Study On Fabric Texture Representation And Automatic Identification

In the fabric production process, the fabric quality control is very important, such as defect detection, pilling rating, puckering assessment, and so on. However, the traditional fabric detection mainly relied on manual testing, due to human factors, whose miss rate and fall-alarm rate were high. With the extensive application of computer technology, it begins to realize the automation and intelligence in fabric texture analysis, which is based on the digital characterization theory of fabric texture. The application effect of the research on fabric parameters and defect study of auto-detection is directly determined by analysis on characterization of the fabric image texture. As to applying computer vision to the analysis and detection of the appearance of textiles, fabric texture characterization and analysis are worth further studying which is important to the application of digital textile.Fabric texture is periodic. Wavelet transforming is one of the most important methods to describe the periodic features. In this paper it extracts the features of texture image using adaptive wavelet, which improves the traditional non-adaptive wavelet methods. This paper verifies the feasibility and effectiveness of the characterization algorithm of fabric texture in terms of fabric defect detection.In the paper it describes the fabric features using adaptive wavelet with the three-ply resolution. Firstly it gives a brief introduction of the application of this method in characterization of woven fabric texture and its verification in defect detection. Adaptive wavelet decomposition can be proved to be a better method in describing the characterization of fabric texture, suitable for fabric defect detection. Secondly, after the image's pre-processing, we search for the filter to match fabric texture through the approximation conditions. This paper offers two new constraint conditions, the fluctuations in the direction and gray-scale of co-occurrence matrix of wavelet coefficient texture. The method in this paper is proved better than the approximation condition about energy and scope of the wavelet coefficient. Thirdly, Breaking through the single adaptive wavelet decomposition, it shows a stop signal is the extent of some of the decrease of entropy in Approximate Image. It applies the three-ply resolution method, which is more appropriate for fabric defect detection. In the paper the method to describe the periodic features is the adaptive wavelet with the three-ply resolution, which is shown effective using defect detection. At last, this texture characterization method is applied to the defect detection of the twill fabric and the plain fabric, it shows on how to decide if the defect exists by the characteristic value. According to comparing several thresholding methods, it finds a suitable method of binarization, which is one-dimension maximum entropy. It calculates the target's area, size, the angle between the long axis and horizontal and the long axis and short axis ratio, using ellipse method. And it gives an analysis of defect characteristics, such as size and type, and so on. The result is shown feasible and effective.