• DocumentCode
    1586595
  • Title

    Covariant support region and detection algorithm based on LoG corners

  • Author

    Liu, Yawei ; Li, Jianwei

  • Author_Institution
    Key Lab. on Opto-Electron. Tech. of State Educ. Minist., Chongqing Jiaotong Univ., Chongqing, China
  • fYear
    2009
  • Firstpage
    1200
  • Lastpage
    1204
  • Abstract
    Detection of local feature covariant region is a new technology of image contents and image semantic representations, and it has become an important foundation of the image recognition, learning and understanding. First, a Laplace of Gaussian corner detection method is proposed based on edge contour curves, in the meantime, a new local feature descriptor, named covariant support region, is introduced. Then, a detection algorithm of covariant support region is framed, which is covariant for rotation and scale transformation. Comparing with previous studies, the computational complexity of proposed algorithm is significantly reduced by this method. The experiments data indicate that the method proposed in this paper has good performance on higher accuracy, higher repeatability, and lower complexity.
  • Keywords
    computational complexity; edge detection; image representation; Gaussian corner detection method; LoG corners; computational complexity; covariant support region detection algorithm; edge contour curves; image content representation; image recognition; image semantic representations; local feature covariant region detection; local feature descriptor; Biomimetics; Computational complexity; Detection algorithms; Detectors; Image edge detection; Image recognition; Image reconstruction; Object detection; Robot vision systems; Surface reconstruction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference on
  • Conference_Location
    Guilin
  • Print_ISBN
    978-1-4244-4774-9
  • Electronic_ISBN
    978-1-4244-4775-6
  • Type

    conf

  • DOI
    10.1109/ROBIO.2009.5420847
  • Filename
    5420847