{"title":"Robust Ellipse Detection by Fitting Randomly Selected Edge Patches","authors":"Watcharin Kaewapichai, Pakorn Kaewtrakulpong","volume":24,"journal":"International Journal of Computer and Information Engineering","pagesStart":4018,"pagesEnd":4022,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/14384","abstract":"
In this paper, a method to detect multiple ellipses is presented. The technique is efficient and robust against incomplete ellipses due to partial occlusion, noise or missing edges and outliers. It is an iterative technique that finds and removes the best ellipse until no reasonable ellipse is found. At each run, the best ellipse is extracted from randomly selected edge patches, its fitness calculated and compared to a fitness threshold. RANSAC algorithm is applied as a sampling process together with the Direct Least Square fitting of ellipses (DLS) as the fitting algorithm. In our experiment, the method performs very well and is robust against noise and spurious edges on both synthetic and real-world image data.<\/p>\r\n","references":"[1] L. Xu and E. Oja, \"Randomized Hough Transform (RHT): Basic\r\nMechanisms, Algorithms, and Computational Complexities,\" Graphical\r\nModels and mage rocessing mage Understanding, vol. 57, pp\r\n111-122, 1993.\r\n[2] R.A. McLaughlin, \"Randomized Hough Transform: Better Ellipse\r\nDetection,\" EEE TENC N igital ignal rocessing Applications\r\n1996, pp. 409-414.\r\n[3] D. Ben-Tzvi and M.B. Sandler, \"A Combinatorial Hough Transform,\"\r\nattern Recognition Letter, vol. 11, pp 167-174, 1990.\r\n[4] N. Kiryati, Y. Eldar, and A.M. Bruckstein, \"Probabilistic Hough\r\ntransform,\" attern Recognition Letter, vol. 24, pp 303-316, 1991.\r\n[5] V.F. Leavers, \"The Dynamic Generalized Hough Transform: Its\r\nRelationship to The Probabilistic Hough Transforms and An Application\r\nto The Concurrent Detection of Circles and Ellipse,\" Graphical Models\r\nand mage rocessing mage Understanding, vol. 56, pp. 381-398,\r\n1992.\r\n[6] E. Lutton and P. Martinez, \"A Genetic Algorithm for the Detection of 2D\r\nGeometric Primitives in Images,\" roceedings of the 12th A R\r\nnternational Conference on attern Recognition, vol. 1, pp. 526-528,\r\n1994.\r\n[7] . Yao, N. Kharma, and P. Grogono, \"A Multi-Population Genetic\r\nAlgorithm for Robust and Fast Ellipse Detection,\" attern Analysis and\r\nApplication, vol. 8, pp.169-162, 2005.\r\n[8] A. Soetedjo and K. Yamada, \"Fast and Robust Traffic Sign Detection,\"\r\nEEE nternal Conference on ystems Man and Cybernetics vol. 2, pp.\r\n1341-1346, 2005.\r\n[9] T. Kawaquchi and R.-I. Nagata, \"Ellipse Detection Using a Genetic\r\nAlgorithm,\" roceedings ourteenth nternational Conference on\r\nattern Recognition, vol. 1, pp. 141-145, 1998.\r\n[10] G. Song and H. Wang, \"A Fast and Robust Ellipse Detection Algorithm\r\nBased on Pseudo-random Sample Consensus,\" Center for Advanced\r\nnformation rocessing, pp. 669-676, 2007.\r\n[11] M.A. Fischler, and R.C. Bolles, \"Random Sample Consensus: A\r\nParadigm for Modle Fitting with Applications to Image Analysis and\r\nAutomated Cartography,\" Communication of the Association for\r\nComputing Machinery, vol. 24, pp. 381-395, 1981.\r\n[12] A. Fitzgibbon, M. Pilu, and R.B. Fisher, \"Direct Least Square Fitting of\r\nEllipse,\" EEE Transactions on attern Analysis and Machine\r\nntelligence, vol. 21, pp. 446-480, 1999.\r\n[13] Halif and . Flusser, \"Numerically Stable Direct Least Squares Fitting\r\nof Ellipses,\" nternational Conference in Central Europe on Computer\r\nGraphics isualization and nteractive igital Media, pp.125-132,\r\n1998.\r\n[14] Nicholas Higham, \"Handbook of writing for the mathematical\r\nsciences,\" SIAM. ISBN 0898714206, pp. 25.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 24, 2008"}