Dynamic color restoration method in real time image system equipped with digital image sensors

Chiu, Li-Cheng; Fuh, Chiou-Shann

doi:10.1080/02533839.2010.9671614

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…, ) under the certain transformation Γ RGB→ * * * . The mapping coefficient matrix Φ RGB→ * * * with 4 and 11 terms is obtained by polynomial regression using (12)- (18). Using the mapping matrix Φ RGB→ * * * , the assumed tristimulus value of captured color patches ( , , ) ( = 1, 2, .…”

Section: The Polynomial-based Regressionmentioning

confidence: 99%

“…Several color calibration algorithms are proposed for various tasks in different device-independent color spaces. The most commonly used methods include the Polynomial-Based Regression, the Neural Network mapping algorithms, the Support Vector Regression, and the Ridge Regression [18][19][20][21][22][23][24][25][26][27][28][29]. Moreover, these color calibration methods have been applied into the general imaging devices [16,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33], such as digital cameras [16,[18][19][20][21][25][26][27], digital colposcopy [22], scanners [23,24,30], printers and cathode ray tube/liquid crystal display (CRT/LCD) monitors [23,28], and tristimulus colorimeter [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Color Calibration for Colorized Vision System with Digital Sensor and LED Array Illuminator

Zhu

Song

Luo

et al. 2016

Active and Passive Electronic Components

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Color measurement by the colorized vision system is a superior method to achieve the evaluation of color objectively and continuously. However, the accuracy of color measurement is influenced by the spectral responses of digital sensor and the spectral mismatch of illumination. In this paper, two-color vision system illuminated by digital sensor and LED array, respectively, is presented. The Polynomial-Based Regression method is applied to solve the problem of color calibration in the sRGB andCIE L⁎a⁎b⁎color spaces. By mapping the tristimulus values from RGB to sRGB color space, color difference between the estimated values and the reference values is less than3ΔE. Additionally, the mapping matrixΦRGB→sRGBhas proved a better performance in reducing the color difference, and it is introduced subsequently into the colorized vision system proposed for a better color measurement. Necessarily, the printed matter of clothes and the colored ceramic tile are chosen as the application experiment samples of our colorized vision system. As shown in the experimental data, the average color difference of images is less than6ΔE. It indicates that a better performance of color measurement is obtained via the colorized vision system proposed.

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Section: The Polynomial-based Regressionmentioning

confidence: 99%