Abstract
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.
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