• DocumentCode
    1465317
  • Title

    Refining structural texture synthesis approach

  • Author

    Bartakke, P.P. ; Vaidya, S.A. ; Sutaone, M.S.

  • Author_Institution
    Dept. of Electron. & Telecommun., Coll. of Eng., Pune, India
  • Volume
    5
  • Issue
    2
  • fYear
    2011
  • fDate
    3/1/2011 12:00:00 AM
  • Firstpage
    184
  • Lastpage
    189
  • Abstract
    Structural textures are characterised by a repeating pattern called `Texton` and placement rule - that determines the nature of periodicity. Based on the periodicity, textures are classified as homogeneous - perfectly periodic - and weakly homogeneous - quasi-periodic. Both of these textures are assumed to be combination of structural information, illumination, that is, average brightness at different sites of the texture and stochasticity to allow local variations. A top-down approach extracts structural information, that is, the grid, representative texton and illumination component from the original texture patch and then the information is used to synthesise similar textures. Obviously, the technique does not produce ditto copies in the synthesised texture. The experimentation is carried out to improve quality of synthesised textures by incorporating multiple representative textons. Also a parameter, namely homogeneity co-efficient (HC), is suggested to compare the original texture patch and synthesised texture. The parameter captures variations in the textons, contents and sizes both, and thus can be used to compare the synthesis results. The suitability of the proposed synthesis approach and HC is verified by rigorous experimentation on weakly homogeneous artificial and standard structural textures. Efforts are also made to utilise the multi-core processing capability of the processor to improve the speed of analysis and synthesis phases.
  • Keywords
    image classification; image texture; stochastic processes; HC; homogeneity coefficient; illumination; multicore processing capability; multiple representative textons; placement rule; stochastic process; structural information extraction; structural texture synthesis approach;
  • fLanguage
    English
  • Journal_Title
    Image Processing, IET
  • Publisher
    iet
  • ISSN
    1751-9659
  • Type

    jour

  • DOI
    10.1049/iet-ipr.2010.0096
  • Filename
    5724120