Circle detection using isosceles triangles sampling
This work addresses noise sensitivity in circle detection for computer vision applications, representing an incremental improvement over existing randomized sampling methods.
The paper tackled the problem of circle detection in noisy images by introducing a method based on randomized isosceles triangles sampling, which improved robustness and efficiency, achieving high rejection rates of false positives and low iteration counts compared to state-of-the-art algorithms.
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.