Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (05): 97-103.doi: 10.13475/j.fzxb.20210500207

• Textile Engineering • Previous Articles     Next Articles

Lightness prediction method for shaded satin fabrics based on image reconstruction of light and shadow

ZHENG Wenjie1, ZHANG Aidan1,2()   

  1. 1. College of Textile Science and Engineering(International Institute of Silk), Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. Silk and Fashion Culture Research Center of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2021-05-06 Revised:2022-01-25 Online:2022-05-15 Published:2022-05-30
  • Contact: ZHANG Aidan E-mail:zad.andan@163.com

RichHTML

13

PDF (PC)

57

Abstract:

In order to obtain the brightness value of shaded satin fabrics more accurately, a lightness prediction method based on the shadow reconstruction of fabric image was proposed. The fabric image was separated into three layers: warp and weft graphic layer, shadow layer and texture layer, and the actual color values of the warp and the weft yarn were assigned to the relative area of the three separated image layers when their pixel numbers were counted respectively. After the fabric image brightness value were reconstructed by adding the lightness values, fitting analysis were implemented between the reconstructed one and the actual color measurement data of the fabric. According to the analysis results, the warp and weft area rate, shadow area rate and texture area rate were selected as the independent variables. A regression model for the lightness prediction of the fabric was established based on the three independent variables, and random samples were selected to test the accuracy of the model. The results show that the proximity between the lightness of the warp and weft graphic layer and the measured lightness is 0.15, and the proximity increases from 0.76 to 0.89 when the shadow layer and material layer are added in sequence. The overall relative error of the fabric lightness prediction by the model is within ±4%, the prediction results can be used as references for the design of the fabric with shaded weaves.

Key words: shaded satin weave, image reconstruction, regression analysis, fabric brightness, prediction model

CLC Number: 

  • TS141.9

Fig.1

Diagram of light and shadow reconstruction method of fabric images"

Fig.2

Layered extraction of fabric images and its image illustration. (a)Original digital image; (b)Preprocess window image; (c)Connection area image; (d)Open operational image; (e)White warp image; (f)Black weft image; (g)Shadow image on the white warp; (h)Texture image on black weft"

Fig.3

Diagram of fabric lightness"

Tab.1

One-way analysis of variance for fabric lightness"

试样编号 L1与Lpi L1与Lti L1与Lzi
F值 p值 F值 p值 F值 p值
1* 2.53 0.123 0.06 0.812 0.00 0.948
2* 1.51 0.229 0.27 0.608 0.06 0.815
3* 3.21 0.084 0.03 0.871 0.01 0.917

Tab.2

Results of stepwise regression analysis"

模型编号 R2 调整R2 F值 p值 剔除变量
1# 0.985 0.982 336.008 0 x3、x4
2# 0.961 0.958 343.01 0 x1、x4、x7
3# 0.953 0.950 344.864 0 x1、x3、x4、x7

Tab.3

Results of prediction model validation"

