Identifying Fabric Structures with Fast Fourier Transform Techniques

摘要

The Fast Fourier Transform (FFT) plays a very important role in image processing and pattern recognition. Since a woven fabric consists of regular repeating units, the FFT is particularly useful for analyzing periodicity, directionality, and spacing of repeating units in the fabric. This paper describes procedures for applying FFT techniques in image processing to identify weave pattern, fabric count, yarn skewness, and other structural characteristics of woven fabrics. A color scanner is used to digitize fabric images (two-dimensional functions in a spatial domain), and a customized software package is used to apply the FFT to the images. A power spectrum image is derived from the FFT of an image, and considered a two-dimensional function in a frequency domain. Peaks in the power spectrum image stand for frequency terms of periodic elements, from which basic weave patterns (e.g., plain, twill, satin, etc.) can be recognized. A radial function and an angular function, derived from the power spectrum, are used to measure the coarseness and directionality of the periodic elements. By selecting power peaks in a certain direction to reconstruct the image, warp or weft elements can be extracted to facilitate fabric count and skewness measurements. Fourier filtering, that is, image filtering in the frequency domain, is used to suppress noise and to select features that have a certain range of frequencies. Fabrics with various weave patterns and yarn counts are tested using the FFT techniques.