中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
An Overview of Contour Detection Approaches

文献类型:期刊论文

作者Xin-Yi Gong2,3; Hu Su2,3; De Xu1,2,3; Zheng-Tao Zhang2,3; Fei Shen2,3; Hua-Bin Yang2,3
刊名International Journal of Automation and Computing
出版日期2018
卷号15期号:6页码:656-672
关键词Contour detection contour salience gestalt principle contour grouping active contour.
ISSN号1476-8186
DOI10.1007/s11633-018-1117-z
英文摘要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.
源URL[http://ir.ia.ac.cn/handle/173211/42442]  
专题自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位1.Tianjin Intelligent Technology Institute of Institute of Automation, Chinese Academy of Science Co., Ltd, Tianjin 300300, China
2.University of Chinese Academy of Science, Beijing 100049, China
3.Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Science, Beijing 100190, China
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Xin-Yi Gong,Hu Su,De Xu,et al. An Overview of Contour Detection Approaches[J]. International Journal of Automation and Computing,2018,15(6):656-672.
APA Xin-Yi Gong,Hu Su,De Xu,Zheng-Tao Zhang,Fei Shen,&Hua-Bin Yang.(2018).An Overview of Contour Detection Approaches.International Journal of Automation and Computing,15(6),656-672.
MLA Xin-Yi Gong,et al."An Overview of Contour Detection Approaches".International Journal of Automation and Computing 15.6(2018):656-672.

入库方式: OAI收割

来源:自动化研究所

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