样品
编号		 x1 x2 x3 实测
数据		 预测
数据		 误差
率%
1 2.382 0.299 0.035 50.84 49.82 -2.01
2 2.994 0.409 0.012 65.37 64.59 -0.57
3 2.330 0.219 0.023 52.55 52.08 -0.89
4 2.522 0.377 0.006 68.15 68.80 0.96
5 2.319 0.204 0.039 44.92 44.56 -0.81
6 3.096 0.408 0.006 71.02 69.93 -1.93
7 2.225 0.173 0.009 58.71 59.27 0.96
8 5.834 0.468 0.005 73.91 72.76 -1.55
9 2.255 0.162 0.034 34.97 36.14 3.36
10 2.276 0.256 0.010 60.49 59.86 -1.05
11 2.268 0.260 0.048 38.36 37.59 -2.01
12 2.410 0.210 0.008 63.55 63.20 -0.56
[1] 傅艺扬, 刘妹琴, 樊臻, 等. 基于纹理滤波和颜色聚类的提花织物纹样自动提取方法[J]. 丝绸, 2019, 12(56): 9-15.
FU Yiyang, LIU Meiqin, FAN Zhen, et al. Automatic pattern extraction of jacquard fabric based on texture filtering and color clustering[J]. Journal of Silk, 2019, 12(56): 9-15.
[2] 化春键, 孙康康, 陈莹. 基于改进FCM聚类的网孔织物图像分割算法[J]. 光电子·激光, 2020, 31(8): 857-864.
HUA Chunjian, SUN Kangkang, CHEN Ying. Method of mesh fabric image segmentation based on improved fuzzy C-means clustering[J]. Journal of Optoelectro-nics·Laser, 2020, 31(8): 857-864.
[3] 赵艳, 左保齐. 机器视觉在织物疵点检测上的应用研究综述[J]. 计算机工程与应用, 2020, 56(2): 11-17.
ZHAO Yan, ZUO Baoqi. Analysis on application of machine vision in fabric fefect detection[J]. Computer Engineering and Applications, 2020, 56(2): 11-17.
[4] 周赳, 罗秉芬, 叶莹洁. 以影光组织为基础的高花效果提花织物设计[J]. 纺织学报, 2017, 38(5): 49-52.
ZHOU Jiu, LUO Bingfen, YE Yingjie. Design of high pattern jacquard fabric based on shaded weaves[J]. Journal of Textile Research, 2017, 38(5): 49-52.
[5] 周赳, 白琳琳. 纬二重渐变全遮盖结构设计研究与实践[J]. 纺织学报, 2018, 39(1): 32-37.
ZHOU Jiu, BAI Linlin. Design research and practice on gradient weft-full-backed structure[J]. Journal of Textile Research, 2018, 39(1): 32-37.
[6] 罗来丽, 胡丁亭, 唐澜倩, 等. 基于全显色结构的三组纬提花织物混色特征研究[J]. 丝绸, 2011, 48(10): 28-32.
LUO Laili, HU Dingting, TANG Lanqian, et al. Research on color mixture characteristics of triple-weft jacquard fabric with full-color structure[J]. Journal of Silk, 2011, 48(10): 28-32.
[7] 黄紫娟. 一经一纬单层色织物明度模型建立初探[J]. 国外丝绸, 2008(4): 33-34,40.
HUANG Zijuan. A preliminary study on the establish-ment of lightness model of single layer color fabric at one weft[J]. Silk Textile Technology Overseas, 2008(4): 33-34,40.
[8] 罗来丽, 王春燕, 周赳. 基于全显色结构的二组纬提花织物的混色特征[J]. 纺织学报, 2012, 33(4): 39-44.
LUO Laili, WANG Chunyan, ZHOU Jiu. Research on mixed color characteristics of double weft jacquard fabric with colored wefts[J]. Journal of Textile Research, 2012, 33(4): 39-44.
[9] 杨丹, 赵海滨, 龙哲. MatLab图像处理实例详解[M]. 北京: 清华大学出版社, 2013:345.
YANG Dan, ZHAO Haibin, LONG Zhe. Detailed illustration of image processing in MatLab[M]. Beijing: Tsinghua University Press, 2013:345.
[10] 周赳, 吴文正. 无彩数码提花织物的创新设计原理和方法[J]. 纺织学报, 2006, 29(4): 1-5.
ZHOU Jiu, FRANKIE Ng. Innovative principle and method of colorless digital jacquard fabric design[J]. Journal of Textile Research, 2006, 29(4): 1-5.
doi: 10.1177/004051755902900101
[11] 姚金波, 王志意, 董敏, 等. 温湿环境下棉织物的抗皱性能变化规律及其预测模型[J]. 天津工业大学学报, 2020, 39(5): 43-49.
YAO Jinbo, WANG Zhiyi, DONG Min, et al. Change and prediction model of crease resistance of cotton fabric under warm and humid environment[J]. Journal of Tiangong University, 2020, 39(5): 43-49.
[12] 杨阳, 俞欣, 章为敬, 等. 针织面料凉爽性能的评价方法及其预测模型[J]. 纺织学报, 2021, 42 (3): 95-101.
YANG Yang, YU Xin, ZHANG Weijing, et al. Evaluation method and prediction model establishment of cooling performance of knitted fabrics[J]. Journal of Textile Research, 2021, 42 (3): 95-101.
[1] WANG Lijun, MA Ximing, DING Yinjia, CHEN Chengyi. Influence of wind speed on moisture resistance of single-layer and double-layer combined sportswear knit fabrics [J]. Journal of Textile Research, 2021, 42(07): 151-157.
[2] YANG Yang, YU Xin, ZHANG Weijing, ZHANG Peihua. Evaluation method and prediction model establishment of cooling performance of knitted fabrics [J]. Journal of Textile Research, 2021, 42(03): 95-101.
[3] ZHANG Xiaoxia, LIU Fengkun, MAI Wei, MA Chongqi. Prediction of loom efficiency based on BP neural network and its improved algorithm [J]. Journal of Textile Research, 2020, 41(08): 121-127.
[4] HUANG Qianqian, LI Jun. Research progress on mechanism of human thermal sensation under ambient temperature step change [J]. Journal of Textile Research, 2020, 41(04): 188-194.
[5] LIU Hong, DING Xuemei, WU Xiongying. Influence of wash load type of drum washer on textile motion and washing performance [J]. Journal of Textile Research, 2019, 40(06): 44-49.
[6] ZHOU Jie, MA Qiurui. Application of BP neural network in prediction of shapewear pressure [J]. Journal of Textile Research, 2019, 40(04): 111-116.
[7] . Preparation and properties of polypyrrole/cotton anti-microwave radiation fabric [J]. Journal of Textile Research, 2018, 39(09): 95-101.
[8] . Woven fabric texture representation and application based on K-SVD dictionary [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(02): 165-170.
[9] . Consumers decision mechanisms on apparel online retail channels [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(12): 146-152.
[10] . Sensibility assessment of spring and summer shirt yarn-dyed fabrics [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(08): 59-64.
[11] . Prediction model on twist shrindage of wool yarn [J]. Journal of Textile Research, 2016, 37(05): 32-36.
[12] . Predicting model for braiding angle based on initial braiding height and take-up speed [J]. JOURNAL OF TEXTILE RESEARCH, 2015, 36(09): 28-33.
[13] . Performance study of coffee-charcoal/PET filament weft-knitted mattress fabric [J]. Journal of Textile Research, 2015, 36(03): 37-42.
[14] . Use of EMD-ESN model  in temperature control system of hot roll [J]. JOURNAL OF TEXTILE RESEARCH, 2014, 35(7): 128-0.
[15] . Influence on heart rate variability indexes caused by clothing pressure developed by women’s basketball sports bra [J]. JOURNAL OF TEXTILE RESEARCH, 2014, 35(6): 100-0.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd