• DocumentCode
    708196
  • Title

    Ellipse detection method based on the advanced three point algorithm

  • Author

    Bae-keun Kwon ; Dong-joong Kang

  • Author_Institution
    Sch. of Mech. Eng., Pusan Nat. Univ., Pusan, South Korea
  • fYear
    2015
  • fDate
    28-30 Jan. 2015
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    In this paper, we propose a fast ellipse detection method using the geometric properties of three points, which are the components of an ellipse. As many conventional ellipse detection methods carry out the detection using five points, a random selection of such points requires much redundant processing. Accordingly, in order to search for an ellipse with minimum number of points, this paper uses the normal and differential equation of an ellipse which requires three points based on their locations and edge angles. First, in order to reduce the number of candidate edges, the edges are divided into 8 groups depending on the edge angle, and then a new geometric constraint called quadrant condition is introduced for the reduction of noisy candidate edges. Clustering is employed to find prominent candidates in the space of some ellipse parameters. Experiments through real images show that our method satisfies both the reliability and detection speed of ellipse detection.
  • Keywords
    edge detection; pattern clustering; advanced three point algorithm; clustering; differential equation; edge angle; ellipse detection method; geometric constraint; noisy candidate edge reduction; normal equation; quadrant condition; Clustering algorithms; Clutter; Image edge detection; Image resolution; Noise measurement; Plastics; Reliability; Ellipse detection; Mean-shift clustering; Quadrant Constraint; Three-point algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Frontiers of Computer Vision (FCV), 2015 21st Korea-Japan Joint Workshop on
  • Conference_Location
    Mokpo
  • Type

    conf

  • DOI
    10.1109/FCV.2015.7103741
  • Filename
    7103741