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Fabrics are often subject to repeated creasing and bending deformations, such as elbow movements, and the resilience of crease recovery is an important property that affects fabric’s serviceability. In this research, nine types of woven fabrics were examined on an automated crease recovery tester repetitively to investigate the crease recovery behaviors, and possible factors affecting the creasing resilience. Each specimen was pressed and its recovery process was recorded by sequential images, which allowed the recovery angle to be calculated in real-time. This process was repeated 30 times for the same specimen. The parameters, such as initial angular velocity, instant recovery time and angle, stable recovery time and angle, were extracted from the recovery curve of each cycle, and calculate the differences between different cycles to characterize the changes of crease recovery in repeated tests. The experimental results show that the more the circulating test conducts, the earlier the instant and stable recovery time appear. The stable recovery angles decrease rapidly in the first few cycles, fall more slowly when the testing cycle increases, and finally become stable at a cycle near the 25th cycle. Cotton fabrics take fewer cycles to reach steady than wool fabrics. The high elasticity of polyester fibers affects the resilience remarkably.

To recognize the layout of color yarns of single-system-melange color fabric automatically, a novel FCM-based stepwise classification method is proposed in this article. This method consists of three main steps: (1) warp yarn segmentation, (2) weft color recognition, and (3) the layout of color warps recognition. In the first step, the yarn segmentation method based on mathematical statistics of subimages is adopted to localize warp yarns preliminarily; and then the segmentation results of warp yarn are corrected by misrecognized-boundary remove and missing-boundary interpolation. In the second step, the weft color is extracted based on RGB color histograms of whole fabric image. In the third step, the pixels in each warp yarn are classified into two clusters by fuzzy C-means clustering (FCM) algorithm in CIELAB color model separately, and the preliminary recognized layout of color warps is obtained. All warp colors are clustered by FCM algorithm in CIELAB color model again and the precise layout of color warps is output. The experimental and theoretical analysis proved that the proposed method can recognize the layout of color yarns of single-system- melange color fabrics with satisfactory accuracy and good robustness. © 2015 Wiley Periodicals, Inc. Col Res Appl, 40, 626–636, 2015

In this paper, an effective method based on the smallest repeat unit recognition (SRUR) algorithm is proposed to inspect the color effect of yarn-dyed fabric automatically. This method consists of three main steps: (1) color pattern preliminary recognition; (2) weave repeat unit recognition; (3) color yarn repeat unit recognition. In the first step, the floats in the fabric are located by yarn position segmented with mathematical statistics of sub-images and the colors of all floats classified by the fuzzy C-means algorithm. The color yarn layout is recognized by statistical analysis and the color pattern is roughly generated. In the second step, the weave repeat unit is found based on the preliminary color pattern. The weave repeat unit is extracted from the incompletely recognized weave pattern matrix by the SRUR algorithm. In the last step, according to the weave repeat unit and the preliminary identified color pattern, the color yarn layout is rectified by the improved statistical analysis, and the color yarn repeat unit is finally obtained by the SRUR algorithm. According to the weave and color yarn repeat units, the color effect is produced. The experimental analysis proved that the proposed method can recognize color effects of yarn-dyed fabrics with satisfactory accuracy.

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...

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.

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 logical analysis is proposed to recognize the layout of color yarns of yarndyed fabric from the color pattern. The mathematic expressions of the color pattern and the layout of color yarns are introduced first, and then the fitness of the layout of color yarns to the color pattern is defined by the relation between them. The principle of the proposed recognition method begins with the assumption of the color of the first warp yarn in the color pattern. The whole algorithm procedure is then analyzed in detail with an actual sample color pattern. 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 the color pattern of yarn-dyed fabric and it has fault-tolerance ability in some degree. The research in the paper can be used to construct the whole recognition system of the parameters of yarn-dyed fabric.

The traditional approach to unevenness characterization of yarn is based on the CV (i.e. coefficient of variation) of mass between defined portions of yarn measured with the USTER evenness tester. In fact, yarn with the same parameters has different evenness in fabric, and the evenness of yarn in fabric is different from the source yarn, which is caused by the producing process and parameters of the fabric. However, there is no good method to describe the appearance of yarn in fabric. The paper focuses mainly on a novel method known as yarn evenness in fabric (YEF). The method processes the image of the fabric and is divided into four steps. The first step is the acquisition of the relative image from the sample of woven fabric. The second step is the pretreatment of the image and segmentation of the warp and weft from the fabric based on fast Fourier transform and inverse fast Fourier transform. The third step is to separate the single yarn from the warp or weft sets by the gray unidirectional mean method. In the fourth step, the average relative thickness was selected for characterizing yarn. SD data of thickness that we will analyze in future is also a method. Experimental results on virtual and physical woven fabric showed that the method mentioned can obtain the fine information of yarn from fabric in details. The method of YEF was programmed by Matlab software. Computational burdens are about 11.4 seconds on average, for one meter of warp and weft yarn samples. The program could be valuable when applied to the practical industry.

