• DocumentCode
    144825
  • Title

    Color model selection for underwater object recognition

  • Author

    Dalei Song ; Weicheng Sun ; Zehui Ji ; Guojia Hou ; Xiufang Li ; Liang Liu

  • Author_Institution
    Coll. of Eng., Ocean Univ. of China, Qingdao, China
  • Volume
    2
  • fYear
    2014
  • fDate
    26-28 April 2014
  • Firstpage
    1339
  • Lastpage
    1342
  • Abstract
    In this paper, the characteristics of underwater image and the importance of color features in underwater object recognition are presented. Classical illumination invariant color models are analyzed. Our goal is to study and evaluate the various color models in underwater object recognition applications. The illumination invariant color models yuv, c1c2c3, HSy, rSc2 and uSb are applied to carry out comparison experimental tests and the traditional primary colors of underwater targets are selected as object identification. The experiment object images with different illumination intensities are captured by a color video camera equipped in the underwater robotics. The comparison experimental results demonstrate that the yuv and uSb color model achieve high ART (Average Recognition Rate) and lower ERT (Error Recognition Rate).
  • Keywords
    image colour analysis; mobile robots; object recognition; underwater vehicles; video cameras; average recognition rate; color features; color model selection; color video camera; error recognition rate; illumination intensity; illumination invariant color models; lower ERT; object identification; object images; underwater image; underwater object recognition; underwater robotics; underwater targets; Computational modeling; Image color analysis; Image recognition; Lighting; Mathematical model; Object recognition; Robots; color model; illumination invariant; primary colors; underwater object recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science, Electronics and Electrical Engineering (ISEEE), 2014 International Conference on
  • Conference_Location
    Sapporo
  • Print_ISBN
    978-1-4799-3196-5
  • Type

    conf

  • DOI
    10.1109/InfoSEEE.2014.6947890
  • Filename
    6947890