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The accuracy of color prediction results for pre-colored fiber blends is critical in the textile industry. In this paper, we attempt to investigate a feasible method for predicting the color of pre-colored fibers blends. Five pre-colored cotton fibers were divided into two groups, one for achromatic primaries (white and black) and one for chromatic primaries (red, blue, and yellow). Their respective absorption coefficient (K) and scattering coefficient (S) were calculated by the least squares method from the prepared fiber blends samples. The color information of the 34 test blending samples including two-primary and three-primary was predicted by the improved Kubelka-Munk (K-M) double-constant theory. Comparing with the measurement results, the minimum and maximum DE00 color differences were 0.215 and 1.890. The variance of color difference for two-primary samples was 0.128 and for three-primary samples was 0.154, both were smaller than that obtained by the K-M theory relative value method, the Stearns-Noechel (S-N) model, revised S-N models, and the Friele model. The results show that the improved K-M double-constant theory can be used to better predict the color blending effect of pre-colored fibers.

In order to establish an objective, stable and efficient wrinkle evaluation system for fabric wrinkle evaluation, a method based on 2-D Gabor transform was proposed. Among this system, the directions of Gabor filter were determined according to the range of amplitude response. Then a set of Gabor filters were obtained by selecting and optimizing the central frequency, the half peak bandwidth and the shape factor of the Gaussian surface. After Gabor transform by such filter bank, the amplitudes of different response spectrums were extracted, constructing a multi-dimensional feature vector. Finally, the feature vectors of the fabric image samples, whose wrinkle degrees were evaluated manually in advance, were extracted and used to train a support vector machine (SVM), which achieved 81.82 % evaluation accuracy on the 345 samples. The trained SVM was applied to evaluate the wrinkle degree of the fabric samples acquired in different illumination directions, and verified the stability of the proposed method to illumination environment. Compared with the existing method, the proposed method has higher classification accuracy. The comparison results indicate the Gabor amplitude feature proposed by this research has a high correlation with the fabric wrinkle grades.

In this paper, a novel method based on logical analysis is proposed to recognize the layout of color yarns of yarndyed fabric from the color pattern. The mathematic expressions of the color pattern and the layout of color yarns are introduced first, and then the fitness of the layout of color yarns to the color pattern is defined by the relation between them. The principle of the proposed recognition method begins with the assumption of the color of the first warp yarn in the color pattern. The whole algorithm procedure is then analyzed in detail with an actual sample color pattern. Experiments on some color patterns recognized from actual yarn-dyed fabrics, some color patterns simulated manually and some color patterns including error color information of floats prove that the method proposed in this paper is effective for detecting the layout of color yarns from the color pattern of yarn-dyed fabric and it has fault-tolerance ability in some degree. The research in the paper can be used to construct the whole recognition system of the parameters of yarn-dyed fabric.

According to the color yarns in the fabric, the yarn-dyed fabrics are divided into two categories: single-systemmelange color fabrics and double-system-melange color fabrics. The method for inspecting the density of double-systemmelange color fabrics is discussed in this study. By analyzing the pattern and color characters of double-system-melange color fabrics, color-gradient image is proposed to detect the density. The gray-projection method and correlation coefficient method are selected to locate the wefts and warps. With the help of Fourier low-pass filter, the positions of yarns in double-system-melange color fabric are found, and then the density can be obtained by counting the yarns in a unit length automatically. The experiment proved that the method proposed can detect double-system-melange color fabric density successfully.