A novel method for weft and warp yarn segmentation in multicolour yarn-dyed fabric images

This paper proposes a novel method for segmentation of weft and warp yarns in multicolour yarn-dyed fabric images. A multicolour yarn-dyed fabric is cross-woven by weft and warp yarns with different colours. When a multispectral imaging system is used to measure the colour of a multicolour yarn-dyed fabric image, its weft and warp yarns need to be detected before analysing their colours. Detection of interstices between weft and warp yarns is firstly conducted. A modified K-means clustering approach is then utilised to separate weft and warp yarns. The number of clusters is fixed to 2. The metric to measure the distance between a pixel and the mean of a cluster is the CIELAB colour difference. The initial means are determined by the expected values of fitted Gaussian distributions to CIExyY colour histograms. Experimental results show that the proposed method is promising for the segmentation of weft and warp yarns in multicolour yarn-dyed fabrics, with an improved segmentation accuracy and much faster processing speed than K-means clustering in CIEXYZ and CIELAB spaces.