The periodic woven structures of fabrics can be defined on the basis of the convolution theorem. Here an elementary unit with the minimum number of thread crossings and a nonrectangular two-dimensional comb function for the pattern of repetition is used to define woven structures. The expression derived is more compact than the conventional diagram for weaving, and the parameters that one needs to determine a given fabric can easily be extracted from its Fourier transform. Several results with real samples of the most common structures-plain, twill, and satin-are presented.

A method of image analysis is proposed for detection of local defects in materials with periodic regular texture. A general improvement and enlargement of vision system capabilities for versatility, full automatism, computational efficiency, and robustness in their application to the industrial inspection of periodic textured materials is pursued. In the proposed method, a multiscale and multiorientation Gabor filter scheme that imitates the early human vision process is applied to the sample under inspection. The designed algorithm automatically segments defects from the regular texture. A variety of examples of fabric inspection are presented. In all of them defects are successfully segmented from the texture background.

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A Fourier-domain-based recognition technique is proposed for periodic and quasiperiodic pattern recognition. It is based on the angular correlation of the moduli of the sample and the reference Fourier spectra centered at the maximum central point. As in other correlation techniques, recognition is achieved when a high correlation peak is obtained, and this result occurs when the two spectra coincide. The angular correlation is a one-dimensional function of the rotation angle. The position of the correlation peak indicates the rotation angle between two similar patterns in the original images. Some optimizations for the discrete calculation of the Fourier-domain-based angular correlation are also proposed. Some applications of this technique to web inspection tasks, such as pattern recognition and classification, damaged web evaluation, and detection of defects, are presented and discussed.

Digital image processing techniques are applied to perform an automatic method for the objective measure of woven fabric's cover factor. Based on a frequency domain analysis, digital images of woven fabric samples, obtained with a camera assembled to a microscope, are cropped to enclose the maximum integer number of warp and weft periods and leveled for non-uniform illumination. Posterior thresholding, designed to perform suitably for both high and low cover factor fabric samples, gives rise to the objective value. The method has been applied to three different sets of samples manufactured in plain weave, with known yarn numbers and thread counts. Cover factors obtained by this method show good correlation with those obtained by a set of visual observers and are consistent with woven fabric parameters: yarn numbers (tex) and thread counts (yarns/cm). The procedure can be useful to monitor mean cover factor as well as cover factor variability in fabric batches. It does not require sophisticated equipment and can be straightforwardly implemented in a textile analysis laboratory. Keywords-Image analysis, cover factor, woven fabrics CF is a basic construction parameter of woven cloth related to its end use performance [2,3]. Weaving efficiency, fabric quality [4,5], thermo-physiological comfort of garments, air permeability [6] as well as protection against Ultra Violet Radiation [7] are features strongly related to CF. It is a fundamental feature of multiple base fabrics used in the elaboration of