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A fast and robust circle detection method using isosceles triangles sampling
Umeå University, Faculty of Science and Technology, Department of Physics.
Umeå University, Faculty of Science and Technology, Department of Physics.
Umeå University, Faculty of Science and Technology, Department of Physics.
2016 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 54, 218-228 p.Article in journal (Refereed) Published
Abstract [en]

Circle detection using randomized sampling has been developed in recent years to reduce computational intensity. However, randomized sampling is sensitive to noise that can lead to reduced accuracy and false-positive candidates. To improve on the robustness of randomized circle detection under noisy conditions this paper presents a new methodology for circle detection based upon randomized isosceles triangles sampling. It is shown that the geometrical property of isosceles triangles provides a robust criterion to find relevant edge pixels which, in turn, offers an efficient means to estimate the centers and radii of circles. For best efficiency, the estimated results given by the sampling from individual connected components of the edge map were analyzed using a simple clustering approach. 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 have been performed. The results are compared to leading state-of-the-art algorithms and it is shown that the proposed methodology has a number of advantages: it is efficient in finding circles with a low number of iterations, it has high rejection rate of false-positive circle candidates, and it has high robustness against noise. All this makes it adaptive and useful in many vision applications.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 54, 218-228 p.
Keyword [en]
Circle detection, Randomized algorithm, Sampling strategy, Isosceles triangles
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
URN: urn:nbn:se:umu:diva-112312DOI: 10.1016/j.patcog.2015.12.004ISI: 000372380700017OAI: diva2:877153
Swedish Research Council, 2013-5379
Available from: 2015-12-05 Created: 2015-12-05 Last updated: 2016-06-03Bibliographically approved

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Hanqing, ZhangWiklund, KristerAndersson, Magnus
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