A computer vision-based system for automatic detection of misarranged warp yarns in yarn-dyed fabric. Part II: warp region segmentation. Issue 9 (2nd September 2019) - Zhang, Jie; Wang, Jingan; Pan, Ruru
Abstract: This series of studies aim to develop a computer vision-based system for automatic detection of misarranged color warp yarns to replace manpower and improve efficiency. Based on the warp yarn segmentation and fabric image stitching methods presented in Part I, this paper proposes a stepwise segmentation method of warp regions, as a core of the developed computer vision-based system, to detect the layout of color yarns for yarn-dyed fabrics automatically. The proposed framework consists of two main components: rough warp region segmentation and precise warp region merging which are realized by analyzing correlation coefficient of color histograms among segmented warp yarns and warp regions successively. The proposed method has been evaluated on 543 fabric images of four fabric samples consisting of 5533 warp regions, and experimental results show that the proposed method can realize the warp region segmentation in yarn dyed fabrics with the average accuracy of 99.47%.