Formation of digital yarn black board using sequence images

Li, Zhongjian; Pan, Ruru; Gao, Weidong

doi:10.1177/0040517514563725

Cited by 10 publications

(7 citation statements)

References 15 publications

(11 reference statements)

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“…Sengupta et al (2015) presented an algorithm including nine steps in their study [20]. Li et al (2016) proposed an image acquisition and processing system with a video camera to evaluate yarn appearance. Formation of a digital yarn blackboard was ensured by capturing sequence images of a moving yarn and extracting yarn diameter data after the image threshold and morphological opening operation [21].…”

Section: Introductionmentioning

confidence: 99%

The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing

Telli

2021

Tekstil Ve Konfeksiyon

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The resolution, quality and speed of the cameras have improved enormously in recent years. The combination of camera advancements and the software industry offers significant opportunities. In this study, an image processing approach for the determination of yarn hairiness was presented. Yarn images taken under a microscope were examined in MATLAB software. Seven different edge detection algorithms were used in order to separate the hairs from the yarn body. Seven different textural properties of obtained yarn images were compared with Zweigle hairiness test results. The findings have indicated that yarn hairiness can be clearly detected from microscope images with a six-step algorithm. The first four phases are grayscale, double format, 2D median filtering and histogram-fitting, respectively. The fifth stage is the edge detection algorithm and the sixth stage is the use of textural parameters. When compared with the Zweigle hairiness results, the most obvious finding to emerge from this study is that the best appropriate technique for edge detection was the Sobel method, and the textural parameter to be used in the evaluation was the standard deviation of matrix elements.

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Section: Introductionmentioning

confidence: 99%

The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing

Telli

2021

Tekstil Ve Konfeksiyon

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“…Currently, image analysis seemed to be a trend in developing methods to measure the yarn property [16][17][18][19][20][21][22][23][24]. It was generally believed that visual inspection was the best approach to evaluate the detail properties of the sized yarn more efficiently [25].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Measurement of Foam-Sized Yarn Properties from Yarn Sequence Images

Zhu

Cao

et al. 2018

Autex Research Journal

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Unlike the normal sizing method, the foam sizing had been proven to be a low-add-on technology. To investigate the effect of foam sizing, film thickness, sized-yarn evenness, and size penetration rate were necessary to evaluate the performances of foam-sized yarns. However, the conventional image analysis of sized-yarn cross sections primarily relied on artificial testing with a low efficiency. This paper proposed a novel dynamic method to measure the sized-yarn properties including film thickness, sized-yarn evenness, and size penetration rate based on yarn sequence images captured from a moving yarn. A method of dynamic threshold module was adopted to obtain threshold for segmenting yarns in the sequence images. K-means clustering algorithm was applied to segment pixels of the images into yarn and background. To further remove burrs and noise in the images, two judgment templates were carried out to extract the information of yarn core. The film thickness, sized-yarn evenness, and size penetration rate were measured based on the yarn core of each frame in sequence images. In order to compare with the experimental results of the dynamic method, the yarn properties of the same samples were tested by static and artificial testing. Results revealed that the proposed method could efficiently and accurately detect the film thickness, sized-yarn evenness, and size penetration rate.

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“…The diameter of yarn is the direct parameter to evaluate yarn unevenness, and hence a rapid way of obtaining accurate yarn diameter is meaningful in the textile industry. The Uster Evenness Tester is a dominant instrument for testing yarn evenness, which uses capacitive sensors to check the distribution of yarn mass [3]. However, this method depends on the testing environmental conditions, as the capacitors are usually affected by the temperature and ambient humidity, and the resolution of this method is relatively low when the capacitors sample the data every 8 mm (e.g.…”

Section: N Introductionmentioning

confidence: 99%

Real-time Segmentation of Yarn Images Based on an FCM Algorithm and Intensity Gradient Analysis

Pan

Wang

et al. 2016

F&TinEE

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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.

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Formation of digital yarn black board using sequence images

Cited by 10 publications

References 15 publications

The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing

The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing

Dynamic Measurement of Foam-Sized Yarn Properties from Yarn Sequence Images

Real-time Segmentation of Yarn Images Based on an FCM Algorithm and Intensity Gradient Analysis

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