Linked Life Data

19757933

Source:http://linkedlifedata.com/resource/pubmed/id/19757933

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2009-9-17
pubmed:abstractText
Visual information is difficult to search and interpret when the density of the displayed information is high or the layout is chaotic. Visual information that exhibits such properties is generally referred to as being "cluttered." Clutter should be avoided in information visualizations and interface design in general because it can severely degrade task performance. Although previous studies have identified computable correlates of clutter (such as local feature variance and edge density), understanding of why humans perceive some scenes as being more cluttered than others remains limited. Here, we explore an account of clutter that is inspired by findings from visual perception studies. Specifically, we test the hypothesis that the so-called "crowding" phenomenon is an important constituent of clutter. We constructed an algorithm to predict visual clutter in arbitrary images by estimating the perceptual impairment due to crowding. After verifying that this model can reproduce crowding data we tested whether it can also predict clutter. We found that its predictions correlate well with both subjective clutter assessments and search performance in cluttered scenes. These results suggest that crowding and clutter may indeed be closely related concepts and suggest avenues for further research.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1534-7362
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
24.1-11
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
A crowding model of visual clutter.
pubmed:affiliation
Institute of Mathematics and Computing Science and School of Behavioral and Cognitive Neurosciences, University of Groningen, 9713 AW Groningen, The Netherlands. r.van.den.berg@rug.nl
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't