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Title: Image fusion using sparse overcomplete feature dictionaries

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