Circle detection using isosceles triangles sampling

TL;DR

This paper introduces a robust circle detection method using isosceles triangles sampling that improves accuracy and noise resistance in digital images, outperforming existing algorithms in efficiency and false-positive rejection.

Contribution

The paper proposes a novel circle detection technique based on isosceles triangles sampling, enhancing robustness and accuracy in noisy conditions compared to prior randomized methods.

Findings

Efficient circle detection with fewer iterations.

High rejection rate of false positives.

Robust performance against noise in real and synthetic images.

Abstract

Detection of circular objects in digital images is an important problem in several vision applications. Circle detection using randomized sampling has been developed in recent years to reduce the computational intensity. Randomized sampling, however, is sensitive to noise that can lead to reduced accuracy and false-positive candidates. This paper presents a new circle detection method based upon randomized isosceles triangles sampling to improve the robustness of randomized circle detection in noisy conditions. It is shown that the geometrical property of isosceles triangles provide a robust criterion to find relevant edge pixels and thereby efficiently provide an estimation of the circle center and radii. The estimated results given by the isosceles triangles sampling from each connected component of edge map were analyzed using a simple clustering approach for efficiency. To further improve on the accuracy we applied a two-step refinement process using chords and linear error compensation with gradient information of the edge pixels. Extensive experiments using both synthetic and real images were presented and results were compared to leading state-of-the-art algorithms and showed that the proposed algorithm: are efficient in finding circles with a low number of iterations; has high rejection rate of false-positive circle candidates; and has high robustness against noise, making it adaptive and useful in many vision applications.