Fabric smoothness evaluation using the wavelet domain independent mixture model and a landform classification technique

Abstract

The overall quality of a fabric is dependent on a number of factors. Among these is the fabric’s tendency to wrinkle after home laundering - referred to as smoothness. Wrinkle grading is a subjective process involving human graders who compare fabric samples to replicas, representing various degrees of wrinkling. This process is also operator dependent, expensive, and it lacks the ability to adequately describe the many subtle differences that exist between grades. Therefore, the textile industry needs an automated system that can describe wrinkles on a fabric surface in an objective and repeatable manner. In this paper, we describe a computer vision system developed in a previous work and examine the effectiveness of new features extracted from the wavelet domain independent mixture model and a landform classification technique. Shown to be useful in texture classification, features from the wavelet domain independent mixture model are measured based on the two-population characteristic of the wavelet domain. The second technique uses topographical analysis methods originally developed for geographical landform classification that have been successfully applied to digital elevation models of the Earth’s surface. These new measurements, representing quantitative descriptions of the surface of a fabric in both the frequency and spatial domains, are compared to the existing industry grading standard using a fuzzy classifier. Results show a good correlation with technicians’ grades.