0041074 20240111140639.220060913235959.9 5 s. CD-ROM cav_un_epca*00767641-5FlorenceEURASIP2006 independent component analysis blind source separation cav_un_auth*0101212 Tichavský Petr UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0108100 Koldovský Zbyněk UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0213972 Doron E. IL cav_un_auth*0213973 Yeredor A. IL cav_un_auth*0214236 Herrero G. G. FI datovy soubor 373kB 12B 1M0572 GA MŠk cav_un_auth*0001814 CEZ:AV0Z10750506 The aim of this paper is to combine the strengths of two recently proposed Blind Source Separation~(BSS) algorithms. The first algorithm, abbreviated as EFICA, is a sophisticated variant of the well-known Independent Component Analysis~(ICA) algorithm FastICA. EFICA is based on minimizing the statistical dependencies between the instantaneous (marginal) distributions of the estimated source signals and therefore disregards any possible time structure of the sources. The second algorithm, WASOBI, is a weight-adjusted variant of SOBI, a popular BSS algorithm that uses only the time structure of the source signals to achieve the separation. The separation accuracy of EFICA and WASOBI can be assessed using the estimated source signals alone, therefore allowing us to choose the most appropriate of the two in every scenario. Here, two different EFICA-WASOBI combination approaches are proposed and their performance assessed using images and simulated signals. Zamerem clanku je kombinace silnych stranek dvou nedavno navrzenych algoritmu pro slepou separaci signalu. Prvni algoritmus, znamy pod akronymem EFICA, je zalozen na minimalizaci statisticke nezavislosti rozlozeni jednotlivych zdroju v danem okamziku, ktery ignoruje moznou casovou strukturu signalu. Druhym signalem je algoritmus WASOBI, ktery vyuziva prave jen ruzne casove kovariancni struktury jednotlivych zdroju. Jelikoz presnost separace obou algoritmu lze odhadovat z danych dat, je mozne najit optimalni kombinaci techto algoritmu adaptivne. Dve mozne kombinace oubou algoritmu jsou studovany v simulacich na separaci linearni smesi ctyr obrazku. cav_un_auth*0216569 European Signal Processing Conference. EUSIPCO /14./ Florence 04.09.2006-08.09.2006 IT 2007 BB http://hdl.handle.net/11104/0134660 2006 datovy soubor 373kB cav_un_epca*0076764 Proceedings of 14th European Signal Processing Conference. EUSIPCO 2006 1 5 Florence EURASIP 2006