DocumentCode :
2400460
Title :
Feature extraction approach to blind source separation
Author :
Lin, Juan K. ; Grier, David G. ; Cowan, Jack D.
Author_Institution :
Dept. of Phys., Chicago Univ., IL, USA
fYear :
1997
fDate :
24-26 Sep 1997
Firstpage :
398
Lastpage :
405
Abstract :
Local independent component analysis is formulated as a task involving the extraction of local geometric structure in the joint distribution. Because the geometrical structure of statistical independence is not well captured by statistical descriptions such as moments and cumulants, we use feature detection tools from image analysis to locate the local independent component coordinate system. The resulting approach to source separation can be implemented in real time using conventional image analysis hardware. The generality of this approach is demonstrated by blind source separation of multi-modal sources, and the pseudo-separation of three sources from two mixtures
Keywords :
Hough transforms; feature extraction; geometry; probability; blind source separation; feature detection; feature extraction approach; local geometric structure; local independent component analysis; multi-modal sources; pseudo-separation; statistical independence; Blind source separation; Computer vision; Equations; Feature extraction; Histograms; Image analysis; Independent component analysis; Partitioning algorithms; Physics; Source separation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
Conference_Location :
Amelia Island, FL
ISSN :
1089-3555
Print_ISBN :
0-7803-4256-9
Type :
conf
DOI :
10.1109/NNSP.1997.622421
Filename :
622421
Link To Document :
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