Warp Weave Texture Feature Recognition Based on Autocorrelation Function

In this thesis, based on the weft organization features parameters analysis results, according to warp yarn arrangement features, apply correlation coefficient and autocorrelation function in weft weave texture feature recognition, establish weft cell image, analysis the weft cell image correlation coefficient, find out the number of weft circle, set up same kind weft cell image of the same phase of weft unit, calculate the warp brightness values of the same kind weft cell image, distinguish the weft points area and warp points area through luminance signal analysis, analyze the tissue points area changes between the adjacent similar weft cell, and determine the location and density of warp yarn. Experiments prove that the Fourier-transform method is feasible and has considerable accuracy.