The fabric of colored spun yarn has ever-changing appearances and styles with different fancy yarns. The fabric image is commonly designed by the designer using the software, which needs complex user interactions and difficult image segmentation. In this paper, a modified color transfer method was proposed to generate the fabric appearance of colored spun yarn. Given the color card as the target image, the style fabric image was matched as the reference image based on the dominant luminance. After transferring the two images to lαβ color space, Wavelet transform and luminance sampling were utilized to filter the redundant high-frequency information and select the representative pixels, respectively. Then, the chromatic channels were transferred based on the best matched luminance and the neighborhood relation. Finally, the image after color transfer was reconstructed by wavelet reconstruction. The proposed reference image matching maintained the result to be the ground truth. For the samples selected, the combined methods based on wavelet transform and luminance sampling improved the efficiency and performance of the proposed scheme. Experiments were conducted on different fabrics with different colors and styles. Experiments demonstrated the validity and superiority of the proposed method, which can provide referential assistance for the designer and save considerable amounts of labor.

No abstract

Fabric density measurement plays a key role in the analysis of fabric structural parameters. Existing automatic measurement methods lack varieties of adaptability and present poor performance in practical application. In order to solve these problems, we use convolutional neural networks (CNNs) to locate warps and wefts for woven fabric density measurement. First, we use a portable wireless device to capture high-resolution fabric images and set up a new dataset with labeled yarns location. Based on this dataset, we propose an effective multi-scale convolutional neural network (MSnet) architecture to locate warps and wefts. Then, by using Hough transform and image projection of predicted yarns location, the fabric density is measured accurately. The experimental results emphasize that the proposed method has reached high accuracy under various kinds of patterns and densities of the fabrics and is superior to the state-of-theart methods in terms of its accuracy and robustness. Promisingly, the proposed method can provide novel ideas for more fabric structural parameter analyses. INDEX TERMS Warps and wefts locating, density measurement, fabric structural parameters, multi-scale convolutional neural networks.

No abstract

Weaving enterprises are faced with problems of small batches and many varieties, which leads to difficulties in manual scheduling during the production process, resulting in more delays in delivery. Therefore, an automatic scheduling method for the weaving process is proposed in this paper. Firstly, a weaving production scheduling model is established based on the conditions and requirements during actual production. By introducing flexible model constraints, the applicability of the model has been greatly expanded. Then, an improved ant colony algorithm is proposed to solve the model. To address the problem of the traditional ant colony algorithm that the optimizing process usually traps into local optimum, the proposed algorithm adopts an iterative threshold and the maximum and minimum ant colony system. In addition, the initial path pheromone distribution is formed according to the urgency of the order to balance each objective. Finally, the simulation experiments confirm that the proposed method achieves superior performance compared with manual scheduling and other automatic methods. The proposed method shows a certain guiding significance for weaving scheduling in practice.