• DocumentCode
    2457393
  • Title

    The Model of Visual Attention Infrared Target Detection Algorithm

  • Author

    Tingjun, Li ; Fuguang, Zhang ; Xinju, Cai ; Qilai, Huang ; Qiang, Guo

  • Author_Institution
    Naval Aeronaut. & Astronaut. Univ., Yantai, China
  • Volume
    3
  • fYear
    2010
  • fDate
    12-14 April 2010
  • Firstpage
    87
  • Lastpage
    91
  • Abstract
    The model of visual attention infrared target detection algorithm is presented. Mainly the visual features are extracted from the brightness contrast and movement in the current frame still images and image sequences of the motion vector, and then a linear convergence significantly diagram, with locally adaptive thresholding instead of "Winner-Takes-All" neural network (Winner-Take-All, WTA), through the similarity of pixel gray scale and significant regional centroid of the adjacency to split the objectives and background, finally be interested in infrared image targets (including thermal targets and moving targets). Simulation results show that the method for the fusion system, the lower the contrast of the image after the video sequence scene moving target detection with good results.
  • Keywords
    feature extraction; image fusion; image motion analysis; image segmentation; infrared imaging; neural nets; object detection; video signal processing; brightness contrast; fusion system; image sequences; locally adaptive thresholding; motion vector; still images; video sequence; visual attention infrared target detection algorithm; visual feature extraction; winner-takes-all neural network; Brightness; Convergence; Feature extraction; Image sequences; Infrared detectors; Infrared imaging; Neural networks; Object detection; Pixel; Vectors; image fusion; infrared target detection; segmentation of threshold; visual attention model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications and Mobile Computing (CMC), 2010 International Conference on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    978-1-4244-6327-5
  • Electronic_ISBN
    978-1-4244-6328-2
  • Type

    conf

  • DOI
    10.1109/CMC.2010.16
  • Filename
    5471523