Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (12): 111-117.doi: 10.13475/j.fzxb.20200502607

• Apparel Engineering • Previous Articles     Next Articles

Automatic identification of young women's neck-shoulder shapes based on images

WANG Ting1, GU Bingfei1,2,3()   

  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. Key Laboratory of Silk Culture Inheriting and Products Design Digital Technology, Ministry of Culture and Tourism, Hangzhou, Zhejiang 310018, China
  • Received:2020-05-13 Revised:2020-08-31 Online:2020-12-15 Published:2020-12-23
  • Contact: GU Bingfei E-mail:gubf@zstu.edu.cn

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Abstract:

In order to facilitate the automatic identification of young women's neck-shoulder shapes, 15 neck-shoulder shape parameters of 202 young women were measured in the form of three-dimensional point cloud data, and the parameters with a large degree of dispersion were determined through analysis, including the shoulder angle, back angle, shoulder depth/width ratio and armpit depth/width ratio. Combined with these four important body parameters, the neck-shoulder shape of young women was classified following the established classification rules. Based on the front and side images, the parameters required for neck-shoulder shape classification were obtained by extracting the human body contour and identifying the feature points, and the automatic identification of the neck-shoulder shape was achieved according to the body type classification rules. The results show that young women's neck-shoulder shape is divided into four categories, namely round wide shoulder, flat narrow shoulder, round drop shoulder, hunchback flat shoulder, accounting for 25.53%, 23.94%, 25.59% and 23.94%, respectively, of the total sample. The identification of the neck-shoulder shape based on the front and side images of 40 test samples is verified, and the accuracy ratio reaches 90%, indicating that the neck-shoulder shape automatic identification system constructed using this method is effective.

Key words: neck-shoulder shape, body classification, image, size extraction, automatic identification

CLC Number: 

  • TS941.17

Tab.1

Measurement and calculation of neck-shoulder shape parameters"

序号 指标 测量与计算方法 序号 指标 测量与计算方法
1 身高(H) 头顶点至地面的垂直距离 8 颈高比(RhNP) 颈点高(HNP)/身高(H)
2 颈点高(HNP) 侧面头部与颈部交接点至地面的垂直距离 9 侧颈高比(RhSNP) 侧颈点高(HSNP)/身高(H)
3 侧颈点高(HSNP) 正面颈与肩交接点至地面的垂直距离 10 肩高比(RhSP) 肩点高(HSP)/身高(H)
4 肩点高(HSP) 肩端点至地面的垂直距离 11 腋高比(RhAP) 腋点高(HAP)/身高(H)
5 前腋点高(HAP) 手臂与胸部交接点至地面的垂直距离 12 颈矢额径比(RNP) 颈厚(DNP)/颈宽(WNP)
6 肩斜角(AST) 侧颈点和肩端点连线与水平面形成的夹角 13 侧颈矢额径比(RSNP) 侧颈厚(DSNP)/侧颈宽(WSNP)
7 背入角(ADE) 侧面背部最突出的点和颈点的连线
与垂直面形成的夹角		 14 肩矢额径比(RSP) 肩厚(DSP)/肩宽(WSP)
15 腋下矢额径比(RAP) 腋下厚(DAP)/腋下宽(WAP)

Fig.1

Measurement method of neck-shoulder shape parameters"

Fig.2

Normal P-P diagram. (a)Back angle; (b)Shoulder depth/width ratio"

Tab.2

Descriptive statistics analysis of related variables"

变量描述 身高/
cm		 颈点
高/cm		 侧颈点
高/cm		 肩点高/
cm		 前腋点
高/cm		 肩斜角/
(°)		 背入角/
(°)		 颈矢额
径比		 侧颈
矢额径比		 肩矢额
径比		 腋下
矢额径比
最小值 151.1 128.5 125.6 122.9 110.0 15.9 11.1 0.80 0.61 0.31 0.51
最大值 175.0 150.5 148.5 149.2 136.3 32.0 28.9 1.20 0.99 0.51 0.82
平均值 161.5 137.8 135.4 132.7 122.2 25.1 17.7 0.99 0.78 0.41 0.66
标准差 4.6 4.3 4.1 5.0 4.5 3.1 3.5 0.08 0.06 0.04 0.07
变异系数/% 2.848 3.120 3.028 3.768 3.682 12.351 20.468 8.081 7.692 9.756 10.606

