Ruru Pan

Automatic Detection of the Layout of Color Yarns for Yarn-dyed Fabric via a FCM Algorithm

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.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.

Automatic Detection of Structure Parameters of Yarn-dyed Fabric

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 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 sys...

Dynamic measurement of fabric wrinkle recovery angle by video sequence processing

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.

Automatic recognition of the color effect of yarn-dyed fabric by the smallest repeat unit recognition algorithm

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.

Automatic inspection of yarn-dyed fabric density by mathematical statistics of sub-images

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 mathematical statistics method. The number of peaks in the frequency curve is counted, and, therefore, the number of yarns is detected, and the density can be calculated precisely. The experimental results proved that the proposed method is effective for yarn-dyed fabrics and can satisfy the requirement for production practice.

Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

Abstract 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.

Seam detection of inhomogeneously textured fabrics based on wavelet transform

A large amount of seam detection for inhomogeneously textured fabrics makes those performing it fatigued, which leads to misjudgments by human vision, especially for the seam detection of patterned inhomogeneously textured fabrics. The traditional wavelet texture analysis is no longer applicable to fabrics with inhomogeneous textures and irregular patterns. In this paper, a novel mean weighting factor is proposed to obtain an optimized discriminant measure to detect the fabric seams. Firstly, the wavelet coefficients are extracted in individual decomposition levels. Then a mean weighting factor is calculated on the use of the difference of the coefficient values between two consecutive decomposition levels, and a better discriminant measure for seam detection is obtained. Lastly, a thresholding process is used to segment the seam information from the background. The experimental results show that the proposed approach effectively carries out the seam detection in the inhomogeneously textured fabric images.

Exploring the relationship between bending property and crease recovery of woven fabrics

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 velocity is related to fabric bending property and can be used to characterize the fabric crease recovery property.

Evaluation of yarn evenness in fabric based on image processing

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.

Measuring the unevenness of yarn apparent diameter from yarn sequence images

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.