Akademik Veri Yönetim Sistemi
JOURNAL OF THE TEXTILE INSTITUTE, cilt.110, sa.1, ss.105-116, 2019 (SCI-Expanded)
Image processing has become a tremendous tool for various fields of applications as well as for textile manufacturing industry in recent years. Inspection of fabric density is one of the major issues for fabric manufacturers in textile industries. In this study, an image processing method comprising of linear and nonlinear techniques for automatic inspection of warp and weft yarn density of fabrics has been proposed. By avoiding common problems of linear filtering such as blurring and localization, anisotropic diffusion filtering has been applied as preprocessing operation to enhance the edge region/boundaries between adjacent yarns of the fabric images. We conjecture that given a skewed gray level image, the number of peaks in the gray line profile of the image is minimized if the image is rotated in such a way that the inter-spaces between yarns are aligned with the vertical axis. Gabor filter, an orientation-sensitive filter, is applied to the skewed image at that angle to boost the edges between inter-spaces. The number of warp and weft yarn density has been inspected by applying gray line profile method. Simulations have been done on a wide range of fabric image data set. The results have shown that nonlinear and steered filters made a contribution to the performance of the method. The number warp and weft yarn densities are determined with an accuracy rates above 90%.