• DocumentCode
    3707307
  • Title

    Ellipse-specific fitting by relaxing the 3L constraints with semidefinite programming

  • Author

    Jiangpeng Rong;Sen Yang;Xiang Mei;Xianghua Ying;Shiyao Huang;Hongbin Zha

  • Author_Institution
    Key Laboratory of Machine Perception (Ministry of Education), School of Electronic Engineering and Computer Science, Center for Information Science, Peking University, Beijing 100871, P.R. China
  • fYear
    2015
  • Firstpage
    710
  • Lastpage
    714
  • Abstract
    This paper presents a new efficient method to increase the accuracy and the robustness of ellipse fitting, by utilizing the 3L algorithm and semidefinite programming (SDP). The novelty lies on the combination of relaxed geometric distance constraints and semidefinite programming framework. Due to the relaxed 3L constraints, the proposed approach provides high robustness in the presence of noise. The accuracy of the final solution is prominently increased even if the data suffer from strong occlusions or noises. The proposed method represents significant advantages in both accuracy and robustness. Experimental results and comparisons with state-of-the-art fitting methods demonstrate the improvements in ellipse fitting.
  • Keywords
    "Fitting","Robustness","Convex functions","Noise level","Polynomials","Level set","Shape"
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/ICIP.2015.7350891
  • Filename
    7350891