• DocumentCode
    2719943
  • Title

    ATR System Based on the K-M BP Neural Network

  • Author

    Yuan, Zhijie ; Mu, Chengpo ; Chen, Yuanqian ; Song, Jia

  • Author_Institution
    Beijing Inst. of Technol., Beijing, China
  • fYear
    2012
  • fDate
    11-13 Aug. 2012
  • Firstpage
    1197
  • Lastpage
    1200
  • Abstract
    Automatic target recognition (ATR) is an important issue in the military field, the topic of the ATR system is the pattern recognition and classification. In the paper, we present an approach for building an ATR system with improved artificial neural network to recognize and classify the typical targets in the army field. The invariant features of Hu invariant moments and roundness were selected to be the input of the neural network for they have the invariance of rotation, translation and scaling. The pictures of the targets are generated by the 3-D models to improve the recognition rate for it is necessary to provide enough pictures for training the artificial neural network. The simulations prove that the approach can implement the task of ATR system in high recognition rate and real time.
  • Keywords
    backpropagation; image classification; military systems; neural nets; object recognition; solid modelling; 3D models; ATR system; Hu invariant moments; K-M BP neural network; army field; artificial neural network; automatic target recognition; invariant features; military system; neural network training; pattern classification; pattern recognition; recognition rate improvement; rotation invariance; roundness; scaling invariance; target classification; target picture generation; translation invariance; Artificial neural networks; Biological neural networks; Image recognition; Neurons; Solid modeling; Target recognition; Training; ATR syste; BP neural network; Hu invariant; pattern recognition; pictures generation; roundness;
  • 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.2012.303
  • Filename
    6394541