Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (03): 190-196.doi: 10.13475/j.fzxb.20200307007

• Comprehensive Review • Previous Articles    

Research progress in machine vision algorithm for human contour detection

FENG Wenqian1,2, LI Xinrong1,2(), YANG Shuai1,2   

  1. 1. School of Mechanical Engineering, Tiangong University, Tianjin 300387, China
    2. Key Laboratory of Modern Mechanical and Electrical Equipment Technology, Tianjin 300387, China
  • Received:2020-03-26 Revised:2020-12-03 Online:2021-03-15 Published:2021-03-17
  • Contact: LI Xinrong E-mail:lixinrong7507@hotmail.com

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Abstract:

In order to better apply the contour vision detection method to non-contact two-dimensional body circumference measurement, the contour vision detection methods in recent years are reviewed, including edge operators based detection, mathematical morphology based detection, and level set detection algorithm of active contour model. Through the comparison of experimental renderings, the edge definition, anti-noise ability and target edge positioning ability of the contour visual detection method are re-evaluated. In particular, the research progress of Canny edge operator in optimization and improvement is discussed. The relevant researches provide a theoretical reference for carrying out the visual detection of human contours under complicated background and wearing conditions. Finally, the challenges and opportunities of human body contour machine vision detection are described, and it is pointed out that human contour vision detection has a good development prospect in non-contact two-dimensional body circumference measurement.

Key words: machine vision, contour detection, human contour, body circumference, edge operator, active contour model

CLC Number: 

  • TP391.8

Fig.1

Detection effect of different edge operators. (a) Laplacian operator; (b) Roberts operator;(c) Sobel operator; (d) Canny operator"

Fig.2

Comprehensive structural element diagram"

Fig.3

Experimental comparison of different methods in different backgrounds. (a) Traditional active contour model effect; (b) Automatically set initial contour effect;(c) Non-closed active contour model effect"

