Image processing of digital images is one of the essential categories of image transformation in the theory and practice of digital pattern analysis and computer vision. Automated pattern recognition systems are much needed in the textile industry more importantly when the quality control of products is a significant problem. The printed fabric pattern segmentation procedure is carried out since human interaction proves to be unsatisfactory and costly. Hence, to reduce the cost and wastage of time, automatic segmentation and pattern recognition are required. Several robust and efficient segmentation algorithms are established for pattern recognition. In this paper, different automated methods are presented to segregate printed patterns from textiles fabric. This has become necessary because quality product devoid of any disturbances is the ultimate aim of the textile printing industry.
In this article, a computerized method is proposed for simulating digital woven fabric (DWF) based on sequential yarn images captured from a moving yarn. A mathematical model of woven fabric structure is established by assuming that the crimped shape of yarns in weave structure is elastica, and the cross-sections of yarn in sequence image and fabric are circular and ellipse, respectively. The sequential yarn images, which are preprocessed and stitched first by image processing methods, are resized based on the mathematical model. Then a light intensity curve, which consists of radial curve model and axial curve model, is used to simulate the gray texture distribution of interlacing points in radial and axial directions. Finally, a Boole Matrix model is used to control the woven pattern. In the experiment, a slub yarn and a normal yarn samples with same count are applied to simulate gray texture fabrics. Then the gray fabrics are transformed to color fabrics based on three color maps. The fabric simulations are confined to single fabrics of plain, 2/2 matt, and 1/3 twill weaves.
In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes.