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Abstract:
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Wrinkling caused in wearing and laundry procedures is one of the most important performance properties of a fabric . Visual examination performed by trained experts is a routine wrinkle evaluation method in textile industry , however , this subjective evaluation is time -consuming . The need for objective , automatic and efficient methods of wrinkle evaluation has been increasing remarkably in recent years .
In the present thesis , a wavelet transform based imaging analysis method was developed to measure the 2D fabric surface data captured by an infrared imaging system . After decomposing the fabric image by the Haar wavelet transform algorithm , five parameters were defined based on modified wavelet coefficients to describe wrinkling features , such as orientation , hardness , density and contrast . The wrinkle parameters provide useful information for textile , appliance , and detergent manufactures who study wrinkling behaviors of fabrics .
A Support -Vector -Machine based classification scheme was developed for automatic wrinkle rating . Both linear kernel and radial -basis -function (RBF ) kernel functions were used to achieve a higher rating accuracy . The effectiveness of this evaluation method was tested by 300 images of five selected fabric types with different fiber contents , weave structures , colors and laundering cycles . The results show agreement between the proposed wavelet -based automatic assessment and experts’ visual ratings . |