Performance of an Objective Fabric Pilling Evaluation Method

Abstract

In previous work, we established the principle of objective fabric pilling evaluation based on two-dimensional dual-tree complex wavelet transform (2DDTCWT) image reconstruction and non-linear classification using a neural network. This proof-of-principle work was performed using standard pilling test images. Here, we demonstrate the practical operation of the objective pilling evaluation method using a large set of real fabric pilling samples. We show that piling classification results from a trained multiple-layer perceptron neural network achieve a regression correlation of approximately 96% with the corresponding human expert pilling ratings.