CIE XYZ Spaces Research Articles

On the Acquisition of High-Quality Digital Images and Extraction of Effective Color Information for Soil Water Content Testing.

Soil water content (SWC) is a critical indicator for engineering construction, crop production, and the hydrologic cycle. The rapid and accurate assessment of SWC is of great importance. At present, digital images are becoming increasingly popular in environmental monitoring and soil property analysis owing to the advantages of non-destructiveness, cheapness, and high-efficiency. However, the capture of high-quality digital image and effective color information acquisition is challenging. For this reason, a photographic platform with an integrated experimental structure configuration was designed to yield high-quality soil images. The detrimental parameters of the platform including type and intensity of the light source and the camera shooting angle were determined after systematic exploration. A new method based on Gaussian fitting gray histogram for extracting RGB image feature parameters was proposed and validated. The correlation between 21 characteristic parameters of five color spaces (RGB, HLS, CIEXYZ, CIELAB, and CIELUV) and SWC was investigated. The model for the relationship between characteristic parameters and SWC was constructed by using least squares regression (LSR), stepwise regression (STR), and partial least squares regression (PLSR). Findings showed that the camera platform equipped with 45° illumination D65 light source, 90° shooting angle, 1900~2500 lx surface illumination, and operating at ambient temperature difference of 5 °C could produce highly reproducible and stable soil color information. The effects of image scale had a great influence on color feature extraction. The entire area of soil image, i.e., 3,000,000 pixels, was chosen in conjunction with a new method for obtaining color features, which is beneficial to eliminate the interference of uneven lightness and micro-topography of soil samples. For the five color spaces and related 21 characteristic parameters, RGB and CIEXYZ spaces and characteristic parameter of lightness both exhibited the strongest correlation with SWC. The PLSR model based on soil specimen images ID had an excellent predictive accuracy and the best stability (R2 = 0.999, RMSE = 0.236). This study showed the potential of the application of color information of digital images to predict SWC in agriculture and geotechnical engineering.

A color image analysis tool to help users choose a makeup foundation color

This paper presents an approach to predict the color of skin-with-foundation based on a no makeup selfie image and a foundation shade image. Our approach first calibrates the image with the help of the color checker target, and then trains a supervised-learning model to predict the skin color. In the calibration stage, We propose to use three different transformation matrices to map the device dependent RGB response to the reference CIE XYZ space. In so doing, color correction error can be minimized. We then compute the average value of the region of interest in the calibrated images, and feed them to the prediction model. We explored both the linear regression and support vector regression models. Cross-validation results show that both models can accurately make the prediction.

An investigation of how the texture surface of a fabric influences its instrumental color

AbstractIn this article, the influence of texture surface of a fabric on its instrumental color is investigated. While former studies have found it is difficult to establish a quantitative relationships between texture of fabric and its instrumental color (color difference and color attributes, such as lightness, chroma, and hue), this article investigates from a theoretical and empirical perspective the interaction between texture and color. Eighty four knitted cotton yarn dyed fabric samples in four color centers and 21 texture structures were used in this study. It is revealed that fabric samples with different texture surfaces define a set of lines with identical direction in the reflectance space, and thus the normalized reflectance curves of these samples are identical. In the CIEXYZ space, tristimulus values of these fabric samples define a line, and thus their chromaticity coordinates are constant. In the CIELAB space, however, linearity is lost due to the non‐linear transformation from the CIEXYZ space to the CIELAB space. The finding of this article has the potential to discount the influence of texture of a fabric on its color. Experiments show that the influence of texture on color for samples in the four color centers can be reduced by 79, 55, 71, and 57%, respectively comparing to the real measured color difference. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 472–482, 2015

Research on the Color-Difference Evaluation of Food Package Printing Matter Based on the Human Visual Characteristics

In order to improve for the inadequacy of traditional evaluation methods, this paper researched a kind of color-difference evaluation method for the food package printing matter based on human visual characteristics. Research method is as follow: the reference image and the target image were transformed to the opposite color space, and were dealt with the human eye Contrast Sensitive Function (CSF) spatial filter. The modified three opposite color component of images were transformed into the CIE XYZ space image after CSF spatial filtering processed; finally the image was transformed to the CIELAB space image. The color-difference of the target image was calculated according to color-difference formula. The color-difference result of image is the calculation result with human visual characteristics filtering processed; it combines subjective evaluation with objective evaluation. The research could provide practical and effective evaluation method for the field of food package printing matter.

Colour matching experiments with RGB‐LEDs

AbstractCIE colorimetry breaks down when lights produced by narrow band RGB‐LEDs are matched with broad‐band lights. A colour matching experiment was set up and matches in a number of parts of the chromaticity diagram have been made, to determine the magnitude of the discrepancy. Differences between visual and instrumental matches increase as one moves in the chromaticity diagram from yellowish white lights toward greenish and bluish lights.CIE TC 1‐36 recently suggested newly defined cone fundamentals: Applying a transformation of these to a space similar to the CIE XYZ space enables a much better prediction of the matches to be made. The difference between the visual match and its instrumental prediction decreases by a factor of two or even more.The use of a cone fundamental based colorimetric system is recommended for LED colorimetry. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 108–112, 2008

A comprehensive study on the colour of virgin olive oils and its relationship with their chlorophylls and carotenoids indexes (I): CIEXYZ non-uniform colour space

Up to 1700 samples of virgin olive oil from eight varieties at different ripening stages were analyzed for their colour, in terms of the CIEXYZ space, and pigments. The samples differed greatly in pigments contents (the chlorophylls index ranging from 0.15 to 61.96 mg/kg and the carotenoid one from 0.53 to 31.51 mg/kg) and therefore in colour, as it was inferred from the variations in X (16.19–91.39), Y (18.32–98.16), Z (0.17–81.14), purity of excitation (13.99–99.02), colorimetric purity (20.80–99.34) and dominant wavelength (566–575 nm). After exploring the relationships between the colour parameters and the indexes it was concluded that the purity of excitation and the colorimetric purity were better correlated with the carotenoid index than to the chlorophyll index and that λd was much poorly correlated to both. When multiple regression analyses were applied and the set of tristimulus values was considered altogether the best correlation was found with the carotenoid content.

Colorimetric characterization of imaging device by total color difference minimization

Colorimetric characterization is to transform the device-dependent responses to device-independent colorimetric values, and is usually conducted in CIEXYZ space. However, the optimal solution in CIEXYZ space does not mean the minimization of perceptual error. A novel method for colorimetric characterization of imaging device based on the minimization of total color difference is proposed. The method builds the transform between RGB space and CIELAB space directly using the downhill simplex algorithm. Experimental results showed that the proposed method performs better than traditional least-square (LS) and total-least-square (TLS) methods, especially for colors with low luminance values.