• DocumentCode
    584552
  • Title

    Study on Detection Method of Small Object on Sea Based on Kernel-MRF Foreground Segmentation Model

  • Author

    Yong-Xin, Jiang ; Qun-zhe, Yuan ; Cheng-yong, Shao ; Chang-qin, Yang ; Xi-yong, Ye

  • Author_Institution
    Dept. of Equip. Autom., Dalian Naval Acad., Dalian, China
  • fYear
    2012
  • fDate
    11-13 Aug. 2012
  • Firstpage
    1869
  • Lastpage
    1872
  • Abstract
    It is very difficult to detect the small target over water in the marine environment because of the complexity of water movement and the complex physical fields produced by the water interact with environment. We mainly study foreground segmentation for small target in visual image based on MRF (Markov Random Fields). A foreground segmentation method is proposed that is based on kernel function and MRF. In this method, the kernel function uses the special and temporal correlation between neighbor pixels. We take the probability distribution of the kernel function method as the observed value of the MRF, and get the energy function of the MRF. Results show our method is applicable to low speed small target segmentation in sea image.
  • Keywords
    Markov processes; image segmentation; image sequences; marine engineering; object detection; statistical distributions; video signal processing; Markov random fields; complex physical fields; energy function; kernel function; kernel-MRF foreground segmentation model; marine environment; probability distribution; small object detection method; small target detection; temporal correlation; video sequence; water movement complexity; Computational modeling; Image segmentation; Kernel; Motion segmentation; Noise; Object detection; Silicon; Markov Random Field (MRF); kernel functions; object detection; video sequence;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science & Service System (CSSS), 2012 International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4673-0721-5
  • Type

    conf

  • DOI
    10.1109/CSSS.2012.465
  • Filename
    6394784