• DocumentCode
    3209737
  • Title

    Infrared target detection based on fuzzy ART neural network

  • Author

    Bingwen Chen ; Wenwei Wang ; Qianqing Qin

  • Author_Institution
    Coll. of Electron. Inf., Wuhan Univ., Wuhan, China
  • Volume
    2
  • fYear
    2010
  • fDate
    13-14 Sept. 2010
  • Firstpage
    240
  • Lastpage
    243
  • Abstract
    The infrared target detection is a challenge task. In order to solve the lower signal-to-noise ratio, the lower resolution and the halo effect problems, we propose a novel detection approach based on fuzzy ART neural network. The fuzzy ART neural network is capable of rapid stable learning of recognition categories, and it can determine the total number of categories adaptively. At first, in the background modeling stage, the fuzzy ART neural networks were applied to classify the background and non-background categories, and the non-background categories were discarded so as to build the background model. Then the background model was combined with fuzzy ART neural networks to detect the targets. Experiments have been carried out and the results demonstrate that the proposed approach is robust to noise, and can eliminate the halo effectively. It can detect the targets effectively without much more post-process.
  • Keywords
    ART neural nets; fuzzy neural nets; infrared imaging; learning (artificial intelligence); object detection; fuzzy ART neural network; halo effect problems; infrared target detection; stable learning; Fuzzy logic; Image segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Natural Computing Proceedings (CINC), 2010 Second International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-7705-0
  • Type

    conf

  • DOI
    10.1109/CINC.2010.5643745
  • Filename
    5643745