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Historically, yarn-dyed plaid fabrics (YDPFs) have enjoyed enduring popularity with many rich plaid patterns, but production data are still classified and searched only according to production parameters. The process does not satisfy the visual needs of sample order production, fabric design, and stock management. This study produced an image dataset for YDPFs, collected from 10,661 fabric samples. The authors believe that the dataset will have significant utility in further research into YDPFs. Convolutional neural networks, such as VGG, ResNet, and DenseNet, with different hyperparameter groups, seemed the most promising tools for the study. This paper reports on the authors' exhaustive evaluation of the YDPF dataset. With an overall accuracy of 88.78%, CNNs proved to be effective in YDPF image classification. This was true even for the low accuracy of Windowpane fabrics, which often mistakenly includes the Prince of Wales pattern. Image classification of traditional patterns is also improved by utilizing the strip pooling model to extract local detail features and horizontal and vertical directions. The strip pooling model characterizes the horizontal and vertical crisscross patterns of YDPFs with considerable success. The proposed method using the strip pooling model (SPM) improves the classification performance on the YDPF dataset by 2.64% for ResNet18, by 3.66% for VGG16, and by 3.54% for DenseNet121. The results reveal that the SPM significantly improves YDPF classification accuracy and reduces the error rate of Windowpane patterns as well. [ABSTRACT FROM AUTHOR]

To measure the warp and weft densities of high-tightness fabrics, an efficient inspection method utilizing a novel density- texture formulation is developed. The density-texture relationship of twill and satin weaves is analyzed, and a formulation based on the weft and wale densities is developed and applied to calculate the warp density. In the experiment, the weft and wale densities are detected using a projection method on separately acquired transmittance images, and the warp density is calculated using the density-texture formulation. Experimental results proved that the proposed method is effective for measuring yarn densities of high-tightness fabrics with twill and satin weaves under realistic production conditions and can satisfy the requirement for production practice. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a new method for real-time segmentation of yarn images which are captured by a real-time image acquisition device. The first frame of the images is clustered by the local average intensity and entropy of the image based on the FCM (Fuzzy C-means) algorithm to obtain a segmentation threshold value. The pixels with an intensity below the threshold value in each column of the image are convolved with a convolve template to construct an intensity gradient curve. The points of maximum value and minimum value in the curve are considered as the upper and lower edge points of yarn. A robust real-time segmentation algorithm of yarn images is obtained for evaluating yarn diameter more precisely. Finally two indices of SE (Segmentation Error) in% and ADE (Average Diameter Error) in % are proposed to evaluate the segmentation method, which is then compared with the manual method. [ABSTRACT FROM AUTHOR]

To realise the density measurement of high-tightness woven fabrics, an efficient inspection method based on the structure relation is developed in this paper. The structure relations of typical HTWF, twill and satin weave are analyzed and a calculation equation of warp density is given with the fabric weave, weft density and wale density. In the experiment, the weft and wale densities are measured with the Fourier transform, image reconstruction and threshold processing based on separately captured images. The warp density is finally calculated based on the mean value of wale and weft density and the given calculation equation constructed with the weave pattern. The experimental results prove that the automatic measurement density system can realize the precise measurement of high-tightness woven fabric density with satisfactory precision and can replace the current manual analysis method. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To replace time-consuming and lab-intensive of manual inspection, a Fourier transform is proposed to detect the thread densities of woven fabric in this paper. First, theories of the Fourier transform, yarn image reconstruction and the threshold method are introduced. Then the steps of fabric image acquisition, the Fourier transform of the fabric image, feature analysis in the frequency domain, image construction of fabric yarns, and threshold processing are analysed. Lastly, after locating and counting the yarns in the fabric segmentation results, thread densities of the woven fabric are calculated. The experimental results prove that the method proposed can detect the thread densities of woven fabric correctly and it can be used to replace the current manual analysis. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose – The purpose of this paper is to research an auto generation method of developing FFT image and image pattern for textile based on FFT theory. Design/methodology/approach – In the research, a program was developed to generate FFT images using the FFT algorithm. The process of auto generation FFT image can be divided into the following steps: initializing the size of image, painting source image, giving the color pattern, transforming FFT image by FFT, designing mask template, and image pattern combining by point diagram. These image patterns can be used to apply on the textile. Findings – The results showed that the FFT images can be used for textile designer directly. The FFT images can also be used as elements for textile image design such as clothing. The auto generation FFT images by FFT reflect different modern sense of beauty from traditional geometric images. Research limitations/implications – There are many parameters that affect the art effect of FFT image generating by FFT algorithm. However, there is no discussion about the relationship between the parameters and art effect. Three dimension effects are not obvious in the simulation results by virtual clothing software. Originality/value – The paper presents a fundamental understanding of the property of the FFT image generating by FFT algorithm and application method of the image pattern in clothing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]