Linked Life Data

20884173

Source:http://linkedlifedata.com/resource/pubmed/id/20884173

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-12-1
pubmed:abstractText
Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real gray-level and color images and the simulation results show the effectiveness of the system.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1879-2782
pubmed:author
pubmed:copyrightInfo
Copyright © 2010 Elsevier Ltd. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
24
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
54-64
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Selecting salient objects in real scenes: an oscillatory correlation model.
pubmed:affiliation
Department of Science and Technology, Federal University of São Paulo (Unifesp), São José dos Campos, SP, Brazil. quiles@unifesp.br
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't