[1] 邹柏贤, 林京壤. 图像轮廓提取方法研究[J]. 计算机工程与应用, 2008(25):161-165.
doi: 10.3778/j.issn.1002-8331.2008.25.049
ZOU Baixian, LIN Jingrang. Research on image contour extraction[J]. Computer Engineering and Applications, 2008(25):161-165.
doi: 10.3778/j.issn.1002-8331.2008.25.049
[2] DUAN Hongyan, SHAO Hao, ZHANG Shuzhen, et al. An improved algorithm for image edge detection based on canny operator[J]. Journal of Shanghai Jiaotong University, 2016,50(12):1861-1865.
[3] 覃禹舜. 基于深度神经网络的图像边缘检测算法研究[D]. 成都: 西南交通大学, 2019: 7-16.
QIN Yushun. Research on image edge detection algorithm based on deep neural network[D]. Chengdu: Southwest Jiaotong University, 2019: 7-16.
[4] 胡志斌, 邓彩霞, 邵云虹, 等. 二进小波与改进的形态学融合的边缘检测算法[J]. 计算机工程与设计, 2020,41(1):190-196.
HU Zhibin, DENG Caixia, SHAO Yunhong, et al. Edge detection algorithm fusion of binary wavelet and improved morphology[J]. Computer Engineering and Design, 2020,41(1):190-196.
[5] 邹昆, 马黎, 李蓉, 等. 基于图像的非接触式人体参数测量方法[J]. 计算机工程与设计, 2017,38(2):511-516.
ZOU Kun, MA Li, LI Rong, et al. Image-based non-contacting anthropometric method[J]. Computer Engineering and Design, 2017,38(2):511-516
[6] NAVDEEP, GOYAL SONAL, RANI Asha, et al. An improved local binary pattern based edge detection algorithm for noisy images[J]. Journal of Intelligent and Fuzzy Systems, 2019,36(3):2043-2054.
doi: 10.3233/JIFS-169916
[7] 刘晓刚, 闫红方, 张荣. 基于形态学多尺度多结构的熔池图像边缘检测[J]. 热加工工艺, 2019,48(5):216-219.
LIU Xiaogang, YAN Hongfang, ZHANG Rong. Edge detection of molten pool image based on morphology multi-scale and multi-structure[J]. Thermal Processing Technology, 2019,48(5):216-219.
[8] YU Haiping, HE Fazhi, PAN Yiteng. A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation[J]. Multimedia Tools and Applications, 2020,79(6):5743-5765.
doi: 10.1007/s11042-019-08493-1
[9] 刘其思, 徐平华, 周佳, 等. 基于变分水平集的服饰图案轮廓提取[J]. 服装学报, 2016,1(5):482-486.
LIU Qisi, XU Pinghua, ZHOU Jia, et al. Contour extraction of clothing patterns based on variational level set[J]. Journal of Clothing Research, 2016,1(5):482-486.
[10] BIAN Guiping, QIN Yilin. An adaptive edge-detection method based on Canny algorithm[J]. Electronic Design Engineering, 2017,25(10):53-56,60.
[11] 于晓海, 张阳, 须颖. 一种改进自适应阈值的Canny算法[J]. 机械与电子, 2020,38(1):6-9.
YU Xiaohai, ZHANG Yang, XU Ying. A Canny algorithm with improved adaptive threshold[J]. Mechanical and Electronic, 2020,38(1):6-9.
[12] ZHANG Weichuan, ZHAO Yali, BRECKON TP, et al. Noise robust imageedge detection based upon the automatic anisotropic Gaussian kernels[J]. Pattern Recognition, 2016,63(8):193-205.
doi: 10.1016/j.patcog.2016.10.008
[13] QIN Wei, LI Juan. The application study on the improved Canny algorithm for edge detection in strain gauge image[J]. MATEC Web of Conferences, 2017,128(2):152-156.
[14] XU Dongqing, WANG Xiuyou, SUN Gang, et al. Towards a novel image denoising method with edge-preserving sparse representation based on laplacian of B-spline edge-detection[J]. Multimedia Tools and Applications, 2017,76(17):17839-17854.
doi: 10.1007/s11042-015-3097-0
[15] XU Q, VARADARAJAN S, CHAKRABARTI C, et al. A distributed canny edge detector: algorithm and FPGA implementation[J]. IEEE Transactions on Image Processing, 2014,23(7):2944-2960.
doi: 10.1109/tip.2014.2311656 pmid: 24983098
[16] 杨静娴, 任小洪. 基于图像处理的白酒酒花轮廓检测[J]. 食品与机械, 2019,35(12):52-55,145.
YANG Jingxian, REN Xiaohong. Contour detection of white wine hops based on image processing[J]. Food and Machinery, 2019,35(12):52-55,145.
[17] 齐英兰. 应用自适应滤波与阈值迭代的原棉杂质视觉检测方法[J]. 毛纺科技, 2020,48(2):73-77.
QI Yinglan. Visual detection method of raw cotton impurities using adaptive filtering and threshold iteration[J]. Wool Textile Journal, 2020,48(2):73-77.
[18] 李东兴, 高倩倩, 张起, 等. 融合数学形态学滤波技术的边缘检测算法[J]. 山东理工大学学报(自然科学版), 2018,32(6):1-5.
LI Dongxing, GAO Qianqian, ZHANG Qi, et al. Edge detection algorithm incorporating mathematical morphology filtering technology[J]. Journal of Shandong University of Technology (Natural Science Edition), 2018,32(6):1-5.
[19] 庞明明, 安建成. 融合模糊LBP和Canny边缘的图像分割[J]. 计算机工程与设计, 2019,40(12):3533-3537.
PANG Mingming, AN Jiancheng. Image segmentation based on fuzzy LBP and Canny edges[J]. Computer Engineering and Design, 2019,40(12):3533-3537.
[20] KOTHAPALLI Vignesh, ARORA Shaveta, HANMANDLU Madasu. Edge detection using fractional derivatives and information sets[J]. Journal of Electronic Imaging, 2018,27(5):51-79.
[21] MAKSIMOVIC Vladimir, JAKSIC Branimir. Analysis of edge detection on compressed images with different complexities[J]. Journal of Acta Polytechnica Hungarica, 2020,17(4):123-143.
