Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (05): 66-71.doi: 10.13475/j.fzxb.20190601606

• Textile Engineering • Previous Articles     Next Articles

Visual feature coding and wrinkle assessment of repeatedly laundered fabrics

XU Pinghua1,2(), MAO Hailin3, SHEN Hongying4, DING Xuemei4   

  1. 1. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. Clothing Engineering Research Center of Zhejiang Province, Hangzhou, Zhejiang 310018, China
    3. Jiangsu Yihai Clothing Co., Ltd., Nantong, Jiangsu 226007, China
    4. College of Fashion and Design,Donghua University, Shanghai 200051, China
  • Received:2019-06-10 Revised:2020-01-22 Online:2020-05-15 Published:2020-06-02

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Abstract:

In order to improve the consistency between subjective and objective ratings for the repeatedly laundered fabrics, an automatic method of wrinkling assessment based on visual feature coding and multi-classification support vector machine (SVM) was proposed. 450 representative wrinkled fabric images including 6 standard samples were selected as the training set. In addition, the human visual focusing mechanism was simulated, and the sparse coding method was utilized to extract the feature vector chain codes from 9 half-level image sub-databases. 200 wrinkled images as testing samples in this experiment were classified by linear multi-classification SVM. Results show that consistency between subjective and objective rating reaches 95.1%, rating precision is 0.1 and rating speed for single sample is less than 6 seconds, which meet the current commercial rating requirement for effective evaluations of fabric finishing, detergent products and care equipment.

Key words: smoothness appearance, visual feature, sparse coding, subjective and objective rating, fabric appearance

CLC Number: 

  • TS107.4

Fig.1

Hardware of image acquisition"

Fig.2

Partial fabric sample images. (a) Grade 1;(b) Grade 1.5; (c) Grade 2 (d) Grade 2.5;(e) Grade 3; (f) Grade 3.5; (g) Grade 4;(h) Grade 4.5; (i) Grade 5"

Fig.3

Pipeline of D-SIFT feature extraction"

Fig.4

Visual feature coding vocabulary. (a) Grade 1; (b) Grade 1.5; (c) Grade 2; (d) Grade 2.5; (e) Grade 3; (f) Grade 3.5; (g) Grade 4; (h) Grade 4.5; (i) Grade 5"

Fig.5

Output of sub-obj rating consistency"

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