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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.

Fabric shape retention is one of the most important attributes of fabrics that can influence the quality of the end use product. In this paper, we present a computer vision-based method to analyze the sequential images, which records the dynamic change of a deformed fabric, to model the recovery process, and extract the features of the recovery curve to characterize the shape retention after the deformation. Image processing and the perceptual hash algorithm were used to convert the measurements of a fabric shape variable at different times into Hamming distance points. The recovery function of the fabric shape was formed by fitting the Hamming distance points with exponential function, and three new shape retention indexes, that is, the average slope, the abscissa of the inflation point, and the radius of curvature at the inflation point, were defined based on the recovery function. The experiment showed that the shape retention of 12 fabric samples after deformation could be effectively distinguished by the new indexes. This paper also discussed the relationships between the new indexes and the transitional measurements indicating the fabric shape retention. [ABSTRACT FROM AUTHOR]

Yarn evenness and hairiness are the appearance characteristics of yarn, which affect textile processing and product quality. To evaluate yarn appearance economically and effectively, an image-processing method is proposed in this paper to analyze yarn appearance on a blackboard. Firstly, an image of a yarn blackboard is captured by the scanner. Then, the yarn core and hairy fibers are segmented from the captured image with image-processing algorithms. The coefficients of variation of the yarn diameter (CVbd) and the hairiness index (M) are respectively calculated based on the information about the yarn core and hairy fibers in the image. Finally, the results of the proposed method are compared with those from the Uster Tester. The experimental results demonstrate that yarn appearance can be objectively evaluated using yarn blackboard images. The test results of different yarn blackboards made from the same yarn are stable and consistent. The correlation coefficient between the proposed method and the Uster Tester is 0.98, which proves that the H value can be accurately predicted by the hairiness prediction model. A hairiness prediction model built by the M value is also proven to be accurate when used to predict the corresponding value of the Uster Tester. Compared with the existing yarn evenness and hairiness test methods, the proposed method is more economical and practical. [ABSTRACT FROM AUTHOR]

The slurry coating characteristics of sized yarns directly impact warp weavability. Due to the damage to sized films, the conventional methods of detecting sized-yarn coating characteristics have drawbacks of low efficiency and poor repeatability. A novel detecting method of slurry coating characteristics was proposed based on image processing. Through the starch-iodine color reaction principle, a self-made dynamic image acquisition device was developed in this paper, in which the apparent images of starch-based sized yarns after color reaction were captured consecutively. The slurry coating percentage (SCP), slurry coating depth (SCD) and slurry coating unevenness (SCU), respectively reflecting the sizing coating integrity, sizing coating thickness and thickness unevenness, were extracted by image processing. The effects of experimental parameters, including immersion time and concentration of I2-KI solution, on slurry coating characteristics were analyzed, and central composite design was adopted to optimize the stability of the test system. Sized yarns commonly used in textile mills were characterized by the proposed method. The experimental results indicated that immersion time of 3.56 min and I2-KI concentration of 0.11‱ (‱ represents that the mass of the solute is one ten-thousandth of solution) led to the optimal stability of slurry coating characteristics (the CV of SCP, CV of SCD and CV of SCU were 3.32%, 5.56% and 9.37%, respectively). The much lower CV of the proposed method compared with conventional ones confirmed that the method was useful for evaluating slurry coating characteristics. [ABSTRACT FROM AUTHOR]

The influence of different yarn structure on moisture conductivity of staple fibers yarn and its fabric were discussed.Polyester was used as raw material.28 tex ring yarn, rotor yarn and vortex yarn were spun separately. The yarn structure and basic properties of three kinds of yarn were tested.Capillary wicking method and water drop diffusion area contrast method were adopted. The moisture conductivity of three kinds of yarn and its fabric were evaluated.The results showed that the trend of fabric wicking height and water drop diffusion area were the same.But the trend of yarn wicking height and corresponding fabric wicking height and water drop diffusion area test results were not the same.It is considered that less hairiness on the surface has an obvious influence on the improvement of the fabric moisture conductivity though the wicking height of vortex yarn was smaller. [ABSTRACT FROM AUTHOR]

