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Variational Blind Source Separation Toolbox and its Application to Hyperspectral Image Data

  1. 1.
    0447094 - ÚTIA 2016 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
    Tichý, Ondřej - Šmídl, Václav
    Variational Blind Source Separation Toolbox and its Application to Hyperspectral Image Data.
    Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015). Piscataway: IEEE Computer Society, 2015, s. 1336-1340. ISBN 978-0-9928626-4-0. ISSN 2076-1465.
    [23rd European Signal Processing Conference (EUSIPCO). Nice (FR), 31.08.2015-04.09.2015]
    Grant CEP: GA ČR GA13-29225S
    Institucionální podpora: RVO:67985556
    Klíčová slova: Blind source separation * Variational Bayes method * Sparse prior * Hyperspectral image
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    http://library.utia.cas.cz/separaty/2015/AS/tichy-0447094.pdf

    The task of blind source separation (BSS) is to decompose sources that are observed only via their linear combination with unknown weights. The separation is possible when additional assumptions on the initial sources are given. Different assumptions yield different separation algorithms. Since we are primarily concerned with noisy observations, we follow the Variational Bayes approach and define noise properties and assumptions on the sources by prior probability distributions. Due to properties of the Variational Bayes algorithm, the resulting inference algorithm is very similar for many different source assumptions. This allows us to build a modular toolbox, where it is easy to code different assumptions as different modules. By using different modules, we obtain different BSS algorithms. The potential of this open-source toolbox is demonstrated on separation of hyperspectral image data. The MATLAB implementation of the toolbox is available for download.
    Trvalý link: http://hdl.handle.net/11104/0249082

     
     
Počet záznamů: 1  

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