Weave pattern recognition by measuring fiber orientation with Fourier transform

Abstract

An effective method based on measuring the fiber orientation of yarn floats with two-dimensional Fourier transform (2-D FFT) is proposed to recognize the weave pattern of yarn-dyed fabric in the high-resolution image. The recognition process consists of four main steps: 1. High-resolution image reduction, 2.Fabric image skew correction, 3.Yarn floats localization, 4. Yarn floats classification. Firstly, the high-resolution image is reduced by the nearest interpolation algorithm. Secondly, the skew of the fabric image is corrected based on Hough transform. Thirdly, the yarn floats in the fabric image is localized by the yarns segmentation method based on the mathematical statistics of sub-images. Fourthly, the high-resolution image is corrected and its yarns are segmented successively according to the inspection information of the reduced image. The fiber orientations are detected by 2-D FFT, and the yarn floats are classified by k-means clustering algorithm. Experimental results and discussions demonstrate that, by measuring the fiber orientation of yarn floats, the proposed method is effective to recognize the yarn floats and the weave pattern for yarn-dyed, solid color, and gray fabrics.

Keywords:

• Yarn-dyed fabric
• weave pattern recognition
• yarn float
• fiber orientation measurement
• two-dimensional Fourier transform

Acknowledgments

This work was supported by the National Natural Science Foundation of China [61202310]; Research Fund for the Doctoral Program of Higher Education of China [20120093130001]; the Henry Fok Educational Foundation [141071]; the National Postdoctoral Fund Project [2013M541602]; the Postdoctoral Fund Project of Jiangsu Province [grant number 1301075C]; Prospective Industry University Research Project of Jiangsu Province [BY2013015-20]; the Postgraduate Research Project of Jiangsu Province [KYLX15_1180]; the Priority Academic Program Development of Jiangsu Higher Education Institutions.