Complex Characterization of Yarn Unevenness

Militký, Jiřı́; Ibrahim, S. S.

doi:10.1007/978-3-540-70658-8_4

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…An image of the virtual ideal fabric and the corresponding power spectrum (magnitude of DFT of input image) after the log transformation is illustrated in figure 2 [13]. High-energy peaks in the frequency domain express information about periodic direction lines in the spatial domain image.…”

Section: Power Spectrum Of Ideal Fabricmentioning

confidence: 99%

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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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Section: Power Spectrum Of Ideal Fabricmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

Automatic measurement for dimensional changes of woven fabrics based on texture

Liu

Jiang

Liu

et al. 2013

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…Liu et al 6 presented an automatic method for characterizing the parameters of ring slub yarn by analyzing the 2D image using cluster analyses with an amended similarity-based clustering method. Militky´et al 7 proposed that the complex analysis of mass variation curve from USTER apparatus enables a deeper investigation of many phenomena connected with yarn unevenness and related problems. For providing precise higher measurement, Carvalho et al 8 presented the mass parameterization system, an automatic system for determination of mass characteristics of textile yarn.…”

mentioning

confidence: 99%

Evaluation of yarn evenness in fabric based on image processing

Liu

Jiang

Pan

et al. 2011

Textile Research Journal

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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.

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Complex Characterization of Yarn Unevenness

Cited by 2 publications

References 6 publications

Automatic measurement for dimensional changes of woven fabrics based on texture

Automatic measurement for dimensional changes of woven fabrics based on texture

Evaluation of yarn evenness in fabric based on image processing

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