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Scale Sensitivity of Textural Features

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    0471593 - ÚTIA 2018 RIV DE eng C - Conference Paper (international conference)
    Haindl, Michal - Vácha, Pavel
    Scale Sensitivity of Textural Features.
    Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 21st Iberoamerican Congress, CIARP 2016. Cham: Springer International Publishing, 2017 - (Beltran-Castanon, C.; Nystrom, I.; Famili, F.), s. 84-92. Lecture Notes in Computer Science, 10125. ISBN 978-3-319-52276-0.
    [CIARP 2016 - 21st Iberoamerican Congress 2016. Lima (PE), 08.11.2016-11.11.2016]
    R&D Projects: GA ČR(CZ) GA14-10911S
    Institutional support: RVO:67985556
    Keywords : Textural features * texture scale recognition sensitivity * surface material recognition * Markovian illumination invariant features
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    http://library.utia.cas.cz/separaty/2017/RO/haindl-0471593.pdf

    Prevailing surface material recognition methods are based on textural features but most of these features are very sensitive to scale variations and the recognition accuracy significantly declines with scale incompatibility between visual material measurements used for learning and unknown materials to be recognized. This effect of mutual incompatibility between training and testing visual material measurements scale on the recognition accuracy is investigated for leading textural features and verified on a wood database, which contains veneers from sixty-six varied European and exotic wood species. The results show that the presented textural features, which are illumination invariants extracted from a generative multispectral Markovian texture representation, outperform the most common alternatives, such as Local Binary Patterns, Gabor features, or histogram-based approaches.
    Permanent Link: http://hdl.handle.net/11104/0271350

     
     
Number of the records: 1