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Texture provides important visual information for object identification, and texture representation is still a challenging problem in texture analysis. Fabric texture is a typical structural textur...

Computer-based fabric segmentation has recently increased significantly in diverse fields of which textiles design and engineering is no exception. Extracting appropriate information from printed f...

With the advancing consumption level, it is difficult to shorten the product cycle and meet consumer’s demands during the design process of yarn-dyed plaid fabric. In order to extract consumers’ pr...

This series of studies aims to develop a computer vision-based system for automatic detection of misarranged color warp yarns. This paper proposes a yarn layout proofing strategy, integrating with ...

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 segme...

Residual yarn detector plays an important role in the pipeline of spinning-linked winding systems. This research proposed an image-based method to improve the traditional detectors who have weaknes...

In textile and garment industries, misarranged warp yarns of yarn-dyed fabrics disorganize the layout of fabrics and lead to poor product quality. This series of studies aims to develop a computer vision-based system for automatic detection of misarranged color warp yarns in terms of high efficiency and good accuracy. Four main parts are included in this series of studies: warp yarn segmentation, fabric image stitching, warp regional segmentation, and yarn layout proofing. This paper proposes a continuous segmentation method of warp yarns to detect the misarranged color warp yarns for yarn-dyed fabrics automatically, which is the foundation of the developed computer vision-based system. The proposed framework consists of two main components: warp yarn segmentation and fabric image stitching. Firstly, the sequence images of a fabric stripe are captured using a designed offline image acquisition platform. Secondly, the warp yarns in the sequence images are segmented by a sub-image projection-based m...

For the problem of hairiness information missed in existing hairiness measurement method, the goal of this work is to accurately measure the length of long yarn hairiness and obtain the path over every hairiness point of the whole hairiness. To achieve this goal, the yarn images were captured by the video microscope (MOTIC) and the thinned hairiness images were obtained by a series of image processing. The different measurement baseline and step value were choose to segment long hairiness in the method of hairiness segmentation, and the different hairiness lengths were obtained, the results of length show that the length of 0.5mm (baseline)and 3 pixels (step value) is closest to hairiness real length. And then, the more accurate lengths of the hairiness were calculated by the method of hairiness tracking. The lengths of the two new methods are longer than the length of the method of fixed length (1mm), but the lengths of hairiness tracking is longer than the longest lengths of hairiness segmentati...

An effective method based on measuring the fiber orientation of yarn floats with two-dimensional Fourier transform (2-D FFT) is proposed to recognize the weave pattern of yarn-dyed fabric in the high-resolution image. The recognition process consists of four main steps: 1. High-resolution image reduction, 2.Fabric image skew correction, 3.Yarn floats localization, 4. Yarn floats classification. Firstly, the high-resolution image is reduced by the nearest interpolation algorithm. Secondly, the skew of the fabric image is corrected based on Hough transform. Thirdly, the yarn floats in the fabric image is localized by the yarns segmentation method based on the mathematical statistics of sub-images. Fourthly, the high-resolution image is corrected and its yarns are segmented successively according to the inspection information of the reduced image. The fiber orientations are detected by 2-D FFT, and the yarn floats are classified by k-means clustering algorithm. Experimental results and discussions de...

In this paper, the anisotropic wrinkle recovery properties of plain and twill fabrics are explored by studying the variations of the wrinkle recovery angle with sample orientation angle. Orientation angle is the angle measured counterclockwise from the weft direction to the sample’s long axis, that is, the crease direction. This study focused on inspecting anisotropy in wrinkle recovery to find more effective test angles for different woven fabrics. A dynamic wrinkle recovery tester was used to measure the recovery angles of specimens automatically which were cut in various directions. The trend of plain fabrics shows that its recovery angle generally increases at first and then decreases with the increase in the orientation angle. The trend of twill fabrics differs in folding ways. The experimental results revealed that the wrinkle recovery angles of the woven fabrics had the lowest values near the orientation angles of 0° and 90°, i.e. the warp and weft directions, and therefore these two traditionally ...

Crease recovery, one of an essential property for assessing fabric usability, is often evaluated by crease recovery angle method. This paper presents an image registration method based on mutual information to measure the crease recovery angle. The mutual information of two random variables is a measure of their mutual dependence. The maximum normalized mutual information can be worked out when the positions of the free wing in both the rotated frame and the other frame achieve a perfect match. The difference of recovery angles between two frames is defined as the rotated angle of the former frame when the normalized mutual information of the rotated frame and the latter frame is the maximum. Compared with Hough transform method, the recovery angle measured by the proposed method is more accurate when the free wing bends. Besides, mutual information is also applied to determine the stable time when the recovery process reaches a steady state by measuring it between a frame and the other frame which is cap...

