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Circle Detection Using Its Maximal Symmetry Property
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 Title & Authors
Circle Detection Using Its Maximal Symmetry Property
Koo, Ja Young;
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Circle detection has long been studied as one of fundamental image processing applications. It is used in divers areas including industrial inspection, medial image analysis, radio astronomy data analysis, and other object recognition applications. The most widely used class of circle detection techniques is the circle Hough transform and its variants. Management of 3 dimensional parameter histogram used in these methods brings about spatial and temporal overheads, and a lot of studies have dealt the problem. This paper proposes a robust circle detection method using maximal symmetry property of circle. The basic idea is that if perpendicular bisectors of pairs of edges are accumulated in image space, center of circle is determined to be the location of highest accumulation. However, directly implementing the idea in image space requires a lot of calculations. The method of this paper reduces the number of calculations by mapping the perpendicular bisectors into parameter space, selecting small number of parameters, and mapping them inversely into image space. Test on 22 images shows the calculations of the proposed method is 0.056% calculations of the basic idea. The test images include simple circles, multiple circles with various sizes, concentric circles, and partially occluded circles. The proposed method detected circles in various situations successfully.
circle detection;symmetry detection;parameter space histogram;ridge detection;
 Cited by
E. N. Malamas, et al., "A survey on industrial vision systems, applications and tools." Image and vision computing 21.2, pp. 171-188, 2003. crossref(new window)

J. Mitra, A. Chandra, and T. Halder, "Peak trekking of hierarchy mountain for the detection of cerebral aneurysm using modified Hough circle transform." ELCVIA Electronic Letters on Computer Vision and Image Analysis12.1, pp. 57-84, 2013.

R. Smith, K. Najarian, and K. Ward, "A hierarchical method based on active shape models and directed Hough transform for segmentation of noisy biomedical images; application in segmentation of pelvic X-ray images", BMC Med. Informat. Decision Making, vol. 9, pp. S2-S12, 2009. crossref(new window)

E. Cuevas, et al., "White blood cell segmentation by circle detection using electromagnetism-like optimization." Computational and mathematical methods in medicine, 2013.

C. Hollitt and M. Johnston-Hollitt, "Feature detection in radio astronomy using the circle Hough transform." Publications of the Astronomical Society of Australia 29.3, pp. 309-317 2012. crossref(new window)

H. Nguyen Van and H. Kim, "A novel circle detection method for iris segmentation." Image and Signal Processing, CISP'08. Congress on. Vol. 3. IEEE, 2008.

Duda, R. O. and P. E. Hart, "Use of the Hough Transformation to Detect Lines and Curves in Pictures," Comm. ACM, Vol. 15, pp. 11-15 , 1972. crossref(new window)

T. De Marco, et al., "Randomized circle detection with isophotes curvature analysis." Pattern Recognition 48.2, pp. 411-421, 2015. crossref(new window)

W. Lu and J. Tan, "Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT)," Pattern Recognition 41 (4) pp. 1268-1279. 2008. crossref(new window)

L. Q. Jia, et al., "A fast randomized circle detection algorithm," In: Image and Signal Processing (CISP), 2011 4th International Congress on. IEEE, pp. 820-823, 2011.

B.H. Won, J.Y. Koo, "An Acceleration Method for Symmetry Detection using Edge Segmentation," Journal of The Korea Society of Computer and Information, Vol 20, No 9, pp. 31-37, 2015.

S. Koka, et al., "Ridge detection with the steepest ascent method." Procedia Computer Science 4 pp. 216-221, 2011. crossref(new window)