• DocumentCode
    2040925
  • Title

    Surface targets recognition method based on LVQ neutral network

  • Author

    Peng Li ; Yihui Zhang ; Chao Wang ; Shuangmiao Li

  • Author_Institution
    Coll. of Autom. of Harbin Eng., Univ. of Harbin, Harbin, China
  • fYear
    2015
  • fDate
    2-5 Aug. 2015
  • Firstpage
    676
  • Lastpage
    680
  • Abstract
    These A method to identify the different surface targets with combination features was proposed on the conditions of pretreatment that the video image sequence was preprocessed by removing noise and image stabilization. Firstly, Targets and background were separated by segmenting the clearer images. Secondly, the geometrical feature and the moment invariant feature in different targets were extracted. The LVQ (Learning Vector Quantization) neutral network was trained to identify surface targets by using combination features. Finally, the simulation study of identifying test targets was done. The results of simulation research show that the proposed method based on combination features of different surface targets can recognizes the three types of common surface targets effectively. And, the convergence speed of LVQ neural network is fast compared with the BP neural network and the recognition has a good effect.
  • Keywords
    feature extraction; image denoising; image recognition; image segmentation; image sequences; learning (artificial intelligence); neural nets; vector quantisation; LVQ neural network; geometrical feature extraction; image denoising; image segmentation; image stabilization; learning vector quantization neutral network; moment invariant feature extraction; surface target recognition method; video image sequence; Biological neural networks; Feature extraction; Marine vehicles; Neurons; Target recognition; Training; LVQ neutral network; combination features; surface targets; targets recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronics and Automation (ICMA), 2015 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-7097-1
  • Type

    conf

  • DOI
    10.1109/ICMA.2015.7237566
  • Filename
    7237566