@inproceedings{c182197a69b645febbc828a88c39773b,
title = "Gradient center tracking: A novel method for edge detection and contour detection",
abstract = "Detecting complete contours with less clutters is a very challenging task in edge detection. This paper presents a new lightweight edge detection method, Gradient Center Tracking (GCT), to detect the main contours including the boundary and the structural lines of the objects. This method tracks the center curve of contours in the gradient image and detects edges while tracking. It makes full use of the edge correlation and contour continuity to choose edge candidates, then computes the gradient intensities of the candidates to select the real edge. In this method, the intensity of the edge is redefined as the Directional Weighted Intensity (DWI) which helps to present the result with more complete contours and less clutters. The GCT method outperforms Canny detector and shows better results than several learning based methods. The comparison results are shown in our experiments and a typical scheme to apply the GCT method is also provided.",
keywords = "Complete contours, Edge detection, Less clutters",
author = "Yipei Su and Xiaojun Wu and Xiaoyou Zhou",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018 ; Conference date: 23-11-2018 Through 26-11-2018",
year = "2018",
doi = "10.1007/978-3-030-03398-9\_34",
language = "英语",
isbn = "9783030033972",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "397--407",
editor = "Jian-Huang Lai and Hongbin Zha and Jie Zhou and Cheng-Lin Liu and Tieniu Tan and Nanning Zheng and Xilin Chen",
booktitle = "Pattern Recognition and Computer Vision - First Chinese Conference, PRCV 2018, Proceedings",
address = "德国",
}