A real-time ellipse detection based on edge grouping

In this paper, we present a efficient algorithm for real-time ellipse detection. Unlike Hough transform algorithm that is computationally intense and requires a higher dimensional parameter space, our proposed method reduces the computational complexity significantly, and accurately detects ellipses in realtime. We present a new method of detecting arc-segments from the image, based on the properties of ellipse. We then group the arc-segments into elliptical arcs in order to estimate the parameters of the ellipse, which are calculated using the least-square method. Our method has been tested and implemented on synthetic and real-world images containing both complete and incomplete ellipses. The performance is compared to existing ellipse detection algorithms, demonstrating the robustness, accuracy and effectiveness of our algorithm.

[1]  M. Campani,et al.  Robust road sign detection and recognition from image sequences , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[2]  Andrew W. Fitzgibbon,et al.  Direct Least Square Fitting of Ellipses , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Robert A. McLaughlin,et al.  The Hough Transform Versus the UpWrite , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Robert A. McLaughlin,et al.  Randomized Hough Transform: Improved ellipse detection with comparison , 1998, Pattern Recognit. Lett..

[5]  John Illingworth,et al.  A Comparison of the Randomised Hough Transform and a Genetic Algorithm for Ellipse Extraction , 1994 .

[6]  Jie Yao,et al.  A multi-population genetic algorithm for robust and fast ellipse detection , 2005, Pattern Analysis and Applications.

[7]  Euijin Kim,et al.  Fast and Robust Ellipse Extraction from Complicated Images , 2002 .

[8]  R. Halír.  Numerically Stable Direct Least Squares Fitting of Ellipses , 1998 .

[9]  Yuncai Liu,et al.  Efficient technique for ellipse detection using restricted randomized Hough transform , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..

[10]  Cataldo Guaragnella,et al.  A new algorithm for ball recognition using circle Hough transform and neural classifier , 2004, Pattern Recognit..

[11]  Qiang Ji,et al.  Camera self-calibration from ellipse correspondences , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[12]  K.-U. Kasemir,et al.  Detecting ellipses of limited eccentricity in images with high noise levels , 2003, Image Vis. Comput..

[13]  Yeung Sam Hung,et al.  A Hierarchical Approach for Fast and Robust Ellipse Extraction , 2007, 2007 IEEE International Conference on Image Processing.

[14]  C. J. Radford,et al.  Vehicle detection in open-world scenes using a Hough transform technique , 1989 .

[15]  R. A. McLaughlin,et al.  Randomized Hough transform: better ellipse detection , 1996, Proceedings of Digital Processing Applications (TENCON '96).

[16]  Erkki Oja,et al.  A new curve detection method: Randomized Hough transform (RHT) , 1990, Pattern Recognit. Lett..

[17]  J. Flusser,et al.  Numerically Stable Direct Least Squares Fitting of Ellipses , 1998 .

[18]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  P. S. Nair,et al.  Hough transform based ellipse detection algorithm , 1996, Pattern Recognit. Lett..

[20]  Yeung Sam Hung,et al.  A Hierarchical Approach for Fast and Robust Ellipse Extraction , 2007, ICIP.