Fabric Surface Roughness Evaluation Using Wavelet-Fractal Method

Kim, Soo Chang; Kang, Tae Jin

doi:10.1177/0040517505059209

Cited by 25 publications

(14 citation statements)

References 11 publications

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“…This fact affects the scales of values provided by the methods that employ these AATCC replicas. Another method based on fractals is presented in [21]. The proposed method was validated using the fractal surfaces produced from the mathematical functions and compared with the box and cube counting methods.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Garment smoothness appearance evaluation through computer vision

Silvestre-Blanes

Berenguer-Sebastiá

Pérez-Lloréns

et al. 2011

Textile Research Journal

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The measurement and evaluation of the appearance of wrinkling in textile products after domestic washing and drying is performed currently by the comparison of the fabric with the replicas. This kind of evaluation has certain drawbacks, the most significant of which are its subjectivity and its limitations when used with garments. In this paper, we present an automated wrinkling evaluation system. The system developed can process fabrics as well as any type of garment, independent of size or pattern on the material. The system allows us to label different parts of the garment. Thus, as different garment parts have different influence on human perception, this labeling enables the use of weighting, to improve the correlation with human visual system. The system has been tested with different garments showing good performance and correlation with human perception.

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Section: Introduction and Related Workmentioning

confidence: 99%

Garment smoothness appearance evaluation through computer vision

Silvestre-Blanes

Berenguer-Sebastiá

Pérez-Lloréns

et al. 2011

Textile Research Journal

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“…43 In the textile industry, as a two-stage process, image analysis techniques have been generally used as the first step before Bayesian classification. Firstly, textile surface characteristics (i.e., surface roughness, color mean values, number, area, and density of pills) were extracted through image processing, and then, the classification algorithm was applied to evaluate fabric appearance, such as fabric pilling or fabric wrinkle.…”

Section: Bayesian Classification In Textile Industrymentioning

confidence: 99%

“…As a well-established classification algorithm, the Naive Bayes algorithm has been used to overcome problems in various types of textile applications, such as the objective evaluation of surface roughness, 42 color difference evaluation in fabric dyeing, 35 and fabric pilling evaluation. 43 In the textile industry, as a two-stage process, image analysis techniques have been generally used as the first step before Bayesian classification. Firstly, textile surface characteristics (i.e., surface roughness, color mean values, number, area, and density of pills) were extracted through image processing, and then, the classification algorithm was applied to evaluate fabric appearance, such as fabric pilling or fabric wrinkle.…”

Section: Bayesian Classification In Textile Industrymentioning

confidence: 99%

Data mining and machine learning in textile industry

Yıldırım

Birant

Alpyıldız

2017

WIREs Data Min & Knowl

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Data mining has been proven useful for knowledge discovery in many areas, ranging from marketing to medical and from banking to education. This study focuses on data mining and machine learning in textile industry as applying them to textile data is considered an emerging interdisciplinary research field. Thus, data mining studies, including classification and clustering techniques and machine learning algorithms, implemented in textile industry were presented and explained in detail in this study to provide an overview of how clustering and classification techniques can be applied in the textile industry to deal with different problems where traditional methods are not useful. This article clearly shows that a classification technique has higher interest than a clustering technique in the textile industry. It also shows that the most commonly applied classification methods are artificial neural networks and support vector machines, and they generally provide high accuracy rates in the textile applications. For the clustering task of data mining, a K-means algorithm was generally implemented in textile studies among the others that were investigated in this article. We conclude with some remarks on the strength of the data mining techniques for textile industry, ways to overcome certain challenges, and offer some possible further research directions.

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“…A further exploration is probably to see whether the CV values can be well identified by the computerized method, so that the values can be used to indicate the uneven distribution level or variation of yarn snarls on a single yarn specimen. Table 6 shows the errors of the CVs of the snarl height and width between the interactive and fully computerized methods for each yarn sample, which is defined as (3) in which E c is the error of the CVs of the snarl height or width for each sample, CI i is the CV value of the snarl height or width of the ith yarn specimen obtained by the interactive method, CP i is the CV value of the snarl height or width of the ith yarn specimen calculated by the computerized method, and N is the number of specimens.…”

Section: Of Snarl Height and Widthmentioning

confidence: 99%

Evaluation of a Digital Image-signal Approach on the Automatic Measurement of Cotton Yarn Snarls

Tao

Murrells

2009

Textile Research Journal

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Residual torque or twist liveliness of a single-spun yarn, among other factors, is the most prominent and fundamental factor contributing to the spirality of plain-knitted fabrics and the skewness of denim fabrics made from the yarn. In textile processing, yarn snarling caused by the twist liveliness is also considered a serious problem that leads to yarn breakage, the deterioration of yarn properties, and equipment malfunction [1]. Over recent decades, various testing apparatus and procedures have been developed for the measurement of twist liveliness. Generally, all of these methods are based on manual operation and can be classified into the three categories of direct, semi-direct, and indirect methods [2]. 1 Recently, computer vision has attracted the increasing attention of researchers in textiles and apparel. Attempts have been made to replace the conventional observation method with computer vision to resolve the limitation of human vision. The notable applications include pilling assessment [3][4][5][6], fabric texture analysis [7-10], fabric defects identification [11][12][13][14], and fiber morphological measurement [15,16]. In our earlier work [1], the methodology of a digital image-signal approach was proposed for the automatic measurement of yarn wet snarls from an image of snarled yarn samples captured in a water bath. After the image acquisition and pre-processing, the two digital signal-processing methods of Fast Fourier Transform (FFT) and Adaptive-orientated Orthogonal-projecAbstract This paper is the second part of a series reporting the recent development of a computerized method for the automatic measurement and recognition of yarn wet snarls from an image of snarled yarn samples captured in a water bath. In our earlier work, a digital image-signal approach for fully computerized yarn-snarl measurement was developed and the effects of various influencing factors on the recognition algorithms were numerically examined. In this paper, the feasibility and accuracy of the fully computerized method on the measurement of actual yarn wet snarls are evaluated through laboratory experiments. One hundred percent cotton ring spun single yarns of 7, 10, 16, and 20 Ne are prepared and used for the evaluation. In addition to the number of snarl turns per unit length, the snarl height and width of the yarn samples are also objectively measured by using the computerized method. The measurement results obtained by the computerized method are analyzed and compared with those measured manually by using a twist tester and an interactive computer method.

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Fabric Surface Roughness Evaluation Using Wavelet-Fractal Method

Cited by 25 publications

References 11 publications

Garment smoothness appearance evaluation through computer vision

Garment smoothness appearance evaluation through computer vision

Data mining and machine learning in textile industry

Evaluation of a Digital Image-signal Approach on the Automatic Measurement of Cotton Yarn Snarls

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