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RISS 인기검색어
- 검색키워드
- 저자 : Ruru Pan (검색결과 2 건)
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Ge Zhang,Jian Zhou,Ruru Pan,Lei Wang,Weidong Gao 한국섬유공학회 2021 Fibers and polymers Vol.22 No.2
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 cottonfibers 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 leastsquares method from the prepared fiber blends samples. The color information of the 34 test blending samples including twoprimaryand three-primary was predicted by the improved Kubelka-Munk (K-M) double-constant theory. Comparing withthe measurement results, the minimum and maximum DE00 color differences were 0.215 and 1.890. The variance of colordifference for two-primary samples was 0.128 and for three-primary samples was 0.154, both were smaller than that obtainedby the K-M theory relative value method, the Stearns-Noechel (S-N) model, revised S-N models, and the Friele model. Theresults show that the improved K-M double-constant theory can be used to better predict the color blending effect of precoloredfibers.
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Objective Evaluation of Fabric Wrinkles Based on 2-D Gabor Transform
Kangjun Shi,Jingan Wang,Lei Wang,Ruru Pan,Weidong Gao 한국섬유공학회 2020 Fibers and polymers Vol.21 No.9
In order to establish an objective, stable and efficient wrinkle evaluation system for fabric wrinkle evaluation, amethod based on 2-D Gabor transform was proposed. Among this system, the directions of Gabor filter were determinedaccording to the range of amplitude response. Then a set of Gabor filters were obtained by selecting and optimizing thecentral frequency, the half peak bandwidth and the shape factor of the Gaussian surface. After Gabor transform by such filterbank, 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 extractedand used to train a support vector machine (SVM), which achieved 81.82 % evaluation accuracy on the 345 samples. Thetrained 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, theproposed method has higher classification accuracy. The comparison results indicate the Gabor amplitude feature proposedby this research has a high correlation with the fabric wrinkle grades.
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