• DocumentCode
    2046962
  • Title

    Application of Neural Networks in Image Definition Recognition

  • Author

    Guojin, Chen ; Miaofen, Zhu ; Honghao, Yu ; Yan, Li

  • Author_Institution
    Hangzhou Dianzi Univ., Hangzhou, China
  • fYear
    2007
  • fDate
    24-27 Nov. 2007
  • Firstpage
    1207
  • Lastpage
    1210
  • Abstract
    To simulate the auto-focus mechanism of human´s eyes by using artificial neural networks is a reasonable and available way to improve the auto-focus effect of digital cameras. The evaluation of image definition plays a key role in the auto-focus of digital cameras, so constructing the image definition evaluation is an important step of designing auto-focus systems. In this paper, we use the pattern recognition method of the RBF neural networks to recognize the image definition. The designed neural networks are trained by the training set that is composed of 75 pictures. Then, the testing set that is composed of 102 pictures is verified experimentally. The experiment results indicate that this method is quite effective. The method gives full play to self-adapting ability of neural networks and gets a higher recognition rate.
  • Keywords
    cameras; image recognition; learning (artificial intelligence); optical focusing; radial basis function networks; RBF neural networks; artificial neural networks; auto-focus effect; auto-focus mechanism; auto-focus systems; digital cameras; human eyes; image definition evaluation; image definition recognition; neural networks training; pattern recognition method; self-adapting ability; Artificial neural networks; Biomedical optical imaging; Digital cameras; Eyes; Focusing; Humans; Image recognition; Neural networks; Pattern recognition; Retina; Neural networks; image processing; pattern recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on
  • Conference_Location
    Dubai
  • Print_ISBN
    978-1-4244-1235-8
  • Electronic_ISBN
    978-1-4244-1236-5
  • Type

    conf

  • DOI
    10.1109/ICSPC.2007.4728542
  • Filename
    4728542