• DocumentCode
    3391736
  • Title

    Binary fuzzy rough set model based on triangle modulus and its application to image processing

  • Author

    Wang Dan ; Wu, Meng-Da

  • Author_Institution
    Dept. of Mathematic & Syst. Sci., Nat. Univ. of Defense Technol., Changsha, China
  • fYear
    2009
  • fDate
    15-17 June 2009
  • Firstpage
    249
  • Lastpage
    255
  • Abstract
    Rough sets theory is an important tool that process uncertainty information. In this paper, image processing based on rough sets theory is discussed in detail. The paper presents a binary fuzzy rough set model based on triangle modulus, which describes binary relationship by upper approximation and lower approximation. As image can be described by binary relationship, the upper approximation and lower approximation can be used to represent an image. The model in this paper is well fit for processing image that have gentle gray change. An edge detection algorithm by the upper approximation and the lower approximation of image is presented, and image denoising also is discussed. At last, its better effect can be testified by many experiments.
  • Keywords
    approximation theory; edge detection; fuzzy set theory; image denoising; image representation; rough set theory; approximation theory; binary fuzzy rough set model; edge detection algorithm; gray image; image denoising; image processing; image representation; triangle modulus; Approximation algorithms; Change detection algorithms; Fuzzy set theory; Fuzzy sets; Image denoising; Image edge detection; Image processing; Pattern analysis; Rough sets; Uncertainty; Rough Sets; edge detection; image denoising; the upper (lower) approximation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cognitive Informatics, 2009. ICCI '09. 8th IEEE International Conference on
  • Conference_Location
    Kowloon, Hong Kong
  • Print_ISBN
    978-1-4244-4642-1
  • Type

    conf

  • DOI
    10.1109/COGINF.2009.5250738
  • Filename
    5250738