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Title: Image fusion using sparse overcomplete feature dictionaries

Abstract

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

Inventors:
; ; ; ;
Issue Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1222629
Patent Number(s):
9,152,881
Application Number:
14/026,295
Assignee:
Los Alamos National Security, LLC (Los Alamos, NM)
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Patent
Resource Relation:
Patent File Date: 2013 Sep 13
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Brumby, Steven P., Bettencourt, Luis, Kenyon, Garrett T., Chartrand, Rick, and Wohlberg, Brendt. Image fusion using sparse overcomplete feature dictionaries. United States: N. p., 2015. Web.
Brumby, Steven P., Bettencourt, Luis, Kenyon, Garrett T., Chartrand, Rick, & Wohlberg, Brendt. Image fusion using sparse overcomplete feature dictionaries. United States.
Brumby, Steven P., Bettencourt, Luis, Kenyon, Garrett T., Chartrand, Rick, and Wohlberg, Brendt. Tue . "Image fusion using sparse overcomplete feature dictionaries". United States. https://www.osti.gov/servlets/purl/1222629.
@article{osti_1222629,
title = {Image fusion using sparse overcomplete feature dictionaries},
author = {Brumby, Steven P. and Bettencourt, Luis and Kenyon, Garrett T. and Chartrand, Rick and Wohlberg, Brendt},
abstractNote = {Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {10}
}

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