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Title: Spectral compression algorithms for the analysis of very large multivariate images

Abstract

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

Inventors:
 [1]
  1. Albuquerque, NM
Issue Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
918543
Patent Number(s):
7283684
Application Number:
10/772,548
Assignee:
Sandia Corporation (Albuquerque, NM)
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06K - RECOGNITION OF DATA
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Keenan, Michael R. Spectral compression algorithms for the analysis of very large multivariate images. United States: N. p., 2007. Web.
Keenan, Michael R. Spectral compression algorithms for the analysis of very large multivariate images. United States.
Keenan, Michael R. Tue . "Spectral compression algorithms for the analysis of very large multivariate images". United States. https://www.osti.gov/servlets/purl/918543.
@article{osti_918543,
title = {Spectral compression algorithms for the analysis of very large multivariate images},
author = {Keenan, Michael R},
abstractNote = {A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2007},
month = {10}
}

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