• DocumentCode
    510309
  • Title

    PCNN Edge Detection of Sintering Pellets Image Based on Hybrid Harmony Search Algorithm

  • Author

    Wang Jie-sheng ; Gao Xian-wen

  • Author_Institution
    Sch. of Electron. & Inf. Eng., Liaoning Univ. of Sci. & Technol., Anshan, China
  • Volume
    3
  • fYear
    2009
  • fDate
    7-8 Nov. 2009
  • Firstpage
    535
  • Lastpage
    539
  • Abstract
    Harmony search (HS) is a newly developed derivativefree and meta-heuristic algorithm mimicking the improvisation process of musicians, which has been very successful in a wide variety of optimization problems. A hybrid optimization method is proposed for self-tuning pulse coupled neural network (PCNN) parameters, a biologically inspired spiking neural network, based on harmony search algorithm and simulated annealing (SA) idea and was used to detect sintering pellets image edges automatically and successfully. The effective of the proposed method is verified by simulation results, that is to say, the quality of the sintering pellets grayscale image edge detection is much better and parameters are set automatically.
  • Keywords
    edge detection; neural nets; simulated annealing; PCNN edge detection; harmony search algorithm; meta-heuristic algorithm; optimization problem; self-tuning pulse coupled neural network; simulated annealing; sintering pellets grayscale image edge detection; spiking neural network; Biological system modeling; Data mining; Digital images; Genetic algorithms; Image edge detection; Image processing; Image segmentation; Neural networks; Optimization methods; Simulated annealing; edge detection; harmony search algorithm; pulse-coupled neural network; simulated annealing; sintering pellets image;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-3835-8
  • Electronic_ISBN
    978-0-7695-3816-7
  • Type

    conf

  • DOI
    10.1109/AICI.2009.81
  • Filename
    5376802