wood Research

Tolvaj

2023

Smart Sensors, Measurement and Instrumentation

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Photo-scanning colorimetry of wood and transparent wood coatings

Cited by 10 publications

References 17 publications

Extraction and Classification of Surface Color Information Using Selective LED Illumination and a Statistical Neural Network

Extraction and Classification of Surface Color Information Using Selective LED Illumination and a Statistical Neural Network

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

Applications of IR Spectrum Measurement in Wood Research

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