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Title: Spatial compression algorithm for the analysis of very large multivariate images

Abstract

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms 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:
942284
Patent Number(s):
7400772
Application Number:
10/772,805
Assignee:
Sandia Corporation (Albuquerque, NM)
Patent Classifications (CPCs):
H - ELECTRICITY H04 - ELECTRIC COMMUNICATION TECHNIQUE H04N - PICTORIAL COMMUNICATION, e.g. TELEVISION
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. Spatial compression algorithm for the analysis of very large multivariate images. United States: N. p., 2008. Web.
Keenan, Michael R. Spatial compression algorithm for the analysis of very large multivariate images. United States.
Keenan, Michael R. Tue . "Spatial compression algorithm for the analysis of very large multivariate images". United States. https://www.osti.gov/servlets/purl/942284.
@article{osti_942284,
title = {Spatial compression algorithm for the analysis of very large multivariate images},
author = {Keenan, Michael R},
abstractNote = {A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2008},
month = {7}
}

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  • Keenan, Michael R.; Timlin, Jerilyn A.; Van Benthem, Mark H.
  • International Symposium on Optical Science and Technology, SPIE Proceedings
  • https://doi.org/10.1117/12.451662

Impact of Hierarchical Memory Systems On Linear Algebra Algorithm Design
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