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An overview of contour detection approaches
Gong XY(宫新一)1,2; Hu Su1; De Xu1; Zhengtao Zhang1; Fei Shen1; Huabin Yang1
Source PublicationInternational Journal of Automation and Computing
2018-12
Volume15Issue:6Pages:656-672
Abstract

Object contour plays an important role in fields such as semantic segmentation and image classification. However, the extraction of the contour is a difficult task, especially when the contour is incomplete or unclosed. In this paper, existing contour detection approaches are  reviewed and roughly divided into three categories: pixel-based, edge-based, and region-based. In addition, while the traditional contour detection approaches have achieved a high degree of sophistication, deep convolutional neural networks (DCNNs) have demonstrated a good performance in image recognition, and therefore, the DCNNs based contour detection approaches are also covered in this paper. Furthermore, the future development of contour detection is analyzed and predicted.

KeywordContour Detection, Contour Salience, Gestalt Principle, Contour Grouping, Active Contour
Indexed ByEI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23674
Collection精密感知与控制研究中心_精密感知与控制
Corresponding AuthorHuabin Yang
Affiliation1.中国科学院自动化研究所
2.中国科学院大学人工智能学院
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Gong XY,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 Gong XY,Hu Su,De Xu,Zhengtao Zhang,Fei Shen,&Huabin Yang.(2018).An overview of contour detection approaches.International Journal of Automation and Computing,15(6),656-672.
MLA Gong XY,et al."An overview of contour detection approaches".International Journal of Automation and Computing 15.6(2018):656-672.
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