Fast Circle Detection Using Harmony Search Optimization
Automatic circle detection in digital images has received considerable attention over the last years. Recently, several robust circle detectors, based on evolutionary algorithms (EA), have been proposed. They have demonstrated to provide better results than those based on the Hough Transform. However, since EA-detectors usually need a large number of computationally expensive fitness evaluations before a satisfying result can be obtained; their use for real time has been questioned. In this work, a new algorithm based on the Harmony Search Optimization (HSO) is proposed to reduce the number of function evaluation in the circle detection process. In order to avoid the computation of the fitness value of several circle candidates, the algorithm estimates their values by considering the fitness values from previously calculated neighboring positions. As a result, the approach can substantially reduce the number of function evaluations preserving the good search capabilities of HSO. Experimental results from several tests on synthetic and natural images with a varying complexity range have been included to validate the efficiency of the proposed technique regarding accuracy, speed and robustness.
KeywordsHough Transform Harmony Memory Pitch Adjust Muskingum Model Circle Detection
Unable to display preview. Download preview PDF.
- 2.Muammar, H., Nixon, M.: Approaches to extending the Hough transform. In: Proc. Int. Conf. on Acoustics, Speech and Signal Processing ICASSP, vol. 3, pp. 1556–1559 (1989)Google Scholar
- 3.Atherton, T.J., Kerbyson, D.J.: Using phase to represent radius in the coherent circle Hough transform. In: Proc. IEE Colloquium on the Hough Transform. IEEE, London (1993)Google Scholar
- 6.Han, J.H., Koczy, L.T., Poston, T.: Fuzzy Hough transform. In: Proc. 2nd Int. Conf. on Fuzzy Systems, vol. 2, pp. 803–808 (1993)Google Scholar
- 13.Dasgupta, S., Das, S., Biswas, A., Abraham, A.: Automatic circle detection on digital images whit an adaptive bacterial foraging algorithm. Soft Computing (2009), doi:10.1007/s00500-009-0508-zGoogle Scholar
- 22.Zhou, Z., Ong, Y., Nguyen, M., Lim, D.: A Study on Polynomial Regression and Gaussian Process Global Surrogate Model in Hierarchical Surrogate-Assisted Evolutionary Algorithm. In: IEEE Congress on Evolutionary Computation (ECiDUE 2005), Edinburgh, United Kingdom, September 2-5 (2005)Google Scholar
- 26.Luoa, C., Shao-Liang, Z., Wanga, C., Jiang, Z.: A metamodel-assisted evolutionary algorithm for expensive optimization. Journal of Computational and Applied Mathematics (2011), doi:10.1016/j.cam.2011.05.047Google Scholar
- 28.Van-Aken, J.R.: An Efficient Ellipse Drawing Algorithm. CG&A 4, 24–35 (1984)Google Scholar