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
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei 10672, Taiwan, ROC;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, R ...;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei 10672, Taiwan, ROC;Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Moscow Lomonosov State University, Leninskie Gory, 2nd Educational Building, Office 63 ...;Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Moscow Lomonosov State University, Leninskie Gory, 2nd Educational Building, Office 63 ...;Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Moscow Lomonosov State University, Leninskie Gory, 2nd Educational Building, Office 63 ...
Venue:
Pattern Recognition
Year:
2012

Citing 16
Cited 1

The Adaptive Hough Transform

IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of the Hough transform

Computer Vision, Graphics, and Image Processing
A new curve detection method: randomized Hough transform (RHT)

Pattern Recognition Letters
A probabilistic Hough transform

Pattern Recognition
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities

CVGIP: Image Understanding
Machine vision

Machine vision
Finding circles by an array of accumulators

Communications of the ACM
Use of the Hough transformation to detect lines and curves in pictures

Communications of the ACM
A two-step circle detection algorithm from the intersecting chords

Pattern Recognition Letters
An efficient randomized algorithm for detecting circles

Computer Vision and Image Understanding
Computer Vision: A Modern Approach

Computer Vision: A Modern Approach
Truncating the Hough transform parameter space can be beneficial

Pattern Recognition Letters
Machine Vision: Theory, Algorithms, Practicalities

Machine Vision: Theory, Algorithms, Practicalities
An effective voting method for circle detection

Pattern Recognition Letters
Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT)

Pattern Recognition
Robust and efficient automated detection of tooling defects in polished stone

Computers in Industry

EDCircles: A real-time circle detector with a false detection control

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

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.