• DocumentCode
    547792
  • Title

    Apple defect detection using statistical histogram based EM algorithm

  • Author

    Moradi, Ghobad ; Shamsi, Mousa ; Sedaaghi, Mohammad Hossein ; Moradi, Setareh ; Alsharif, Mohammad Reza

  • Author_Institution
    Young Researcher Club, Branch of Kermanshah Azad Univ., Kermanshah, Iran
  • fYear
    2011
  • fDate
    17-19 May 2011
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Segmentation of an image into its components plays an important role in most of the image processing applications. In this article an important application of image processing in determination of apple quality is studied, and an automatic algorithm is proposed in order to determine apples skin color defects. First, this image is converted from RGB to color space L* a* b*. Then fruit shape is extracted by A CM algorithm. Finally, the image has segmented using SHEM a lgor ithm. Ex per im ental r esults on the a cquir ed im ages show that both EM and SHEM spend the same iterations to accomplish the segmentation process and get the same results. However, the proposed SHEM algorithm consumes less time than the standard EM algorithm. Accuracy of the proposed algorithm on the acquired images is 91.72% and 94.86% for healthy pixels and defected ones, respectively.
  • Keywords
    image colour analysis; image segmentation; iterative methods; statistical analysis; ACM algorithm; EM algorithm; RGB; SHEM algorithm; apple defect detection; apples skin color defects; automatic algorithm; healthy pixels; image processing application; image segmentation; iteration; statistical histogram; Accuracy; Classification algorithms; Histograms; Image color analysis; Image segmentation; Pixel; Image segmentation; active counter model; apple defects; color space; expectation maximization algorithm; statistical histogram;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Engineering (ICEE), 2011 19th Iranian Conference on
  • Conference_Location
    Tehran
  • Print_ISBN
    978-1-4577-0730-8
  • Electronic_ISBN
    978-964-463-428-4
  • Type

    conf

  • Filename
    5955681