洗后织物外观视觉特征编码与折皱评级

doi:10.13475/j.fzxb.20190601606

纺织学报 ›› 2020, Vol. 41 ›› Issue (05): 66-71.doi: 10.13475/j.fzxb.20190601606

洗后织物外观视觉特征编码与折皱评级

徐平华¹^,²(), 冒海琳³, 沈红影⁴, 丁雪梅⁴

1.浙江理工大学服装学院, 浙江杭州 310018
2.浙江省服装工程技术研究中心, 浙江杭州 310018
3.江苏依海服饰有限公司, 江苏南通 226007
4.东华大学服装与艺术设计学院, 上海 200051

收稿日期:2019-06-10 修回日期:2020-01-22 出版日期:2020-05-15 发布日期:2020-06-02
作者简介:徐平华(1984—),男,副教授,博士。主要研究方向为纺织品服装数字化检测。E-mail: shutexph@163.com。
基金资助:
国家自然科学基金项目(61702460);浙江省服装工程技术研究中心开放基金项目(2018FZKF11);服装设计国家级虚拟仿真实验教学中心项目(zx2019004);浙江理工大学一流学科优秀博士专项(2017YBZX15);浙江理工大学科研启动基金项目(17072067-Y)

Visual feature coding and wrinkle assessment of repeatedly laundered fabrics

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

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

摘要/Abstract

摘要：

为提高洗后织物外观平整度主客观评级的一致率,提出了基于视觉特征编码与多分类支持向量机相结合的织物折皱自动评级方法。首先,将6块立体标准样板扩充至450幅具有代表性的织物洗后折皱图像作为训练集;其次,模拟人眼视觉聚焦机制,利用稀疏编码技术,分别解析9个半级图像子库的多尺度编码特征,并构建形成各级特征向量链码;最后,利用线性多分类支持向量机算法评定其余200幅测试样本。实验结果表明:该方法主客观一致率为95.1%,评级精度为0.1级,单样本评级速度小于6 s,能够满足当前织物整理剂、洗涤剂、洗护设备等护理效果的商用评级用途。

关键词: 平整度, 视觉特征, 稀疏编码, 主客观一致率, 织物外观

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

中图分类号:

TS107.4

徐平华, 冒海琳, 沈红影, 丁雪梅. 洗后织物外观视觉特征编码与折皱评级[J]. 纺织学报, 2020, 41(05): 66-71.

XU Pinghua, MAO Hailin, SHEN Hongying, DING Xuemei. Visual feature coding and wrinkle assessment of repeatedly laundered fabrics[J]. Journal of Textile Research, 2020, 41(05): 66-71.

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洗后织物外观视觉特征编码与折皱评级

Visual feature coding and wrinkle assessment of repeatedly laundered fabrics

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