Tab.3

Final clustering center"

体型
分类		 肩斜角/
(°)		 背入角/
(°)		 肩矢额
径比		 腋下矢
额径比		 人数
a 24.0 16.5 0.42 0.72 48
b 23.2 15.7 0.37 0.61 45
c 28.7 16.6 0.45 0.68 50
d 24.4 21.9 0.41 0.62 45

Fig.3

Four types of cross-section. (a)Type-a (round wide shoulder); (b)Type-b (flat narrow shoulder); (c)Type-c (round drop shoulder); (d)Type-d (hunchback flat shoulder)"

Tab.4

Classification rules of four types of neck-shoulder shape"

体型
分类		 肩斜
角/(°)		 背入
角/(°)		 肩矢额
径比		 腋下矢
额径比
a 19~27
(不含27)		 13~19
(不含19)		 0.38~0.46 0.66~0.82
b 16~26 11~18 0.31~0.41 0.51~0.66
(不含0.66)
c 27~32 13~21 0.40~0.51 0.58~0.77
d 19~27
(不含27)		 19~29 0.36~0.46 0.54~0.69

Tab.5

Analysis of discriminant accuracy"

体型
分类		 预测的群成员分类 总计 判别准
确率/%
a b c d
a 46 0 2 0 48 95.8
b 1 44 0 0 45 97.8
c 3 0 46 1 50 92.0
d 0 1 0 44 45 97.8

Fig.4

Calibration diagram"

Fig.5

Schematic diagram of photo pose.(a)Front; (b)Side"

Fig.6

Front and side image processing. (a)Segmentation;(b)Filling holes and opening; (c) Extracted silhouette"

Fig.7

Schematic diagram of neck point determination"

Fig.8

Schematic diagram of shoulder angle calculation"

Fig.9

Neck-shoulder recognition results by classitication rules of this method"

Tab.6

Error analysis of feature shape parameter extraction"

变量 类型 均值 标准差 相关系数 平均绝对误差 误差范围
肩斜角 提取值 25.296° 2.405 0° 0.955 0.63° -1.3°~1.4°
测量值 25.336° 2.451 6°
背入角 提取值 18.201° 2.167 2° 0.891 0.96° -1.5°~1.7°
测量值 18.130° 2.422 6°
肩矢额径比 提取值 0.406 2 0.455 3 0.927 0.01 -0.02~0.02
测量值 0.410 9 0.255 5
腋下矢额径比 提取值 0.642 0 0.563 3 0.776 0.03 -0.07~0.06
测量值 0.630 0 0.555 6

Tab.7

T test results of paired samples of characteristic shape parameters"