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...

According to the color yarns in the fabric, the yarn-dyed fabrics are divided into two categories: single-systemmelange color fabrics and double-system-melange color fabrics. The method for inspecting the density of double-systemmelange color fabrics is discussed in this study. By analyzing the pattern and color characters of double-system-melange color fabrics, color-gradient image is proposed to detect the density. The gray-projection method and correlation coefficient method are selected to locate the wefts and warps. With the help of Fourier low-pass filter, the positions of yarns in double-system-melange color fabric are found, and then the density can be obtained by counting the yarns in a unit length automatically. The experiment proved that the method proposed can detect double-system-melange color fabric density successfully.

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...

In this study, an automatic recognition system based on image analysis is constructed to identify the density, the color effect, the layout of color yarns, and the woven pattern of yarn-dyed fabric. To inspect the density, first the skew angles of yarns are detected by Hough transform and then the texture and boundary information of yarns are enhanced by steering filter and graygrads image. The yarns and floats are located in the enhanced images with gray-projection method. To get the color effect, FCM algorithm is used to classify the floats with the color features extracted from each float and the number of yarn colors obtained with cluster validity analysis. The color effect is then extracted from the color pattern obtained from the float classification results. To detect the layout of color yarns, a novel method is proposed in this study. The woven pattern of the yarn-dyed fabric is recognized based on woven fabric pattern database. Experiments on actual yarn-dyed fabrics show that the recognition system proposed in this study is effective for detecting the structure parameters of yarn-dyed fabric.

In the process of analyzing the yarn-dyed fabric, two kinds of color information about color yarns should be detected: (1) the number of yarn colors; (2) the layout of the color yarns. The traditional detection methods are time-consuming and labor-intensive. An automatic method based on image analysis is proposed in this study. The image of yarn-dyed fabric captured with a flat scanner is analyzed by a fuzzy C-means clustering (FCM) algorithm. By the analysis of the image of the yarn-dyed fabric based with the FCM algorithm, we can conclude that the number of yarn colors can be obtained with cluster validity analysis, and the layout of color yarns can be inspected automatically with the help of Hough transform. Experiments on two actual fabrics show that the approach proposed in this study is effective for detecting the number of yarn colors and the layout of color yarns in the yarn-dyed fabric.

Wrinkle or crease recovery property is a common test parameter for assessing fabric performance. It is usually measured by the wrinkle recovery angle (WRA) and wrinkle recovery appearance rating methods.1 The ability of fabric to recover from creasing is generally measured by tracking changes in the WRA value between two folded fabric wings after releasing a certain load from the specimen surface.2 A larger WRA value represents the better ability of fabric to resist and recover from deformation to the initial wrinkle-free surface.3 In the appearance method, a test specimen is wrinkled under a predetermined load for a prescribed period of time, and then evaluated for appearance by comparison with 3-dimensional reference standards.4 In this paper, we will focus on the recovery angle method.

According to the color yarns in the fabric, the fabrics can be divided into three categories: solid color fabrics, single-system-melange color fabrics, and double-system-melange color fabrics. The density of solid fabrics can be inspected with gray-projection method or Fourier analysis method. But the methods cannot be applied to yarn-dyed fabrics directly. A method for detecting the density of single-system-melange color fabrics will be discussed in this article. By analyzing the pattern and color characters of single-system-melange color fabrics, fuzzy C-means algorithm is proposed to classify the colors in the fabric image based on CIELAB color space first. With the color segmentation results, the fabric can be divided into different blocks. The yarns can be located in different blocks with different average gray-levels, and then the number of yarns can be counted in each block. The linear density of threads can be obtained by counting the yarns in a unit length finally. The experiment proved that the algorithm proposed in this study can inspect the density of single-system-melange color fabric successfully. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 456–462, 2013

In order to resolve the key technology problem of automated fabric defect detection, projected transform is proposed to extract features of the fabric image making use of fabric characteristic in this paper. Automated fabric defect detection scheme is divided into two phases, which are the study phase and the detection phase. During the study phase, features of normal fabric image are extracted to get the feature data set of normal fabric. During the detection phase, the method of anomaly detection is developed using features of fabric image to detect defect. Testing on general fabric by this method, experimental results demonstrate that each of feature values is in the normal range for normal fabric image, and one feature value at least is abnormal for defective fabric image. Defects can be located according to the location of abnormal value.

