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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F15%3A00447094" target="_blank" >RIV/67985556:_____/15:00447094 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/EUSIPCO.2015.7362599" target="_blank" >http://dx.doi.org/10.1109/EUSIPCO.2015.7362599</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EUSIPCO.2015.7362599" target="_blank" >10.1109/EUSIPCO.2015.7362599</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 theVariational 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 theVariational 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.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-29225S" target="_blank" >GA13-29225S: Slepá dekonvoluce obrazu v limitních podmínkách</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015)

ISBN

978-0-9928626-4-0

ISSN

2076-1465

e-ISSN

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Počet stran výsledku

5

Strana od-do

1336-1340

Název nakladatele

IEEE Computer Society

Místo vydání

Piscataway

Místo konání akce

Nice

Datum konání akce

31. 8. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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