• DocumentCode
    3130283
  • Title

    Vehicle license image segmentation using wavelet transform

  • Author

    Zhu, Jianlong ; Zhao, Yannan

  • Author_Institution
    Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    267
  • Lastpage
    270
  • Abstract
    A novel multiscale method of vehicle license image segmentation based on wavelet transform is proposed. This analysis utilizes the local wavelet transform modulus maxima as the image edge at multiple scales, and combines the multiscale edge information. Then a template matching method is applied to segment the vehicle license image based on edge density analysis and character edge spatial feature after eliminating the long straight line noise. The approach integrates multiple scale edge information and overcomes the shortcoming of traditional single scale analysis. This advantage has special significance for the hazy image. Experimentation with about three hundred images obtained from a natural environment shows that the performance of this approach is better than the traditional method, especially for hazy images
  • Keywords
    automobiles; edge detection; image matching; image segmentation; wavelet transforms; character edge spatial feature; edge density analysis; hazy images; image edge; local wavelet transform modulus maxima; long straight line noise; multiple scale edge information; multiple scales; multiscale edge information; multiscale method; natural environment; template matching method; traditional single scale analysis; vehicle license image segmentation; wavelet transform; Image analysis; Image edge detection; Image segmentation; Image texture analysis; Information analysis; Intelligent transportation systems; Licenses; Vehicles; Wavelet analysis; Wavelet transforms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Multimedia, Video and Speech Processing, 2001. Proceedings of 2001 International Symposium on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    962-85766-2-3
  • Type

    conf

  • DOI
    10.1109/ISIMP.2001.925385
  • Filename
    925385