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Proceedings Paper

Spectral decomposition by wavelet approximation to the Karhunen-Loeve transform
Author(s): Ian R. Greenshields; Joel A. Rosiene
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Paper Abstract

There are a wide variety of reasons to link spectroscopy with time-series analysis1 and hence with the theory of random processes. While it remains true that the dominant harmonic analysis of spectroscopy is distributional Fourier theory, there are nonetheless good rationales for exploring other decompositions such as the one explored here (the canonical decomposition). One reason which motivates us the the necessity of discriminating tissue types by color spectrum. rfo do this efficiently, one seeks to mininiize the number of characteristic discriininants which describe the spectrum. By treating the spectrum as an instance of a random process, it is well-known that the eigenvalues ) of its canonical decomposition (or Karhunen-Loeve decomposition) , when ordered in decreasing order () )'2 )3 . . .) will typically decay very rapidly, and it follows that usually only the first few (ordered) eigenvalues are needed to characterize the spectrum.

Paper Details

Date Published: 14 August 1992
PDF: 5 pages
Proc. SPIE 1644, Ophthalmic Technologies II, (14 August 1992); doi: 10.1117/12.137432
Show Author Affiliations
Ian R. Greenshields, Univ. of Connecticut (United States)
Joel A. Rosiene, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 1644:
Ophthalmic Technologies II
Jean-Marie Parel, Editor(s)

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