An integrated method of feature extraction and objective evaluation of fabric pilling

A pilled fabric image consists of sub-images of different frequency components, and the fabric texture and the pilling information are in different frequency bands. Interference from fabric background texture affects the accuracy of computer-aided pilling ratings. A new approach for pilling evaluation based on the multi-scale two-dimensional dualtree complex wavelet transform (CWT) is presented in this paper to extract the pilling information from pilled fabric images. The CWT method can effectively decompose the pilled fabric image with six orientations at different scales and reconstruct fabric background texture and pilling sub-images. This study used an energy analysis method to search for an optimum image decomposition scale and dynamically discriminate pilling image from noise, fabric texture, fabric surface unevenness, and illuminative variation in the pilled fabric image. For pilling objective rating, six parameters were extracted from the pilling image to describe pill properties. A Levenberg-Marquardt backpropagation neural rule was used as a classifier to classify the pilling grade. The proposed method was evaluated using knitted, woven, and nonwoven pilled fabric images photographed with a digital camera.