Image analysis measurement of cottonseed coat fragments in 100% cotton woven fabric

Liu, Jihong; Zhu, Bin; Jiang, Hongxia; Gao, Weidong

doi:10.1007/s12221-013-1208-y

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…This approach has been used in both the composite 30–32 and textile industries 24,33–36 for image analysis applications ranging from feature identification to fault detection …”

Section: Methodsmentioning

confidence: 99%

A machine vision system for the braid angle measurement of tubular braided structures

Hunt

Carey

2018

Textile Research Journal

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Two-dimensional braided composite materials consist of an impregnated and cured braided fiber structure. The braid angle, which describes the orientation of fibers on the mandrel, affects the material properties of the composite part. Furthermore, braid production is often inaccurate, requiring quality assurance measurements of the fiber orientation. In this study, a frequency domain machine vision algorithm was applied to tubular braided preforms to measure fiber alignment during production. Several approaches to compensate for the tubular mandrel shape were tested and the fiber distribution in both the spatial and frequency domains was compared. The developed machine vision system allowed braid angle measurements to be made in real-time and in-line during the braiding process. The integration of such a system eliminates the need to make manual quality assurance measurements of the braided fiber structure, it further automates the braiding process, and it decreases production error.

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“…This approach has been used in both the composite 30–32 and textile industries 24,33–36 for image analysis applications ranging from feature identification to fault detection …”

Section: Methodsmentioning

confidence: 99%

A machine vision system for the braid angle measurement of tubular braided structures

Hunt

Carey

2018

Textile Research Journal

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“…Image analysis has also been proved to be an efficient method for analyzing fabric parameters [11][12][13][14][15][16]. Fast Fourier transform (FFT) is widely used for data processing because of its computational efficiency [17][18][19]. Xu [20] described the detection and measurement method based on the FFT technique and found peaks in the power spectrum, which represented the frequency of periodic elements of warp and weft in the fabric image.…”

Section: Introductionmentioning

confidence: 99%

Automatic measurement for dimensional changes of woven fabrics based on texture

Liu

Jiang

Liu

et al. 2013

Meas. Sci. Technol.

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Dimensional change or shrinkage is an important functional attribute of woven fabrics that affects their basic function and price in the market. This paper presents a machine vision system that evaluates the shrinkage of woven fabrics by analyzing the change of fabric construction. The proposed measurement method has three features. (i) There will be no stain of shrinkage markers on the fabric specimen compared to the existing measurement method. (ii) The system can be used on fabric with reduced area. (iii) The system can be installed and used as a laboratory or industrial application system. The method processed can process the image of the fabric and is divided into four steps: acquiring a relative image from the sample of the woven fabric, obtaining a gray image and then the segmentation of the warp and weft from the fabric based on fast Fourier transform and inverse fast Fourier transform, calculation of the distance of the warp or weft sets by gray projection method and character shrinkage of the woven fabric by the average distance, coefficient of variation of distance and so on. Experimental results on virtual and physical woven fabrics indicated that the method provided could obtain the shrinkage information of woven fabric in detail. The method was programmed by Matlab software, and a graphical user interface was built by Delphi. The program has potential for practical use in the textile industry.

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“…The visual sensing element and computer developed image processing program could be applied to the analysis of woven fabric weave instead of using visual operation, thereby avoiding eyestrain, which causes errors in manual analysis. 3 The digital image processing analysis technique has been applied in different industries extensively. In terms of the textile industry, Wood 4 used the image of carpet structure to convert the minor differences in carpet texture into highlighted areas of maximum strength change, so as to classify the carpet exactly.…”

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confidence: 99%

A novel image processing technology for recognizing the weave of fabrics

Kuo

Shih

Huang

et al. 2015

Textile Research Journal

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The current analysis of fabric weave diagrams requires using fabric analyzing glass to record the weave number manually. This method damages eyesight and is also very time consuming. In addition, the unweaving mode damages the weave structure of woven fabric. This study uses a computer vision system and digital image processing technology for direct non-destructive analysis of the commonly used 12 fabric textures of woven fabrics without unweaving. Moreover, it proposes an automated woven fabric weave recognition method to enhance the practicability and fault tolerance of the recognition system. Firstly, the woven fabric image was shot by using a front light source and back light source, the noise of the woven fabric image was reduced by using a median filter and the contrast was increased by using histogram equalization. The statistical threshold value was used to segment the warp yarn area and the opening operation of morphology was used to disconnect the connected blocks and erode small noise. Horizontal projection and vertical projection were used to segment the warp yarn and weft yarn. The weave diagram was drawn to improve the computing time of the gray-level co-occurrence matrix. The contrast in the gray-level co-occurrence matrix was selected as the eigenvalue. In terms of woven fabric samples, 12 target samples were obtained, the Euclidean distance classifier was used and the 12 test samples were used for the experiment. The result showed a recognition rate of 100%. The recognition system was adopted by this study to effectively recognize the woven fabric weave. Keywords recognition of woven fabric weave, image processingAmong woven fabric analysis items, the qualitative analysis of fabric appearance structure depends considerably on eyesight, especially for woven fabric weave analysis. This process is time consuming, and analysis errors are likely to be caused by eyestrain and mismatching economic benefit. Based on this issue, a timesaving and correct analysis mode must be researched to solve the defects mentioned above. 1 In terms of analytic instruments, there were numerous sorts of fabrics, and the complexity led to difficulties in recognition. The users needed to memorize the analysis process, and the results of instrumental analysis were recorded in the fabric analysis sheet manually, which was time consuming. When establishing the fabric weave diagram, the warp yarn and weft yarn interlace of samples was observed by using counting glass. The weave number was marked on the graph paper until the weave number reoccurred. A minor mistake will restart this process, making it challenging for mass operation. If the data processing was transferred to computers, the analysis time could be shortened, and human resources could then be used more effectively. 2 Furthermore, if the yarn of fabric was very thin and there was no counting glass, then weave analysis would be destructive and result in unweaving. Because the

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Image analysis measurement of cottonseed coat fragments in 100% cotton woven fabric

Cited by 7 publications

References 19 publications

A machine vision system for the braid angle measurement of tubular braided structures

A machine vision system for the braid angle measurement of tubular braided structures

Automatic measurement for dimensional changes of woven fabrics based on texture

A novel image processing technology for recognizing the weave of fabrics

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