The layout of colored yarns in yarn-dyed fabrics is a significant part of designing and production in the textile industry, which is still analyzed manually at present. Existing methods based on image processing have some limitations in accuracy and stability. Therefore, an automatic method is proposed to recognize the layout of colored yarns and some other basic fabric structure parameters: the fabric density and weave pattern. First, a large dataset with fabric structure parameters is constructed. The fabric images are captured by a wireless portable device. Then the yarns and floats are accurately located using a novel multi-task and multi-scale convolutional neural network. Finally, a density-based color clustering algorithm is proposed to recognize the layout of colored yarns. The results of extensive experiments show that the proposed method can automatically identify the basic structure parameters with high effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Fabric shape retention is an essential property for assessing fabric usability and easy-care properties, which needs to be evaluated frequently for quality improvement. At present, certain aspects of shape retention can be characterized by particular devices, such as the crease recovery tester, the fabric drape tester, etc. To effectively and accurately reflect fabric shape retention performance, we developed an automatic crease forming device to simulate fabric crease creation and the shape recovery process in daily life, and objectively assess the shape retention by an image processing method. A specified size specimen was laid flat on the device to create a sharp crease. Then, a video image of the fabric shape recovery is acquired for measuring the evaluation indexes, such as the vertex angle (VA), height (H) and shape retention area (SA). Finally, the results of this proposed method are compared with existing methods. When compared with the existing crease recovery tester, there is good consistency between the VA of the developed measurement system and the recovery angle of the fabric crease recovery tester, which indicates that the proposed method can be used to evaluate the crease recovery of fabrics. Compared with the drapability, there is linear function relationship between the H and SA of the developed measurement system and the draping coefficient of the fabric drape tester, which demonstrates that the proposed method can be used to evaluate the drapability. Therefore, experimental results indicate that the data calculated by our proposed method can be used to determine fabric shape retention. [ABSTRACT FROM AUTHOR]

Fabric smoothness appearance assessment plays an important role in the textile and apparel industry. To evaluate fabric smoothness objectively, different methods have been proposed based on computer vision technology. To further improve the performance and promote the application of the assessment methods, this paper reports a hybrid computer vision system for objective assessment of fabric smoothness appearance with an ensemble classifier to integrate the advantages of the different image feature sets, which are extracted based on different image processing technologies. The image acquisition environment is established in this system with the selection of illumination parameters—intensity, position angle and altitudinal angle—by a designed strategy. The main steps of the strategy include determination of priority by information gain analysis and parameter selection by classifier performance analysis. The support vector machine classifiers trained by each feature sets are grouped into an ensemble by a self-adapting weighted voting method and the redundant feature sets are eliminated based on the weights of the feature sets. The final result shows evaluation accuracies with 82.86% under 0-degree error, 97.14% under 0.5-degree error and 100% under 1-degree error, which outperforms the other methods in the same environment and verifies the applicability of the proposed system. [ABSTRACT FROM AUTHOR]

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 weaknesses such as low precision, low sustainability and yarn-damage-possibility. A detection system was developed to capture and process the bobbin images. The proposed algorithm includes three main steps: bobbin recognition, residual yarn reusability judgment and un-reusable residual yarn detection. With the utilization of the adaptive threshold, profile detection, region-of-interesting extraction and frequency-tuned salient region detection, the bobbins were classified into three classes with a desirable accuracy rate. The proposed method was applied on 21 different bobbin samples and obtained a 100% detection rate, which demonstrated that the method is effective on different samples. To test the robustness of the method, it was tested in eight different light conditions. The result showed that the method is reliable in a wide range of illumination intensity. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to propose a novel method using support vector machine (SVM) classifiers for objective seam pucker evaluation. Features are extracted using wavelet analysis and gray-level co-occurrence matrix (GLCM), and the samples are evaluated using SVM classifiers. The study aims to solve the problem of inappropriate parameters and large required samples in objective seam pucker evaluation.Design/methodology/approach Initially, seam pucker image was captured, and Edge detection and Hough transform were utilized to normalize the seam position and orientation. After cropping the image, the intensity was adjusted to the same identical level through histogram specification. Then, the standard deviations of the horizontal image and diagonal image, reconstructed using wavelet decomposition and reconstruction, were calculated based on parameter optimization. Meanwhile, GLCM was extracted from the restructured horizontal detail image, then the contrast and correlation of GLCM were calculated. Finally, these four features were imported to SVM classifiers based on genetic algorithm for evaluation.Findings The four extracted features reflected linear relationships among five grades. The experimental results showed that the classification accuracy was 96 percent, which catches up to the performance of human vision, and resolves ambiguity and subjective of the manual evaluation.Originality/value There are large required samples in current research. This paper provides a novel method using finite samples, and the parameters of the methods were discussed for parameter optimization. The evaluation results can provide references for analyzing the reason of wrinkles during garment manufacturing. [ABSTRACT FROM AUTHOR]

