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
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