As the multi-varieties and small-batch production mode become more popular in weaving enterprises, the traditional manual operated scheduling exposes the disadvantages of low work efficiency and unsatisfying result. In this paper, by summarizing the weakness previous model, a more practical optimization model is developed for weaving production scheduling to reduce the schedulers’ labor. The model describes the optimization of warp beam looming schedule in weaving process based on the analysis of the schedulers’ working process in weaving enterprises. Genetic algorithm (GA) is adopted to solve the model. To improve the computational efficiency and avoid prematurity convergence of GA, the termination condition is updated and the genetic parameters are optimized based on statistical data. The improved GA with optimized parameters gets solutions superior to manual scheduling with a quicker convergence, which has great practical value.

Fabric bending property dictates fabric crease behaviors. Exploring the relationship between fabric bending and crease recovery properties is important for better understanding of fabric performance. This paper presents the viscoelasticity modeling of a creased fabric to characterize the torque and bending deformation by crease recovery and bending parameters, respectively. In the experiment, nine types of fabrics were selected to analyze the relation between bending property and crease recovery property. The bending rigidity (B) and the bending hysteresis moment (2HB) were measured by the KES-FB2 Pure Bending Tester. The initial angular velocity (IV) was measured by a dynamic crease recovery tester. The experimental results showed that B and 2HB generally decrease at the beginning and then almost remain unchanged with the increase in IV. We used an exponential function to express the non-linear relation between bending rigidity and the initial angular velocity, and proved that the initial angular velocit...

To inspect the yarn-dyed fabric density automatically, an effective image analysis method based on mathematical statistics of sub-images is proposed in this paper. This method consists of two main steps: rough measurement and precise measurement. The rough measurement is based on projection curve of the whole fabric image. The fabric image is converted into HSV model from RGB model firstly, and then the projection curve of value is gained directly. The number of yarns is obtained by counting the number of peaks in the curve roughly. The precise measurement is based on projection curves of the fabric sub-images. According to the roughly estimated yarn number, the whole fabric image is divided into a certain amount of sub-images and the projection method is applied to all the sub-images, respectively. The probability distribution map of peaks is obtained by processing the projection curves of all sub-images and the positions of the yarn center are located in the frequency curve generated from the map by mat...

In an effort to convert the comfortable clothing pressure range on breast into the bust girth range for pressure comfort tight-fit garments, 25 healthy female students whose bodies are very close to 160/84 A served as subjects, the garments used were 20 elastic sports vests made of four types of extensibility fabrics with different bust girth and identical style. The objective clothing pressure exerted on the subjects' breast was measured and subjective compressive feeling was evaluated as well. The comfortable pressure range on breast was found to be 0.96–1.355 kPa by studying the relationship between objective clothing pressure and subjective compressive feeling. In conclusion, the comfortable pressure range on breast was converted into bust girth range and shown in calculation equation, which could provide a novel calculation method for the bust girth design of pressure comfort tight-fit garments.

In this paper a novel method based on a genetic algorithm is proposed to recognize the layout of color yarns of yarn‐dyed fabric from the color pattern. The principle of a genetic algorithm is described first, and then the theories of roulette wheel selection method, crossover operation, and mutation operation are explained with the practical problem. Elitist selection is used to search for the correct result of the layout of color yarns. Some new chromosomes are added to the new generation in the genetic algorithm to avoid the local optimization. The repeat element of the layout of color yarns is then detected with period extraction. The repeat element of color pattern with the layout of color yarns is output together. Experiments on some color patterns recognized from actual yarn‐dyed fabrics, some color patterns simulated manually, and some color patterns including error color information of floats prove that the method proposed in this paper is effective for detecting the layout of color yarns from th...

As there are error judgments of float type in the traditional method based on image processing, it is hard to determine the woven fabric pattern from the recognition results. To solve this problem, fuzzy C‐means (FCM) algorithm was selected to classify the floats into two groups in the experiment, and BP neural network is chosen to recognize woven fabric pattern. White–black co‐occurrence matrix is used to extract its texture features. The texture and structure features of the normal fabrics extracted from the classification are input into the neural network to complete the learning process. During the recognition process, the texture features of the fabric are extracted from the classification results with white–black co‐occurrence matrix. The structure features are extracted simultaneously. These features are then input into BP neural network and woven fabric pattern would be output from the neural network. The experiment on actual fabrics proves that the method proposed in this study has fault tolerant...