The texture of yarn-dyed fabric image has been analyzed in HSL color space. With Fourier Transform, periods of the pixels in a line are obtained. By comparing the periods in different lines, a conclusion that if the line is parallel to the yarns, sum of periods for the three components (H,S,L) comes to minimum, is proposed in this paper. By this conclusion, the center of fabric image is chosen as skew rectification point. All the lines within ± 20 degree are traversed with a step 0.2 degree, and the periods of H,S and L for the pixels in these lines are all calculated with FFT. By sum the three periods of each line, 101 sum values are obtained. Then the position of yarns can be located by finding the line with minimum period. This method is proved efficiency by the experiment, with a precision of 0.2 degree.

A novel method for realizing the color classifying in yarn-dyed fabric is proposed in this paper. The color image of yarn-dyed fabric was obtained by a flat scanner, and then it is converted from RGB color space to HSL color space. By analyzing the characters of Hue component in HSL color space, the distance and cluster center of hue component in fuzzy clustering algorithms are redefined. During the iteration in FCM, membership degree and cluster center of H and (S,L) are calculated independently, and the membership degree is normalized in the process. A better clustering quality is shown in the experiment. The color yarn number is detected based on the validity for FCM clusters. Experimental comparisons on RGB color space and HSL color space show that the approach proposed in this article is more effective for color extracting and classifying in yarn-dyed fabric.

This article presents a novel method for measuring the unevenness of yarn apparent diameter based on yarn sequence images captured from a moving yarn. A dynamic threshold module was designed to gain the global threshold for segmenting yarns in the sequence images. In the module, a K-means clustering algorithm was employed to classify the pixels of each frame in the sequence into two clusters—yarn and background. The cluster center of the current frame was used as the initial value of the cluster center for the next frame in the sequence to expedite the segmentation process. From the segmented yarn image, the yarn core was further extracted utilizing the characteristics of yarn hairiness, and two judgment templates were adopted to remove burrs, isolated points and unrelated small areas in the images. The yarn apparent diameter was measured on the yarn core at a given interval. The same kind of yarns were tested by using this method and Uster Evenness Tester 5. The experimental results show that the proposed method can accurately detect the unevenness of yarn apparent diameter and provide new useful information about yarn unevenness, such as the short-term, the long-term, and the periodic variations of yarn apparent diameters.

Yarn density measurement is a significant part of yarn-dyed fabric analysis, traditionally based on reflective image analysis. In this paper, utilizing fabric light transmittance, a method for two-dimensional discrete Fourier transform (2D DFT) analysis on the transmission fabric image is developed for fabric density inspection. First, the power spectrum is generated from the fabric image by a 2D DFT. Next, the yarn skew angles are detected based on the power spectrum analysis. Then the fabric image is reconstructed by an inverse 2D DFT. Finally, projection curves are generated from the reconstructed images and the number of yarns is counted according to the peaks and valleys to obtain the fabric density. Through a comparison between analysis on the reflective and transmission images of multiple-color fabrics, it is proved that the latter method can segment the yarns with more satisfactory accuracy. Furthermore, the experimental and theoretical analyses demonstrate that the proposed method is effective for the density inspection of yarn-dyed fabrics with good robustness and great accuracy.

Crease recovery is one of the most important performance properties of fabric that needs to be evaluated frequently for quality improvement and product development. It is often measured by tracking changes in crease angle of a folded specimen in a certain period of time. This paper presents a new automated crease recovery testing system and the trial tests on fabrics with different fiber materials, different weave structures, and different folding methods. The system was designed to record and measure changes of the crease angle in the entire recovery process by using image sequence analysis techniques. Thus, both instantaneous and stable recoveries of the crease angle can be quantified. The system also fully automates the folding, pressing, reading, and other operations on the specimen so that human interferences can be avoided. It was found that the fiber content of a fabric is a determinant factor for crease recovery performance and the weave structure can affect this performance to some extent. Crease recovery also varies with the way of folding the specimen. The recoveries of fabrics with different fiber materials tend to stabilize in different durations, proving the universal testing time set in the current standard test methods is unsuitable for some fabrics.

In this study, a new method for recognizing the parameters of slub-yarn based on image analysis has been proposed. The slub yarn was wrapped on the surface of the black board by YG381 Yarn Evenness Tester. A high resolution scanner was used to acquire the yarn image. Gray stretching and thresholding were carried out to preprocess the image of slub yarn. By separating the slubs from base yarn with different widths, the slub length, slub distance and slub amplitude can be obtained. With the lists of slub length and slub distance, the periodicity rule of slub yarn can be determined. The period of the slubs then will be identified by 1D-Fourier transform. The experiment indicated that the method can identify the parameters of slub yarn with satisfactory results.