Warp-knitted fabric defect segmentation based on the non-subsampled wavelet-packet-based Contourlet transform

Abstract

In the work, the Contourlet transform is modified for a more complex situation in order to obtain a subtler decomposition of the warp-knitted fabric image. The new Contourlet transform is named the non-subsampled wavelet-packet-based Contourlet transform (NWPCT) and it consists of wavelet-packet transform and a non-subsampled directional filter bank. Firstly, the fabric image is processed by means of wavelet-packet transform with segmented threshold de-noising to acquire the subtle frequency coefficients. Secondly, the more elaborate directional coefficients will be obtained by decomposing the wavelet-packet coefficients with non-subsampled directional filter bank. Then the directional coefficients with higher energy are chosen to reconstruct the wavelet-packet coefficients. Finally, the iterative threshold method and object operation based on morphology are applied to segment the defect profile. The final experimental result demonstrates that NWPCT has excellent properties to segment out the defects (broken wrap, oil and width barrier). The defect profile is distinct enough for the further work concerning warp-knitted fabric defect recognition.