A novel digital analysis method for measuring and identifying of wool and cashmere fibers

•A novel digital analysis system is proposed to measure and identify the fibers.•K-means is used to realize automatic recognition and reduce the subjective error.•The recognition rate has an improvement compared with the traditional methods. The identification of wool and cashmere is always consider...

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Veröffentlicht in:Measurement : journal of the International Measurement Confederation Jg. 132; S. 11 - 21
Hauptverfasser: Xing, Wenyu, Xin, Binjie, Deng, Na, Chen, Yang, Zhang, Zhengye
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Elsevier Ltd 01.01.2019
Elsevier Science Ltd
Schlagworte:
Algorithms
Cashmere
Cluster analysis
Clustering
Contours
Digital cameras
Digital imaging
Feasibility studies
Feature extraction
Fiber
Fibers
Fineness
Fractal algorithm
Fractal analysis
Identification
Identification methods
K-means algorithm
Measurement
Optical fibers
Parallel-line algorithm
Shape
Wool
Measurement
Fractal algorithm
K-means algorithm
Identification
Parallel-line algorithm
Fiber
ISSN:0263-2241, 1873-412X
Online-Zugang:Volltext
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Zusammenfassung:•A novel digital analysis system is proposed to measure and identify the fibers.•K-means is used to realize automatic recognition and reduce the subjective error.•The recognition rate has an improvement compared with the traditional methods. The identification of wool and cashmere is always considered as a challenge in the field of textile and clothing industry. A novel digital analysis method based on fractal algorithm, parallel-line algorithm, and K-mean clustering algorithm was proposed in the paper to improve the accuracy of fiber measurement and identification. First, the original images of 600 cashmere fibers and 600 wool fibers were captured by optical microscope attached with a digital camera, the self-developed image preprocessing was carried out to obtain the binary image and contour image of cashmere and wool fibers. Then, the fractal algorithm was used to calculate the box-counting and information dimension of fiber binary images, and the parallel-line algorithm was used to measure the fiber fineness of the contour image. These features can be used as the representation of fiber surface morphological features. Finally, the cluster analysis of the extracted feature set was carried out by k-means algorithm, the specimens used for the identification include three sets of wool and cashmere fibers blended with different proportions. The experimental results showed that the accuracy of the fractal-based and diameter-based identification of three sets with different fiber blending proportions was higher than that of traditional methods, the average identification accuracy rate was 97.47%. Through this investigation, the novel digital analysis method proposed in the paper was proved to be feasible to measure and identify the wool and cashmere fibers, it had the protentional application of digital and automatic fiber classification in the future, in terms of both equipment, method and standard.
Bibliographie:ObjectType-Article-1
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ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2018.09.032