Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (04): 45-50.doi: 10.13475/j.fzxb.20190605606

• Textile Engineering • Previous Articles     Next Articles

Comparison of feature extracting and matching methods for fabric patterns

WANG Hui1, SUN Jie1, DING Xiaojun1,2, LONG Ying1, ZOU Fengyuan1,2,3()   

  1. 1. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. Zhejiang Provincial Research Center of Clothing Engineering Technology, Zhejiang Sic-Tech University,Hangzhou, Zhejiang 310018, China
    3. Zhejiang Garment Personalized Customization Collaborative Innovation Center, Hangzhou, Zhejiang 310018, China
  • Received:2019-06-24 Revised:2020-01-18 Online:2020-04-15 Published:2020-04-27
  • Contact: ZOU Fengyuan E-mail:zfy166@zstu.edu.cn

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Abstract:

In fabric pattern recognition, variations in image scaling, rotating, folding and other deformation in the sampling process cause errors, and this paper investigated the improvement of the exacting and matching method for fabric patterns. To explore the applicability of feature extracting and matching methods for complicated patterns, the river cliff water pattern was taken as experimental samples. 5 types of images of the fabric pattern feature were acquired under scaling, rotation, fuzzy, illumination and drape respectively, and fabric pattern features were extracted using scale-invariant feature transform(SIFT), speeded-up robust features (SURF), binary robust invariant scalable key-points (BRISK)algorithms. Euclidean distance method was adopted for matching calculation, eliminating false match points by random sample consensus algorithm. The results show that BRISK algorithm is the best in matching ratio, which is 87.10% on average. Folding is proven to have the greatest impacts on fabric feature matching, and the robustness of BRISK algorithm under folding change is better than SIFT and SURF algorithms. BRISK algorithmic speed is the fastest, taking an average time of 0.551 s to perform the image feature extraction and matching. In fabric pattern matching, BRISK algorithm is demonstrated better applicability than SIFT or SURF algorithms.

Key words: river cliff water pattern, feature extraction, binary robust invariant scalable key-points algorithm, feature matching, fabric pattern recognition

CLC Number: 

  • TS941

Fig.1

Process of obtaining jiangya sea water patterns"

Fig.2

Original image of 3 kinds of jiangya sea water patterns"

Fig.3

Image preprocessing. (a) Grayscale processing;(b) Image noise; (c) Median filtering preprocessing"

Fig.4

SIFT algorithm false match in scale zooming"

Fig.5

Matching effect of original image and fuzzy image. (a) BRISK algorithm matching; (b) BRISK/RANSAC algorithms matching"

Tab.1

Comparison of matching results of different ratio thresholds at drape state"

阈值 算法 原图特
征点数		 变换后特
征点数		 所有匹
配数		 准确匹
配数		 准确匹
配率/%
0.3 SIFT 1 393 1 616 545 452 82.94
SURF 1 393 1 616 545 452 82.94
BRISK 2 639 2 113 140 128 91.43
0.4 SIFT 1 393 1 616 820 645 78.66
SURF 1 393 1 616 820 645 78.66
BRISK 2 639 2 113 305 266 87.21
0.5 SIFT 1 393 1 616 1 147 809 70.53
SURF 1 393 1 616 1 147 809 70.53
BRISK 2 639 2 113 708 585 82.63
0.6 SIFT 1 393 1 616 1 265 855 67.59
SURF 1 393 1 616 1 265 855 67.59
BRISK 2 639 2 113 1 023 889 86.90
0.7 SIFT 1 393 1 616 1 374 907 66.01
SURF 1 393 1 616 1 374 907 66.01
BRISK 2 639 2 113 1 255 1 049 83.59
0.8 SIFT 1 393 1 616 1 543 963 62.41
SURF 1 393 1 616 1 543 963 62.41
BRISK 2 639 2 113 1 571 1 177 74.92
0.9 SIFT 1 393 1 616 1 629 990 60.77
SURF 1 393 1 616 1 629 990 60.77
BRISK 2 639 2 113 2 010 1 202 59.80

Tab.2

Comparison of matching accuracy of three algorithms"

类别 k0 准确匹配率/%
SIFT SURF BRISK
尺度 0.6 35.63 52.01 80.04
旋转 0.6 82.60 81.38 87.10
模糊 0.6 48.85 51.67 85.86
光照 0.6 70.83 67.45 88.63
褶皱 0.6 51.71 52.22 66.78

Tab.3

Comparison of matching time of different algorithms under five kinds of changess"

变化 匹配时间
SIFT SURF BRISK
尺度均值 1.382 1.612 0.504
旋转均值 0.817 1.046 0.330
模糊均值 1.282 1.497 0.444
光照均值 1.593 1.106 0.621
褶皱均值 1.607 1.914 0.855
均值 1.336 1.435 0.551

Fig.6

Fabric patterns.(a)Pattern A;(b)Pattern B"

Tab.4

Average correct matching rate of fabric patterns A and B under five variations%"

纹样名称 类别 SIFT SURF BRISK
纹样A 尺度 62.40 63.81 74.30
旋转 78.04 80.52 81.18
模糊 63.63 63.08 81.13
光照 86.93 89.18 96.17
褶皱 66.64 77.95 77.95
纹样B 尺度 50.43 47.83 84.11
旋转 84.83 86.07 93.02
模糊 43.51 44.56 88.82
光照 83.97 83.69 98.13
褶皱 66.69 66.68 88.19
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