In order to analyze the parameters of slub yarn from sequential images accurately, an automatic image mosaic method is proposed in this paper. In this method, a series of overlapping yarn images, which are captured from a moving slub yarn, are stitched into a panorama automatically. Background subtraction, image segmentation and judgment template traversal methods are applied to preprocess the sequential images for obtaining a test image. Subsequently, certain rows in the bottom of the test image are used as a template image to match the next image. The matching coefficient matrix is calculated between the template image and next image based on the normalized cross-correlation method. In the matrix, the coordinates of the peak value are found as the optimal matching points. Two kinds of slub yarn images captured under 40 fps are stitched by using the proposed method and the manual method, respectively. Finally, an objective method is formulated to evaluate the qualities of the image mosaic by the proposed method. The experimental results show that the proposed method can find the match position accurately and is highly consistent with the manual method. [ABSTRACT FROM AUTHOR]

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 method successively. Thirdly, the sequence images are stitched by a yarn-template matching method based on their warp segmentation results. Finally, the continuous segmentation result of warp yarns is saved for the further processing of warp regional segmentation and color warp layout proofing. The proposed method has been evaluated on 720 fabric images of five fabric examples with plain and 2/2 twill, and experimental results show that the proposed method can realize the continuous segmentation of warp yarns in yarn-dyed fabrics with the yarn segmentation accuracy of 97.43% and image stitching accuracy of 99.53%. [ABSTRACT FROM AUTHOR]

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 captured 10 s after the former frame. The experimental results show that the crease recovery properties of wool–polyester-blended fabrics are better than the fabrics made of other materials. However, the property of 100% cotton fabric is poorer among the selected fabrics. It is demonstrated that the method has excellent robustness and adaptability in recovery angle calculation. [ABSTRACT FROM AUTHOR]

This paper presents a new method for building a digital yarn black board (DYBB) with the yarn diameter data obtained from processing sequential yarn images. An image acquisition and processing system, mainly consisting of a video camera, a single-chip micro-computer and a stepper motor, was set up to capture sequence images of a moving yarn and extract yarn diameter data after the image threshold and morphological opening operation. Then, the diameter data of the yarn was used to construct the DYBB by redrawing all the scans in white on a black plane once they were aligned at the centers. The DYBB provides functions, such as local data amplification, fast focusing, phase adjustment and space adjustment, for more intuitive and convenient evaluation of yarn evenness. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wrinkle recovery is a dynamic process in which a folded fabric specimen continues to be unfolded by itself, and is often evaluated by angle changes between two folded fabric wings. Inspired by the advantages of video sequence in dynamic measurement, we developed a video capturing and processing system for dynamic measurements of fabric wrinkle recovery angle. In the experiment stage, a wrinkled specimen is first compressed by a pneumatic presser for a certain duration, and then videoed using a charge-couple device camera during its entire recovery process. Each image frame in the video sequence is processed to detect the free wing of the wrinkled specimen. To calculate the recovery angle accurately, image-processing algorithms, such as binarization, thinning operation and Hough transform, are implemented subsequently. Finally, the Wilcoxon rank-sum test is carried out to evaluate the difference between the data measured using the American Association of Textile Chemists and Colorists (AATCC) 66-2008 method and this proposed method. Experimental results indicate that the data calculated by our proposed method are consistent with those ones measured by the AATCC 66-2008 method. Compared with the existing fabric wrinkle recovery measurement devices, such as the SDL-M003 wrinkle recovery tester, the developed measurement system has made three important contributions: (1) it automates the entire testing procedure so that human interference is eliminated; (2) it records the complete change of the wrinkle angle so that the recovery property can be analyzed dynamically; and (3) it uses video sequence analysis to calculate recovery angles so that the measurement is more accurate and efficient. [ABSTRACT FROM PUBLISHER]

