A fast and accurate circle detection algorithm based on random sampling

Circle detection in digital images is an important problem in computer vision, pattern recognition, and artificial intelligence. However, the common circle detection strategies, including random sample consensus, randomized Hough transform, and randomized circle detection, have a very low sampling e...

Full description

Saved in:
Bibliographic Details
Published in:Future generation computer systems Vol. 123; pp. 245 - 256
Main Author: Jiang, Lianyuan
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2021
Subjects:
Candidate circle
Circle detection
Detection accuracy
Evidence collecting
Random sampling
Randomized Hough transform
Circle detection
Detection accuracy
Candidate circle
Randomized Hough transform
Random sampling
Evidence collecting
ISSN:0167-739X, 1872-7115
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Circle detection in digital images is an important problem in computer vision, pattern recognition, and artificial intelligence. However, the common circle detection strategies, including random sample consensus, randomized Hough transform, and randomized circle detection, have a very low sampling efficiency and thus a slow detection speed, owing to aimless random sampling. This paper proposes a fast and accurate randomized circle detection algorithm, with the aim to improve the speed and accuracy of circle detection based on random sampling. The proposed algorithm mainly focuses on four aspects: calculating circle parameters, determining candidate circles, searching for true circle, and improving detection accuracy. To verify the effectiveness of our algorithm, contrastive experiments were conducted on lots of synthetic and real images. The results show that our algorithm achieved much higher detection speed and accuracy than random sample consensus, randomized Hough transform, and randomized circle detection, and realized similar robustness as the three contrastive strategies. The research ideas can also be applied to ellipse detection. •An effective method for determining candidate circles is proposed.•A fast method for searching for true circle is proposed.•A strategy for improving detection accuracy is suggested.•Our algorithm was compared through experiments against other classical algorithms.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2021.05.010