Automatic assessment of smoothness grading for fabrics using a laser-ranging vision 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 cormnunity. 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 thesis, we describe the system and detail the steps of two validation studies. First, using histograms of the extracted features, we compare the output of the system among 26 different fabric samples of various color, type, texture, and construction. 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. Second, we apply the system to an area of research involving chemicals that aim to reduce wrinkling, called finishes. The results of the system in this case indicate that it has a much higher resolution of wrinkle characterization than is offered by the current grading system, as defined by the American Association of Textile Chemists and Colorists.