[22] THIRUMAVALAVAN S, JAYARAMAN S. An improved teaching learning based robust edge detection algorithun for noisy images[J]. Journal of Advanced Research, 2016,7(6):979-989.
doi: 10.1016/j.jare.2016.04.002 pmid: 27857845
[23] 李怡燃, 庞春颖, 常知强. Sobel算子和形态学相结合的尿液试纸条边缘检测算法研究[J]. 生物医学工程研究, 2019,38(1):43-47.
LI Yiran, PANG Chunying, CHANG Zhiqiang. Study on edge detection algorithm of urine test strip based on Sobel operator and morphology[J]. Journal of Biomedical Engineering Research, 2019,38(1):43-47.
[24] 王蔚, 王晓凯, 龚真, 等. 基于形态学的机器视觉玻璃切割边缘提取[J]. 测试技术学报, 2020,34(1):22-27.
WANG Wei, WANG Xiaokai, GONG Zhen, et al. Morphology-based extraction of cutting edges in glass for machine vision[J]. Journal of Test and Measurement Technology, 2020,34(1):22-27.
[25] 鄂那林, 王芬芬. 基于多结构元素的形态学边缘检测算法[J]. 科技信息, 2013(11): 73, 111.
E Nalin, WANG Fenfen. Morphological edge detection algorithm based on multiple structural elements [J]. Science and Technology Information, 2013(11): 73, 111.
[26] 吴朔媚, 韩明, 王敬涛. 基于多尺度多方向结构元素的形态学图像边缘检测算法[J]. 量子电子学报, 2017,34(3):278-285.
WU Shuomei, HAN Ming, WANG Jingtao. Morphological image edge detection algorithm based on multi-scale and multi-directional structural elements[J]. Chinese Journal of Quantum Electronics, 2017,34(3):278-285.
[27] 秦玮, 陈希, 马原原, 等. 基于数学形态学的边缘检测算法分析[J]. 信息技术, 2019,43(11):33-36.
QIN Wei, CHEN Xi, MA Yuanyuan, et al. Analysis of edge detection algorithm based on mathematical morphology[J]. Information Technology, 2019,43(11):33-36.
[28] 吴一全, 宋昱, 周怀春. 基于各向异性数学形态学的火焰图像边缘检测[J]. 仪器仪表学报, 2013,34(8):1818-1825.
WU Yiquan, SONG Yu, ZHOU Huaichun. Edge detection of flame image based on anisotropic mathematical morphology[J]. Chinese Journal of Scientific Instrument, 2013,34(8):1818-1825.
[29] 罗进华, 蒋锦朋, 朱培民. 基于数学形态学的侧扫声呐图像轮廓自动提取[J]. 海洋学报, 2016,38(5):150-157.
LUO Jinhua, JIANG Jinpeng, ZHU Peimin. Automatic extraction of contours of sidescan sonar images based on mathematical morphology[J]. Chinese Journal of Oceanology, 2016,38(5):150-157.
[30] 安立新, 李炜. 一种带有印花图案服装图像的轮廓提取[J]. 纺织学报, 2013,34(3):132-136.
AN Lixin, LI Wei. Contour extraction of a garment image with printed patterns[J]. Journal of Textile Research, 2013,34(3):132-136.
[31] 王文豪, 严云洋, 姜明新, 等. 一种去噪声的轮廓提取算法[J]. 江苏科技大学学报(自然科学版), 2017,31(4):519-524.
WANG Wenhao, YAN Yunyang, JIANG Mingxin, et al. A denoising contour extraction algorithm[J]. Journal of Jiangsu University of Science and Technology (Natural Science Edition), 2017,31(4):519-524.
[32] 王涛, 潘国富, 张济博. 基于K-means聚类与数学形态学的侧扫声呐图像目标轮廓自动提取方法[J]. 海洋科学, 2019,43(8):80-85.
WANG Tao, PAN Guofu, ZHANG Jibo. Automatically extracting target contours of side-scan sonar images based on K-means clustering and mathematical morphology[J]. Marine Science, 2019,43(8):80-85.
[33] ENDO Hiromu, TAGUCHI Akira. Color image enhancement method with variable emphasis degree[J]. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2018,E101A(4):713-722.
[34] 刘千, 葛阿雷, 史伟. 形态学与RCF相结合的唐卡图像边缘检测算法[J]. 计算机应用与软件, 2019,36(6):196-201,242.
LIU Qian, GE Alei, SHI Wei. Tangka image edge detection algorithm combining morphology and RCF[J]. Journal of Computer Applications and Software, 2019,36(6):196-201,242.
[35] KASS M, WITKIN A, TERZOPOULOS D. Snakes: active contour models[J]. International Journal of Computer Vision, 1988,1(4):321-331.
[36] 翁桂荣, 何志勇. 基于自适应符号函数的主动轮廓模型[J]. 软件学报, 2019,30(12):3892-3906.
WENG Guirong, HE Zhiyong. Active contour model based on adaptive symbol function[J]. Journal of Software, 2019,30(12):3892-3906.
[37] WANG Xianghai, LI Wei, ZHANG Chong, et al. An adaptable active contour model for medical image segmentation based on region and edge information[J]. Multimedia Tools and Applications, 2019,78(23):33921-33937.
doi: 10.1007/s11042-019-08073-3
[38] 赵方珍, 罗兰花, 梁海英, 等. 改进的水平集方法及其在图像分割中的应用[J]. 数学的实践与认识, 2019,49(22):154-162.
ZHAO Fangzhen, LUO Lanhua, LIANG Haiying, et al. Improved level set method and its application in image segmentation[J]. Mathematics in Practice and Theory, 2019,49(22):154-162.
[39] 韩红伟, 唐静, 洪姗. 基于局部统计信息的主动轮廓模型[J]. 云南民族大学学报(自然科学版), 2017,26(3):241-246,257.
HAN Hongwei, TANG Jing, HONG Shan. Active contour model based on local statistical information[J]. Journal of Yunnan University Nationalities (Natural Science Edition), 2017,26(3):241-246,257.
[40] ZOU Kun, MA Li, FU Yu, et al. Anthropometry oriented local contour extraction based on unclosed snake model[J]. Journal of Computer-Aided Design and Computer Graphics, 2018,30(1):147-154.
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