纺织学报 ›› 2020, Vol. 41 ›› Issue (08): 145-151.doi: 10.13475/j.fzxb.20190806507

• 综合述评 • 上一篇    下一篇

服装款式图识别与样板转换技术研究进展

李涛1, 杜磊1,2, 黄振华1, 蒋玉萍1, 邹奉元1,2()   

  1. 1.浙江理工大学 服装学院, 浙江 杭州 310018
    2.浙江理工大学 浙江省服装工程技术研究中心, 浙江 杭州 310018
  • 收稿日期:2019-08-26 修回日期:2020-04-09 出版日期:2020-08-15 发布日期:2020-08-21
  • 通讯作者: 邹奉元
  • 作者简介:李涛(1993—),男,博士生。主要研究方向为人体工程与服装数字化技术。
  • 基金资助:
    国家自然科学基金面上项目(11671009);国家级大学生创新创业训练计划项目(201910338011);浙江省教育厅一般科研项目(Y201840287)

Review on pattern conversion technology based on garment flat recognition

LI Tao1, DU Lei1,2, HUANG Zhenhua1, JIANG Yuping1, ZOU Fengyuan1,2()   

  1. 1. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. Apparel Engineering Research Center of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2019-08-26 Revised:2020-04-09 Online:2020-08-15 Published:2020-08-21
  • Contact: ZOU Fengyuan

RichHTML

8

PDF (PC)

217

摘要：

为揭示款式图与服装样板之间的转换机制,概述了依据款式图进行人工制板的过程和方法,阐述了服装款式图特征参数识别和机器学习识别2种识别方法,重点论述在款式图识别基础上的样板转换技术,并对其优缺点进行了分析。参数化和匹配转换是目前最常用的样板转换方法:参数化转换适用于款式结构较为定型的服装,转换精度高,但不同款式图需要建立各自的转换模型;匹配转换可实现样板的快速转换,鲁棒性高,且规避了样板设计规则,不足是精度较低,且前期需要构建庞大的数据集作为训练集。研究认为,未来可从细化款式图识别粒度、服装款式图面料参数样板多领域跨域匹配、部件化样板智能生成3个领域开展相关研究。

关键词: 服装款式图, 样板转换, 图像识别, 部件化样板, 参数化转换, 匹配转换

Abstract:

In order to reveal the influence of the conversion mechanism between garment flat and the pattern, this paper reviewed the processes and methods of making pattern manually according to the garment flat, emphasizing on the recognition methods of the characteristic parameters and the machine learning recognition. Discussions on the pattern conversion technology were carried out based on the garment flat recognition, and its advantages and disadvantages were analyzed. The research shows that parametric and match conversion are the most commonly used conversion methods. Parametric conversion is suitable for clothing with relatively fixed style. The conversion accuracy is high, but different garment flat needs to establish different conversion models. Match conversion can facilitate fast conversion of pattern with high robustness, and permit pattern making rules not to be followed. The disadvantage is that the accuracy is low and large data sets need to be built as training sets at the early stage. The review suggests that in the future, relevant researches should be carried out in three fields, i.e., fining garment flat recognition granularity, garment flat fabric parameter pattern multi-domain matching and componentized pattern generation.

Key words: garment flat, pattern conversion, image recognition, componentized pattern, parametric conversion, match conversion

中图分类号: 

  • TS941.17

图1

翻领模型识别步骤"

表1

款式图与样板之间的数据转换"

关键部位 图距 实距 放松量 净尺寸 加放尺寸
胸围 0.12 2.54 8 84 92
腰围 0.10 2.00 6 68 74
臀围 0.06 1.27 4 90 94

图2

款式图到样板参数化转换过程"

图3

基准样板调用示意图"

图4

样板匹配转换示意图"

