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