Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (03): 146-152.doi: 10.13475/j.fzxb.20180403907

• Management & Information • Previous Articles     Next Articles

Identification of wool and cashmere based on multi-feature fusion image analysis technology

XING Wenyu1, DENG Na1, XIN Binjie2(), YU Chen2   

  1. 1. School of Electric and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
    2. Fashion College, Shanghai University of Engineering Science, Shanghai 201620, China
  • Received:2018-04-17 Revised:2018-11-29 Online:2019-03-15 Published:2019-03-15
  • Contact: XIN Binjie E-mail:xinbj@sues.edu.cn

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Abstract:

For rapid identification of wool and cashmere, a method based on the multi-feature fusion for the fiber identification was proposed. Firstly, the images of wool and cashmere fibers were captured by an optical microscope and a digital camera. Secondly, two kinds of preprocessing operations were carried out respectively, and the binary images of single fiber image and background free fiber were obtained. Then, the texture parameters of the first kind of cashmere and wool fiber images were extracted by the gray level co-occurrence matrix algorithm and the diameter parameters of the second kinds of fiber images were extracted based on the central axis algorithm. Finally, the texture and morphological feature parameters were fused into multidimensional array and the clustering analysis was carried out by the K-means algorithm. The experimental results show that the average identification rate of the algorithm proposed can reach 95.25%. Compared with the conventional single fiber feature extraction algorithm, the recognition rate is high, which confirmed that this method can be used for automatic classification and identification of cashmere and wool fibers.

Key words: wool, cashmere, multi-feature fusion, fiber identification, gray level co-occurrence matrix, central axis method, K-means algorithm

CLC Number: 

  • TS102.3

Fig.1

Original microscope images of cashmere (a) and wool (b) (×400)"

Fig.2

Preprocessing diagram of cashmere images. (a) Original image; (b) Gray image; (c) Contrast stretching;(d) Logarithm nonlinear transformation; (e) Histogram equalization"

Fig.3

Gray-level histogram of original image (a) and enhanced image (b)"

Fig.4

Preprocessing diagram of wool images. (a) Contrast stretching; (b) Logarithm nonlinear transformation;(c) Histogram equalization; (d) Morphological processing; (e) Subtraction operation; (f) Fiber binary image"

Fig.5

Gray histogram of image enhancement"

Fig.6

Texture feature change curves"

Fig.7

Process images of fiber contour extraction. (a)Seed selection; (b) Region growth; (c)Noise removal; d)Thinning of contour lines"

Fig.8

Image of fiber counter"

Fig.9

Image of fiber axis"

Fig.10

Flow chart of algorithm"

Tab.1

Identification rate of single level co-occurrence matrix algorithm"

羊绒与羊毛根数比 根数识别率/%
4∶6 92.3
5∶5 91.5
6∶4 92.0

Tab.2

Recognition rate of method proposed"

样本
名称		 样本数量/
根		 识别正确
数量/根		 根数识别
率/%
羊绒 200 189 94.50
羊毛 200 192 96.00
合计 400 381 95.25
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