Xin-Yi Gong, Hu Su, De Xu, Zheng-Tao Zhang, Fei Shen and Hua-Bin Yang. An Overview of Contour Detection Approaches. International Journal of Automation and Computing, vol. 15, no. 6, pp. 656-672, 2018. https://doi.org/10.1007/s11633-018-1117-z
Citation: Xin-Yi Gong, Hu Su, De Xu, Zheng-Tao Zhang, Fei Shen and Hua-Bin Yang. An Overview of Contour Detection Approaches. International Journal of Automation and Computing, vol. 15, no. 6, pp. 656-672, 2018. https://doi.org/10.1007/s11633-018-1117-z

An Overview of Contour Detection Approaches

doi: 10.1007/s11633-018-1117-z
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  • Author Bio:

    Xin-Yi Gong received the B. Sc. degree in automation from Tsinghua University, China in 2014. He is currently a Ph. D. degree candidate in control science and engineering at Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, China. His research interests include computer vision, image processing, pattern recognition and machine learning. E-mail: gongxinyi2014@ia.ac.cn ORCID iD: 0000-0002-6515-2836

    Hu Su received the B. Sc. degree in information and computing science and the M. Sc. degree in operational research and cybernetics from Shandong University, China in 2007 and 2010, respectively, and the Ph. D. degree in control science and engineering from the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences (IACAS), China in 2013. Since 2013, he has been with the IACAS, China, where he is currently an associate researcher in the Research Center of Precision Sensing and Control, China. His research interests include intelligent control and optimization. E-mail: hu.su@ia.ac.cn

    De Xu received the B. Sc. and M. Sc. degrees in control science and engineering from Shandong University of Technology, China in 1985 and 1990, respectively, and the Ph. D. degree in control science and engineering from Zhejiang University, China in 2001. Since 2001, he has been with IACAS, China. He is currently a professor in the Research Center of Precision Sensing and Control, IACAS, China. His research interests include robotics and automation, especially the control of robots such as visual control and intelligent control. E-mail: de.xu@ia.ac.cn ORCID iD: 0000-0002-7221-1654

    Zheng-Tao Zhang received the B. Sc. degree in control science and engineering from China University of Petroleum, China in 2004, the M. Sc. degree in control science and engineering from Beijing Institute of Technology, China in 2007, and the Ph. D. degree in control science and engineering from IACAS, China in 2010. He is a professor in the Research Center of Precision Sensing and Control, IACAS, China. His research interests include visual measurement, micro-assembly and automation. E-mail: zhengtao.zhang@ia.ac.cn

    Fei Shen received the B. Sc. degree in measurement and control technology and instrument from Xidian University, China in 2007, and the M. Sc. degree in guidance navigation and control from Beijing Institute of Technology, China in 2009, and the Ph. D. degree in control science and engineering from IACAS, China in 2012. He is currently an associate professor in the Research Center of Precision Sensing and Control, IACAS, China. His research interests include robot control, robot vision control and micro-assembly. E-mail: fei.shen@ia.ac.cn

    Hua-Bin Yang received the B. Sc. degree in mechanical manufacture and automation from Shandong University of Technology, China in 2010, and the Ph. D. degree in mechanical manufacture and automation from Changchun Institute of Optics, Mechanics and Physics, Chinese Academy of Sciences (CIOMPCAS), China in 2015. He is a research assistant professor in the Research Center of Precision Sensing and Control, IACAS, China. His research interests include optics and precision mechanics, visual measurement and automation. E-mail: huabin.yang@ia.ac.cn (Corresponding author) ORCID iD: 0000-0001-9955-0560

  • Received Date: 2017-10-24
  • Accepted Date: 2018-01-25
  • Publish Online: 2018-06-29
  • Publish Date: 2018-12-01
  • Object contour plays an important role in fields such as semantic segmentation and image classification. However, the extraction of contour is a difficult task, especially when the contour is incomplete or unclosed. In this paper, the existing contour detection approaches are reviewed and roughly divided into three categories: pixel-based, edge-based, and region-based. In addition, since the traditional contour detection approaches have achieved a high degree of sophistication, the deep convolutional neural networks (DCNNs) have good performance in image recognition, therefore, the DCNNs based contour detection approaches are also covered in this paper. Moreover, the future development of contour detection is analyzed and predicted.

     

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