A new robust recognition algorithm is proposed for fabric weave pattern recognition. The gray-level images of solid woven fabrics are captured by a color scanner and converted into digital files, then enhanced images are obtained by a gray-level morpho logical operation. Based on the interstices of yarns, warp and weft crossed areas are located, and four texture features of these areas are obtained by first-order and second- order statistics. Unsupervised decision rules for recognizing warp and weft floats are developed using a fuzzy c-means clustering method. The experimental materials include plain, twill, and satin woven fabrics. Experimental results demonstrate that three basic weave patterns can be clearly identified.
The high quality transparent conducting film (TCF) at a low sheet resistance of uniform and purified silver nanowires (AgNWs) have been successfully produced, the optoelectronic performance, which exceeds that of indium tin oxide (ITO).
A novel approach of repeat pattern segmentation is proposed for printed fabrics. Printed fabrics are captured by a color scanner and converted into full color digital files. To classify the color segmentation and pattern elements a fuzzy C-means (FCM) clustering algorithm and a specific cluster-validity criterion are used to obtain the pattern image of printed fabric. Then, the repeat pattern segmentation of the pattern image is established by Hough transform. The experimental results have shown that this systematic method is very suitable for the analysis of the repeat pattern of printed fabrics.
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