  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.