[1] HU Z H, DING Y S, ZHANG W B, et al. An interactive co-evolutionary CAD system for garment pattern design[J]. Computer-Aided Design, 2008,40(12):1094-1104.
[2] LIU K X, ZENG X Y, BRUNIAUX P, et al. 3D interactive garment pattern-making technology[J]. Computer-Aided Design, 2018,104:113-124.
[3] 董晨雪. 服装款式图像自动识别研究[D]. 上海: 东华大学, 2013: 33-35.
DONG Chenxue. The study of auto-identification of garment illustration[D]. Shanghai: Donghua University, 2013: 33-35.
[4] DING Y S, HU Z H, ZHANG W B. Multi-criteria decision making approach based on immune co-evolutionary algorithm with application to garment matching problem[J]. Expert Systems with Applications, 2011,38:10377-10383.
[5] 丁敏敏. 服装衣领款式图数字化识别研究与实现[D]. 上海: 东华大学, 2007: 15-24.
DING Minmin. The study and achievemnet of digital recognition on collar illustration based on autocad[D]. Shanghai: Donghua University, 2007: 15-24.
[6] AN L X, LI W. Lapel pattern recognition in flat sketches based on lapel model[J]. Journal of Donghua Univer-sity (English Edition), 2014,31(4):463-467.
[7] XU J, MOK T, YEE R W Y, et al. A web-based design support system for fashion technical sketches[J]. International Journal of Clothing Science and Technology, 2016,28(1):130-160.
[8] AN L X, LI W. An integrated approach to fashion flat sketches classification[J]. International Journal of Clothing Science and Technology, 2014,26(5):346-366.
[9] LIN C, PUN C M, VONG C M, et al. Efficient shape classification using region descriptors[J]. Multimedia Tools & Applications, 2017,76(1):83-102.
[10] 李东, 万贤福, 汪军. 采用傅里叶描述子和支持向量机的服装款式识别方法[J]. 纺织学报, 2017,38(5):122-127.
LI Dong, WAN Xianfu, WANG Jun. Clothing style recognition approach using fourier descriptors and support vector machines[J]. Journal of Textile Research, 2017,38(5):122-127.
[11] HOU A L, ZHAO L Q, SHI D C. Garment image retrieval based on multi-features[C]// 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE). Changchun: [s.n.], 2010: 194-197.
[12] 吴欢, 丁笑君, 李秦曼, 等. 采用卷积神经网络CaffeNet模型的女裤廓形分类[J]. 纺织学报, 2019,40(4):117-121.
WU Huan, DING Xiaojun, LI Qinman, et al. Classification of women's trousers silhouette using convolution neural network CaffeNet model[J]. Journal of Textile Research, 2019,40(4):117-121.
[13] DI W, WAH C, BHARDWAJ A, et al. Style finder: fine-grained clothing style recognition and retrie-val[C]// Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition Workshops. Washington DC: IEEE Computer Society, 2013: 1-6.
[14] SABOUR S, FROSST N, HINTON G E. Dynamic routing between capsules[C]// 31st Conference on Neural Information Processing Systems (NIPS). Long Beach: Neural Information Processing Systems Foundation Inc, 2017: 3856-3866.
[15] LEE J, SUL I. Construction of garment pattern shape information system using image analysis and shape recognition techniques[J]. International Journal of Clothing Science and Technology, 2016,28(4):543-555.
[16] 刘肖, 邓咏梅, 陈金广. 男衬衫一片袖款式图到纸样图的转换方法[J]. 纺织学报, 2016,37(3):119-126.
LIU Xiao, DENG Yongmei, CHEN Jinguang. Transformation from style to pattern for men's shirt with one-piece sleeve[J]. Journal of Textile Research, 2016,37(3):119-126.
[17] LIU K X, ZENG X Y, WANG J P, et al. Parametric design of garment flat based on body dimension[J]. International Journal of Industrial Ergonomics, 2018,65:46-59.
[18] BAEK S Y, LEE K. Parametric human body shape modeling framework for human-centered product design[J]. Computer-Aided Design, 2012,44(1):56-67.
[19] 杜劲松, 陈哲, 方方. 服装款式图的空间结构研究[J]. 东华大学学报(自然科学版), 2014,40(5):582-586.
DU Jinsong, CHEN Zhe, FANG Fang. Study on the garment drawing's spatial structer[J]. Journal of Donghua University (Natural Science Edition), 2014,40(5):582-586.
[20] XIU Y, WAN Z K, CAO W. A constructive approach towards parametric pattern-making model[J]. Textile Research Journal, 2011,81(10):979-991.
[21] 王燕珍. 服装款式识别数字化表现原则[J]. 纺织学报, 2007,28(12):94-98, 106.
WANG Yanzhen. Digital principles for identification of garment styles[J]. Journal of Textile Research, 2007,28(12):94-98, 106.
[22] GU B F, GU P Y, LIU G L. Pattern generation rules for basic women's suits[J]. International Journal of Clothing Science and Technology, 2017,29(3):330-348.
