• DocumentCode
    3283998
  • Title

    A Research on the Recognition of Chironomid Larvae Based on SVM

  • Author

    Zhao, Jing-Ying ; Guo, Hai ; Sun, Xing-Bin

  • Author_Institution
    Dept. of Comput. Sci.& Eng., Dalian Nat. Univ., Dalian, China
  • fYear
    2009
  • fDate
    16-17 May 2009
  • Firstpage
    610
  • Lastpage
    613
  • Abstract
    The traditional method of detecting Chironomid larvaes and plankton mostly is manual identification, which is inefficient. This paper puts forward the Chironomid larvae recognition method which is based on the support vector machines. The Chironomid larvae images are decomposed using a wavelet from which the features were extracted and kernel function is used. The experiment shows that the recognition rate is up to 86%, which demonstrates the effectiveness of this method.
  • Keywords
    environmental science computing; image recognition; support vector machines; water quality; wavelet transforms; Chironomid larvae recognition; SVM; support vector machines; Circuits; Image analysis; Image recognition; Kernel; Marine vegetation; Monitoring; Pattern recognition; Support vector machine classification; Support vector machines; Water resources; Chironomid larvae recognition; biometric recognition; support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits, Communications and Systems, 2009. PACCS '09. Pacific-Asia Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-0-7695-3614-9
  • Type

    conf

  • DOI
    10.1109/PACCS.2009.70
  • Filename
    5232016