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Disambiguating Multi–Modal Scene Representations Using Perceptual Grouping Constraints

Overview of attention for article published in PLOS ONE, June 2010
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  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Average Attention Score compared to outputs of the same age and source

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41 Mendeley
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Article details
Title
Disambiguating Multi–Modal Scene Representations Using Perceptual Grouping Constraints
Published in
PLOS ONE, June 2010
DOI 10.1371/journal.pone.0010663
Pubmed ID
Authors
Abstract

In its early stages, the visual system suffers from a lot of ambiguity and noise that severely limits the performance of early vision algorithms. This article presents feedback mechanisms between early visual processes, such as perceptual grouping, stereopsis and depth reconstruction, that allow the system to reduce this ambiguity and improve early representation of visual information. In the first part, the article proposes a local perceptual grouping algorithm that - in addition to commonly used geometric information - makes use of a novel multi-modal measure between local edge/line features. The grouping information is then used to: 1) disambiguate stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. The integration of mutual feedback between early vision processes is shown to reduce considerably ambiguity and noise without the need for global constraints.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 2%
Turkey 1 2%
Portugal 1 2%
Luxembourg 1 2%
United Kingdom 1 2%
Belgium 1 2%
Austria 1 2%
Unknown 34 83%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 9 22%
Researcher 8 20%
Lecturer 6 15%
Student > Master 5 12%
Student > Postgraduate 5 12%
Other 6 15%
Unknown 2 5%
Readers by discipline
Readers by discipline Count As %
Computer Science 8 20%
Engineering 8 20%
Psychology 7 17%
Chemistry 6 15%
Environmental Science 2 5%
Other 7 17%
Unknown 3 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 June 2010.
All research outputs
#9,598,533
of 34,361,833 outputs
Outputs from PLOS ONE
#81,401
of 224,520 outputs
Outputs of similar age
#50,698
of 151,464 outputs
Outputs of similar age from PLOS ONE
#442
of 901 outputs
Altmetric has tracked 34,361,833 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 224,520 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.2. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 151,464 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 901 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.