Color is an important property to grade cotton. However, precise measurement and comprehensive evaluation of cotton color have not yet been carried out because of instrumental defect, colorful trash and less indicators. This work proposed an accurate method of color measurement and cotton grading. In order to remove the influence of trash on result, a novel trash detection method based on color features of defined three categories of trash and cotton was introduced. In order to improve the accuracy of trash detection, a method based on two-step threshold algorithm is proposed. The original images stored in sRGB format was transformed into binary images according to fixed thresholds and ‘Otsu’ algorithm. Compared to previous trash detection methods, the two-steps detection method was more suitable for cottons with different trash-content. Indicators expressed in Hunter color space were determined by the conversion referring to optical theory and light source. The feasibility of the proposed method was confirmed by comparing the present result with that obtained from standard instruments system. Furthermore, the grading indicators of cotton discussed in this paper suggested that the variation and distribution within a cotton sample should be considered in cotton grading, including a parameter redness or blueness (a). Our work would provide a much better approach to measure and evaluate color of cotton.
A color-separation algorithm was proposed to predict the length of each color fiber in mixed-wool fiber assemblies based on a red, green, and blue transmission image. In this work, mixed-wool fiber assemblies consisted of different color wool fibers and a digital color image was obtained by a scanner. The relative thickness of the fiber assemblies was measured based on the Beer-Lambert theory. The color-separation formula was constructed to calculate the quantity of each color fiber at every point of the mixed-wool top to achieve the relative linear density curve and the average length. A series of systematic experiments demonstrated high consistency with the reference relative linear density curve and average length and confirmed the validity of the color-separation formula. This algorithm could be used for quality detection and control of mixed-wool tops. It could be also extended to uniformity detection of other mixed-color fiber assemblies.
The quality of cashmere, such as color and length, determines its price and application. In the current cashmere inspection system, color and length are tested by visual assessment, which is a subjective, time- and labor-consuming process. Herein, the goal of this research is to develop a new method of testing cashmere color using image analysis, and to study the application of color in length measurement. During the color measurement, cashmere was prepared under two sample placement methods, and color features including RGB, XYZ and Lab obtained by the new method were compared with the standard. The calculation method of optical index used in length testing was determined based on theoretical and experimental analysis. Experiments show that fixed weight and pressure are suited for cashmere color measurement. In RGB space, the correlation coefficients ( R2) between the two devices were calculated and were 0.990, 0.995 and 0.996 for parameters R, G and B, respectively. Good agreement also exhibited in XYZ space, with R2 equal to 0.994, 0.996 and 0.999 for X, Y and Z, respectively. This confirmed the accuracy of the proposed color measurement method in RGB and XYZ space. Finally, an accurate fibrogram was obtained by the proposed conversion model for calculating optical index from color values, which is the key curve to testing cashmere length. This study emphasis on methodological aspects and the results acquired are regarded as preliminary, as the experiments studied compose the first stage of research on the exploration of the application of image analysis on cashmere color measurement.
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