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Proceedings Paper

Independent component analysis (ICA) using wavelet subband orthogonality
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Paper Abstract

There are two kinds of RRP: (1) invertible ones, such as global Fourier transform (FT), local wavelet transform (WT), and adaptive wavelet transform (AWT); and (2) non-invertible ones, e.g. ICA including the global principle component analysis (PCA). The invertible FT and WT can be related to the non-invertible ICA when the continuous transforms are approximate din discrete matrix-vector operations. The landmark accomplishment of ICA is to obtain, by unsupervised learning algorithm, the edge-map as image feature ayields, shown by Helsinki researchers using fourth order statistics of nyields -- Kurosis K(uyields), and derived from information- theoretical first principle is augmented by the orthogonality property of the DWT subband used necessarily for usual image compression. If we take the advantage of the subband decorrelation, we have potentially an efficient utilization of a pari of communication channels if we could send several more mixed subband images through the pair of channels.

Paper Details

Date Published: 26 March 1998
PDF: 14 pages
Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); doi: 10.1117/12.304868
Show Author Affiliations
Harold H. Szu, Univ. of Southwestern Louisiana (United States)
Charles C. Hsu, George Washington Univ. (United States)
Takeshi Yamakawa, Kyushu Institute of Technology (Japan) and Fuzzy Logic System Institute (Japan)

Published in SPIE Proceedings Vol. 3391:
Wavelet Applications V
Harold H. Szu, Editor(s)

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