Cognition of "cool-looking" textile surface pattern on thin blue shirting fabrics by using wavelet transform.

Abstract

In general, visual features of textiles are characterized by fabric density, material, color and weave. However, theimpressi ons of the fabrics for human eyes are different, although the fabric density, material, color and weave are same. Surface patterns of the fabrics vary depending on the yarn's characteristics. And the surface patterns seem to be captured as visual feature through one of the human senses, “KANSEI”. Due to the “KANSEI”sense, people perceive some fabrics “look cool”or “look warm”regardless the actual density, material, color, or weave. In this study, the underlying assumption was that irregular parts offre quency components of the textile surfaces resulted “cool-looking”visual feature. In order to extract the irregular parts, wavelettransform was applied. First, warp and weft yarn's cycles in the textile images were investigated by the fast Fourier transform (FFT). Second, “mother wavelet”functions were constructed by using these cycles, and the wavelet transform with the “motherwa velet”functions was applied to the original images. Third, the wavelet-transformed images were converted into binary images.Information obtained in thi s way was used as the objective evaluation scale. Finally, the authors examined the correlation between objective evaluation scales of the frequency components and subjective impression of fabrics in order to verify the validity of themethod.