变量 均值 标准差 标准误差 T值 显著性
肩斜角 -0.040 0.727 0.115 -0.352 0.727
背入角 0.705 1.102 0.174 0.405 0.688
肩矢额径比 -0.005 0.024 0.004 -1.249 0.219
腋下矢额径比 0.011 0.047 0.008 1.438 0.158
[1] YAN J, KUZMICHEV V E. A virtual e-bespoke men's shirt based on new body measurements and method of pattern drafting[J]. Textile Research Journal, 2020,90(19/20):2223-2244.
doi: 10.1177/0040517520913347
[2] 龚昱. 基于形状上下文的体型分类和自动识别研究[D]. 上海: 东华大学, 2017: 1-5.
GONG Yu. Research of body shape classification and recognition based on shape context[D]. Shanghai: Donghua University, 2017: 1-5.
[3] 李小辉, 张思严, 赵楚瞳, 等. 衣身肩斜角与人体体型特征的关系[J]. 纺织学报, 2018,39(11):111-115.
LI Xiaohui, ZHANG Siyan, ZHAO Chutong, et al. Relationship between garment shoulder angle and characteristics of human body[J]. Journal of Textile Research, 2018,39(11):111-115.
[4] 蒋黎. 基于人体肩部形态的女衬衣肩线结构设计研究[D]. 武汉: 武汉纺织大学, 2018: 29-44.
JIANG Li. Research on pattern design of blouse shoulder line based on female body shape[D]. Wuhan: Wuhan Textile University, 2018: 29-44.
[5] 贺莉文. 基于肩截面曲线的弓背型青年男体细分与识别模型[D]. 苏州: 苏州大学, 2018: 27-32.
HE Liwen. Classification and recognition model of the hunchback young male body based on shoulder section curve[D]. Suzhou: Soochow University, 2018: 27-32.
[6] LEONG I F, FANG J J, TSAI M J. A feature-based anthropometry for garment industry[J]. International Journal of Clothing Science & Technology, 2013,25(1):6-23.
[7] UHM T, PARK H, PARK J I. Fully vision-based automatic human body measurement system for apparel application[J]. Measurement, 2015,61:169-179.
doi: 10.1016/j.measurement.2014.10.044
[8] WANG L, WAN T R, TANG W, et al. An efficient human body contour extraction method for mobile apps[C]// International Conference on Technologies for E-Learning and Digital Entertainment. Cham: Springer, 2017: 173-181.
[9] PRADHAN L, SONG G, ZHANG C, et al. Feature extraction from 2D images for body composition ana-lysis[C]// IEEE International Symposium on Multimedia. Miami: IEEE, 2015: 45-52.
[10] JIANG L, YAO J, LI B, et al. Automatic body feature extraction from front and side images[J]. Journal of Software Engineering & Applications, 2013,5(12):94-100.
[11] 马黎. 基于图像的非接触式人体测量算法的研究与实现[D]. 成都: 电子科技大学, 2017: 36-47.
MA Li. Research and implementation of non-contacting anthropometric algorithm based on image[D]. Chengdu: University of Electronic Science and Technology, 2017: 36-47.
[12] 胡新荣, 刘嘉文, 刘军平, 等. 基于ABSS的着装人体多特征点提取与尺寸测量[J/OL]. 计算机工程: 1-10 [2020-01-10]. http://kns.cnki.net/kcms/detail/31.1289.tp.20200110.1329.004.html.
HU Xinrong, LIU Jiawen, LIU Junping, et al. Multi-features extraction and dimension measurement for dressed human bodies via adaptive body structure segmentation[J/OL]. Computer Engineering: 1-10 [2020-01-10]. http://kns.cnki.net/kcms/detail/31.1289.tp.20200110.1329.004.html.
[13] SENANAYAKE M, RAHEJA A, ZHANG Y H. Automated human body measurement extraction[J]. International Journal of Clothing Science & Technology, 2018,30(2):175-194.
[14] 许家岩, 匡才远. 体表特征参数对远程人体测量精度的影响[J]. 丝绸, 2019,56(8):36-45.
XU Jiayan, KUANG Caiyuan. Influence of characteristic parameters of body surface on long-range body measurement precision[J]. Journal of Silk, 2019,56(8):36-45.
[15] 雷启然, 刘琴, 尚笑梅. 人体肩斜度不同测量方法的优化分析[J]. 北京服装学院学报(自然科学版), 2019,39(3):16-23.
LEI Qiran, LIU Qin, SHANG Xiaomei. Optimization analysis of different measurement methods of shoulder inclination[J]. Journal of Beijing Institute of Fashion Technology(Natural Science Edition), 2019,39(3):16-23.
[16] 侯梦婕. 基于三维人体扫描枕型的结构研究[D]. 西安: 西安工程大学, 2019: 15-31.
HOU Mengjie. Research on pillow structure based on three-dimensional scanning of human body[D]. Xi'an: Xi'an Polytechnic University, 2019: 15-31.
[17] 金娟凤, 庞程方, 陈伟杰, 等. 青年男性肩点横截面曲线及其体型细分[J]. 纺织学报, 2016,37(8):100-106.
JIN Juanfeng, PANG Chengfang, CHEN Weijie, et al. Study on subdivision of young male's shoulder shapes and cross-section curve[J]. Journal of Textile Research, 2016,37(8):100-106.
[18] GU B, LIU G, XU B. Girth prediction of young female body using orthogonal silhouettes[J]. The Journal of The Textile Institute, 2017,108(1):140-146.
doi: 10.1080/00405000.2016.1160756
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