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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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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

Nicolas Pugeault, Florentin Wörgötter, Norbert Krüger

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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 3%
Turkey 1 3%
Austria 1 3%
United Kingdom 1 3%
Belgium 1 3%
United States 1 3%
Luxembourg 1 3%
Unknown 33 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Researcher 8 20%
Lecturer 6 15%
Student > Postgraduate 5 13%
Student > Master 4 10%
Other 6 15%
Unknown 2 5%
Readers by discipline Count As %
Engineering 8 20%
Computer Science 8 20%
Psychology 7 18%
Chemistry 6 15%
Environmental Science 2 5%
Other 6 15%
Unknown 3 8%