[23] JING J F, LI Q, LI P F, et al. A new method of printed fabric image retrieval based on color moments and gist feature description[J]. Textile Research Journal, 2015,86(11):1137-1150.
[24] LUU V H, DINH V K, LUONG N H H, et al. Improving the bag-of-words model with spatial pyramid matching using data augmentation for fine-grained arbitrary-oriented ship classification[J]. Remote Sensing Letters, 2019,10(9):826-834.
[25] GUAN C Y, QIN S F, LONG Y. Apparel-based deep learning system design for apparel style recommen-dation[J]. International Journal of Clothing Science and Technology, 2019,31(3):376-389.
[26] SONG J F, YU Q, SONG Y Z, et al. Deep spatial-semantic attention for fine-grained sketch-based image retrieval[C]// Proceedings of 2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 5552-5561.
[27] YU Q, YANG Y X, SONG Y Z, et al. Sketch-a-net that beats humans[C]// Proceedings of the British Machine Vision Conference. Swansea: BMVA Press, 2015: 1-7.
[28] SUN G L, WU X, PENG Q. Part-based clothing image annotation by visual neighbor retrieval[J]. Neurocomputing, 2016,213(12):115-124.
[29] WANG Y, LIANG W, SHEN J B, et al. A deep coarse-to-fine network for head pose estimation from synthetic data[J]. Pattern Recognition, 2019,94:196-206.
[30] DONATI L, IOTTI E, MORDONINI G L, et al. Fashion product classification through deep learning and computer vision[J]. Applied Sciences, 2019,9(7):1385.
[31] HEISELE B, SERRE T, POGGIO T. A component-based framework for face detection and identifica-tion[J]. International Journal of Computer Vision, 2007,74(2):167-181.
[32] AK K E, LIM J, THAM J Y, et al. Which shirt for my first date? Towards a flexible attribute-based fashion query system[J]. Pattern Recognition Letters, 2018,112:212-218.
[33] WANG B, LI J T, ZENG J P, et al. Construction of level of details in garment image skeleton[J]. International Journal of Clothing Science and Technology, 2016,28(1):92-104.
[34] WANG T Y, CEYLAN D, POPOVIC J, et al. Learning a shared shape space for multimodal garment design[J]. ACM Transactions on Graphics, 2017,36(4):1-13.
[35] CHOPRA S, HADSELL R, LECUN Y. Learning a similarity metric discriminatively with application to face verification[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington DC: IEEE Computer Society, 2005: 1-8.
[36] YANG Y C, ZHANG W Y, SHAN C. Investigating the development of digital patterns for customized appa-rel[J]. International Journal of Clothing Science and Technology, 2007,19(3/4):167-177.
[37] LIU K X, WANG J P, TAO X Y, et al. Fuzzy classification of young women's lower body based on anthropometric measurement[J]. International Journal of Industrial Ergonomics, 2016,55:60-68.
[38] LIU K X, WANG J P, KAMALHA E, et al. Construction of a prediction model for body dimensions used in garment pattern making based on anthropometric data learning[J]. The Journal of The Textile Institute, 2017,108(12):2107-2114.
[39] WANG Z J, WANG J P, XING Y M, et al. Estimating human body dimensions using RBF artificial neural networks technology and its application in activewear pattern making[J]. Applied Science, 2019,9(6):1140.
[40] 刘为敏, 谢红. BP神经网络下的智能化合体服装样板生成[J]. 纺织学报, 2018,39(7):116-121.
LIU Weimin, XIE Hong. Generation of intelligent fitting pattern based on BP neural network[J]. Journal of Textile Research, 2018,39(7):116-121.
[41] 丛芳, 赵野军. 基于神经网络的服装制版系统[J]. 纺织学报, 2008,29(1):129-132.
CONG Fang, ZHAO Yejun. Realization of the intelligent garment pattern system based on neural networks[J]. Journal of Textile Research, 2008,29(1):129-132.
[1] 许倩, 陈敏之. 基于深度学习的服装丝缕平衡性评价系统[J]. 纺织学报, 2019, 40(10): 191-195.
[2] 黎聪 闫学娜 曾祥忠 梁猛 张莹. 应用一维傅里叶变换的剖幅区自动识别与定位[J]. 纺织学报, 2016, 37(01): 147-151.
[3] 夏帆 刘翔. 基于服装设计要素和法则的智能配搭设计[J]. 纺织学报, 2015, 36(07): 94-99.
[4] 张海波 黄铁军 修毅 赵野军 章江华. 基于神经网络的男西装图像情感语义识别[J]. 纺织学报, 2013, 34(12): 138-0.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号　 京公网安备11010502044800号
版权所有 © 2021 《纺织学报》编辑部
地址： 北京市朝阳区延静里中街3号主楼6层《纺织学报》杂志社　邮政编码：100025　
电话：010-65017711,65917740　传真：010-65016539　Email:fangzhixuebao@vip.126.com
本系统由北京玛格泰克科技发展有限公司设计开发