Objective assessment of pilling of nonwoven fabrics using the two dimensional discrete wavelet transform

Fabric pilling is a serious problem for the apparel industry, causing an unsightly appearance and premature wear. Woolen products are particularly prone to pilling. Recently, a process for production of woolen nonwoven apparel fabrics has been commercialized in Australia, and may lead to new markets for Australian wool. However, the success of such nonwoven fabrics will partly rely on their propensity to pill. A key element in the control of fabric pilling is the evaluation of resistance to pilling by testing. Resistance to pilling is normally tested in the laboratory by processes that simulate accelerated wear, followed by a manual assessment of the degree of pilling by an expert based on a visual comparison of the sample to a set of test images. To bring more objectivity into the pilling rating process, a number of automated systems based on image analysis have been developed. The authors previously proposed a new method of image analysis based on the two-dimensional discrete wavelet transform to objectively measure the pilling intensity for woven fabrics. This paper presents preliminary work in extending this method to nonwoven fabrics.

New methods for objective evaluation of fabric pilling by frequency domain image processing

Fabric pilling is a serious problem for the apparel industry. Resistance to pilling is normally tested by simulated accelerated wear and manual assessment of degree of pilling based on a visual comparison of the sample to a set of test images. A number of automated systems based on image analysis have been developed. The authors propose new methods of image analysis based on the two-dimensional wavelet transform to objectively measure the pilling intensity in sample images. Initial work employed the detail coefficients of the two-dimensional discrete wavelet transform (2DDWT) as a measure of the pilling intensity of woven/knitted fabrics.

This method is shown to be robust to image translation and brightness variation. Using the approximation coefficients of the 2DDWT, the method is extended to non-woven pilling image sets. Wavelet texture analysis (WTA) combined with principal components analysis are shown to produce a richer texture description of pilling for analysis and classification. Finally, employing the two-dimensional dual-tree complex wavelet transform as the basis for the WTA feature vector is shown to produce good automated classification on a range of standard pilling image sets.