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A randomized algorithm for detecting multiple ellipses based on least square approach

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Języki publikacji
EN
Abstrakty
EN
In this paper, a randomized method for detecting multiple ellipses based on the least square approach is presented. The main concept used is that we first randomly select three edge pixels in the image, which are the centre of three windows with the same size. In order to determine a possible ellipse, we use the least square method to fit all the edge points in these three window, and to solve the ellipse parameters through Lagrange multiplier method. Then we randomly select the fourth edge pixel in the image and define a distance criterion to determine whether there is a possible ellipse in the image. After finding a possible ellipse, we apply a further verification process to determine whether the possible ellipse is a true ellipse or not. Some artificial images with different levels of noises and some natural grey images containing circular objects with some occluded ellipses and missing edges have been taken to test the performance. Experimental results demonstrate that the proposed algorithm is faster and more accurate than other methods.
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autor
  • Institute of System Engineering, Xi'an Jiaotong University, XI'an, ShanXi, 710049, P. R. China
autor
  • Institute of System Engineering, Xi'an Jiaotong University, XI'an, ShanXi, 710049, P. R. China
autor
  • Institute of System Engineering, Xi'an Jiaotong University, XI'an, ShanXi, 710049, P. R. China
Bibliografia
  • 1. N. Yamaguchi and H. Mizoguchi, “Robot vision to recognize both face and object for human-robot ball playing”, IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, Vol. 2, 999–1004 (2003).
  • 2. G. Adorni, S. Cagnoni, and M. Mordonini, “Landmark-based robot self-localization: a case study for the RoboCup goal-keeper”, Int. Conf. on Information Intelligence and Systems, 164–171 (1999).
  • 3. P.V.C. Hough, “Method and means for recognizing complex patterns”, U.S. Patent 3069654, 1962.
  • 4. Ch. The-Chuan and Ch. Kuo-Liang, “An efficient randomized algorithm for detecting circles”, Computer Vision and Image Understanding 83, 172–191 (2001).
  • 5. Ch. Cheng and Y. Liu, “Efficient technique for ellipse detection using restricted randomized Hough transform”, Int. Con. on Information Technology: Coding and Computing, Vol. 2, 714–718 (2004).
  • 6. R.A. McLaughlin, “Randomized Hough transform: better ellipse detection”, IEEE Proc. on TENCON, Digital Signal Processing Applications, Vol. 1, 409–414 (1996).
  • 7. R.A. McLaughlin, “Randomized Hough transform: improved ellipse detection with comparison” 19, 299–305 (1998).
  • 8. H.T. Sheu, H.Y. Chen, and W.C. Hu, “Consistent symmetric axis method for robust detection of ellipses”, IEE Proceedings on Vision, Image and Signal Processing 144, 332–338 (1997).
  • 9. R. Halir and J. Flusser, “Numerically stable direct least squares fitting of ellipses”, 6th Int. Conf. in Central Europe on Computer Graphics and Visualization, (1998).
  • 10. A. Fitzgibbon, M. Pilu, and R.B. Fisher, “Direct least square fitting of ellipses”, IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 476–480 (1999).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA2-0015-0060
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