Seam detection of inhomogeneously textured fabrics based on wavelet transform

A large amount of seam detection for inhomogeneously textured fabrics makes those performing it fatigued, which leads to misjudgments by human vision, especially for the seam detection of patterned inhomogeneously textured fabrics. The traditional wavelet texture analysis is no longer applicable to fabrics with inhomogeneous textures and irregular patterns. In this paper, a novel mean weighting factor is proposed to obtain an optimized discriminant measure to detect the fabric seams. Firstly, the wavelet coefficients are extracted in individual decomposition levels. Then a mean weighting factor is calculated on the use of the difference of the coefficient values between two consecutive decomposition levels, and a better discriminant measure for seam detection is obtained. Lastly, a thresholding process is used to segment the seam information from the background. The experimental results show that the proposed approach effectively carries out the seam detection in the inhomogeneously textured fabric images.