Variational Blind Source Separation Toolbox and its Application to Hyperspectral Image Data
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Variational Blind Source Separation Toolbox and its Application to Hyperspectral Image Data
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/GA13-29225S" target="_blank" >GA13-29225S: Image Blind Deconvolution in Demanding Conditions</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015)
ISBN
978-0-9928626-4-0
ISSN
2076-1465
e-ISSN
—
Number of pages
5
Pages from-to
1336-1340
Publisher name
IEEE Computer Society
Place of publication
Piscataway
Event location
Nice
Event date
Aug 31, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—