Efficient sampling strategy and refinement strategy for randomized circle detection |
| |
| Authors: | Kuo-Liang Chung Yong-Huai Huang Shi-Ming Shen Andrey S. Krylov Dmitry V. Yurin Ekaterina V. Semeikina |
| |
| Affiliation: | 1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei 10672, Taiwan, ROC;2. Institute of Computer and Communication Engineering and Department of Electronic Engineering, Jinwen University of Science and Technology, No. 99, An-Chung Road, Hsin-Tien, Taipei 23154, Taiwan, ROC;3. Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Moscow Lomonosov State University, Leninskie Gory, 2nd Educational Building, Office 638, Moscow 119991, Russia |
| |
| Abstract: | Circle detection is fundamental in pattern recognition and computer vision. The randomized approach has received much attention for its computational benefit when compared with the Hough transform. In this paper, a multiple-evidence-based sampling strategy is proposed to speed up the randomized approach. Next, an efficient refinement strategy is proposed to improve the accuracy. Based on different kinds of ten test images, experimental results demonstrate the computation-saving and accuracy effects when plugging the proposed strategies into three existing circle detection methods. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
