Image fusion using sparse overcomplete feature dictionaries

Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

Title: Image fusion using sparse overcomplete feature dictionaries

Patent · Tue Oct 06 00:00:00 EDT 2015

OSTI ID:1222629

Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC52-06NA25396

Assignee:: Los Alamos National Security, LLC (Los Alamos, NM)

Patent Number(s):: 9,152,881

Application Number:: 14/026,295

OSTI ID:: 1222629

Resource Relation:: Patent File Date: 2013 Sep 13

Country of Publication:: United States

Language:: English

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