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 ability, and it can recognize fabric patterns correctly. [ABSTRACT FROM AUTHOR]

In calendering process, the fabric undergoes luster treatment to make the surface uniformly glossy at a high operation speed. It is easy to be misjudged for the seam detection by human vision in the process, especially for the seam detection of light fabric. To improve quality products and revenues, effective and fast feature extraction algorithms are urgently needed to solve the problem. This paper presents a new wavelet energy measure method and also compares it with a novel scheme for automated textural feature extraction implementation in CIELAB color space. In wavelet-based approach, energy measures are extracted in the best number decomposition. In the CIELAB color space, we proposed to calculate characteristic parameters included mean, standard deviation and variation coefficient (CV). Then the feature data obtained from the two approaches are analyzed to determine optimized threshold values in order to recognize seam information. Compared the detection results of the two algorithms, the characteristic parameters extraction in the CIELAB color space shows higher accuracies and more computationally efficient than wavelet-based approach on detecting fabric seam in calendering process. [ABSTRACT FROM AUTHOR]

• 3D measurement for yarn evenness. • The intersection method and the tangent method for yarn cross-section reconstruction. • Characterize the yarn evenness by the area change of yarn cross-sections. • Reconstruction of 3D model of yarn core. A three-dimensional measurement method for yarn evenness is proposed in this paper. A sample is placed in the image acquisition system and the virtual images and real image are collected in one image by a camera. The geometric relationship of the xoy and the xoz plane of the system is obtained. The multi-view image of sample is calibrated according to the geometric relationship. The intersection method and the tangent method are proposed to determine the key-points of yarn cross-section reconstruction in the xoy plane. 3D model of yarn core is obtained by combining all the yarn cross-sections in the xoz plane. The correlation coefficient between mean area of yarn cross-sections in the proposed method and diameter measured by the Uster TESTER 5 is up to 0.759. The correlation coefficient of CV values between the proposed method and the Uster TESTER 5 is up to 0.944. [ABSTRACT FROM AUTHOR]

• The fabric crease contour lines can be reasonably fitted to Gaussian curves. • Indexes extracted from Gaussian curves describe fabric shape retention properties. • The fabric recovery regularity can be dynamically detected by the indexes. • Fabric creases can be stable within 60 s of the recovery stage. Evaluation of fabric shape retention is an indispensable part of fabric performance evaluation. The existing evaluation methods for investigating the wrinkle recovery, drape, stiffness of fabrics are very mature, but a comprehensive and intuitive evaluation of fabric shape retention is not achieved. Based on the previously proposed fabric shape retention testing device and image processing method, this paper proposes a method to fit fabric crease contour lines to Gaussian curves to optimize the fabric crease contour lines and describe the fabric crease shape more intuitively. The indexes, which are the vertex angle (VA), the vertex height (VH), the maximum curvature (MC), and the enclosed area (EA), can be extracted from the Gaussian curves of the stable state to characterize different shape retention properties of the fabric. By comparing the test results of the standard method, it was found that VA and MC can form a linear regression relationship with the crease recovery angle to describe the crease recovery property of the fabric. VH, MC and EA have linear regression relationships with static drape coefficient or bending length to describe the drape and stiffness of the fabric. Compared with previous studies, this article enables to inspect the fabric crease recovery process dynamically, depict the crease recovery shape visually and evaluate the fabric shape retention performance comprehensively and accurately. This paper provides an experimental basis for investigating the evaluation method of fabric shape retention performance. [ABSTRACT FROM AUTHOR]