The overall quality of a fabric is dependent on a number of factors. Among these is the fabric’s tendency to wrinkle after home laundering - referred to as smoothness. Wrinkle grading is a subjective process involving human graders who compare fabric samples to replicas, representing various degrees of wrinkling. This process is also operator dependent, expensive, and it lacks the ability to adequately describe the many subtle differences that exist between grades. Therefore, the textile industry needs an automated system that can describe wrinkles on a fabric surface in an objective and repeatable manner. In this paper, we describe a computer vision system developed in a previous work and examine the effectiveness of new features extracted from the wavelet domain independent mixture model and a landform classification technique. Shown to be useful in texture classification, features from the wavelet domain independent mixture model are measured based on the two-population characteristic of the wavelet domain. The second technique uses topographical analysis methods originally developed for geographical landform classification that have been successfully applied to digital elevation models of the Earth’s surface. These new measurements, representing quantitative descriptions of the surface of a fabric in both the frequency and spatial domains, are compared to the existing industry grading standard using a fuzzy classifier. Results show a good correlation with technicians’ grades.
A fabric's tendency to wrinkle is vitally important to the textile industry as it impacts the visual appeal of apparel. Current methods of grading this characteristic, called fabric smoothness, are very subjective and inadequate. As such, a quantitative method for assessing fabric smoothness is of the utmost importance to the textile community. To that end, we propose a laser-based surface-profiling system that utilizes a smart camera to sense the 3-D topography of fabric specimens. The system incorporates methods based on anisotropic diffusion and the facet model for characterizing edge information that ultimately relate to a specimen's degree of wrinkling. We detail the initial steps in a large-scale validation of this system. Using histograms of the extracted features, we compare the output of the system between two studies that total more than 200 fabric specimens. The results show that with the features used so far, this system is at least as good as the current American Association of Textile Chemists and Colorists (AATCC) smoothness grading system.
A fabric's tendency to wrinkle is vitally important to the textile industry as it impacts the visual appeal of apparels. Current methods of grading this characteristic, called fabric smoothness, are very subjective and inadequate. As such, a quantitative method for assessing fabric smoothness is of the utmost importance to the textile community. To that end, we have proposed a laser-based surface profiling system that utilizes a smart camera to sense the 3-D topography of the fabric specimens. The system incorporates methods based on anisotropic diffusion and the facet model for characterizing edge information that ultimately relate to a specimen's degree of wrinkling. In this paper, we detail the initial steps in a large-scale validation of this system. Using histograms of the extracted features, we compare the output of the system among 78 swatches of various color, type, and texture. The results show consistency among repeated scans of the same swatch as well as among different swatches taken from the same fabric sample. Also, since swatches taken from the same piece of fabric typically wrinkle similarly, this adds to the feasibility of the system. In other words, it adequately identifies and measures appropriate features of the wrinkles found on a sample.
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