An Overview of Contour Detection Approaches  

作　　者：Xin-Yi Gong Hu Su De Xu Zheng-Tao Zhang Fei Shen Hua-Bin Yang 

机构地区：[1]Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Science, Beijing 100190, China [2]University of Chinese Academy of Science, Beijing 100049, China [3]Tianjin Intelligent Technology Institute of Institute of Automation, Chinese Academy of Science Co.,Ltd, Tianjin 300300, China

出　　处：《International Journal of Automation and computing》2018年第6期656-672,共17页国际自动化与计算杂志（英文版）

基　　金：supported by National Natural Science Foundation of China (Nos. 61503378, 61473293, 51405485 and 61403378);the Project of Development in Tianjin for Scientific Research Institutes, and Tianjin Government (No. 16PTYJGX00050)

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.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.

关 键 词：Contour detection contour salience gestalt principle contour grouping active contour. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP751[自动化与计算机技术—计算机科学与技术]