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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

Fabric cover factor is the ratio ofthe area covered by the yarns to the whole area ofthe fabric. In this work we propose a straightforward method to evaluate the fabric cover factor by automatic thresholding of a digital image of the fabric.

Fabric openness factor (OF) is the fraction of the web area that is uncovered by yarns. OF is a critical feature regarding the end-use performance of the fabric and should be accurately assessed.However, digital OF estimates yielded by image binarization algorithms differ among them depending on the criteria used, mainly due to ill-defined boundaries, thus precluding a PRACTICAL APPLICATIONSThis work addresses the problems that arise at the moment of measuring the area of a region with illdefined boundaries without any standard to control the process of measurement. Similar problems are also found in important fields such as medical imaging. In these cases, there are usually different digital methods that give rise to estimates of the magnitude which mismatch among them. To solve these differences, a more common approach is to use estimates provided by a panel of observers and consider them to be objective, regardless of the bias between subjective responses and actual values.However, visual estimates do not show either accuracy (because they are psychophysical measures) or precision (due to the variability among observers). To overcome these difficulties, we propose a procedure to validate a digital method through visual estimates provided by a panel of observers, taking into account the psychophysical models of Fechner and Stevens.

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