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On the Inverse Problem of Binocular 3D Motion Perception

Overview of attention for article published in PLoS Computational Biology, November 2010
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Title
On the Inverse Problem of Binocular 3D Motion Perception
Published in
PLoS Computational Biology, November 2010
DOI 10.1371/journal.pcbi.1000999
Pubmed ID
Authors

Martin Lages, Suzanne Heron

Abstract

It is shown that existing processing schemes of 3D motion perception such as interocular velocity difference, changing disparity over time, as well as joint encoding of motion and disparity, do not offer a general solution to the inverse optics problem of local binocular 3D motion. Instead we suggest that local velocity constraints in combination with binocular disparity and other depth cues provide a more flexible framework for the solution of the inverse problem. In the context of the aperture problem we derive predictions from two plausible default strategies: (1) the vector normal prefers slow motion in 3D whereas (2) the cyclopean average is based on slow motion in 2D. Predicting perceived motion directions for ambiguous line motion provides an opportunity to distinguish between these strategies of 3D motion processing. Our theoretical results suggest that velocity constraints and disparity from feature tracking are needed to solve the inverse problem of 3D motion perception. It seems plausible that motion and disparity input is processed in parallel and integrated late in the visual processing hierarchy.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 9%
Germany 1 2%
France 1 2%
Belgium 1 2%
Italy 1 2%
Japan 1 2%
Luxembourg 1 2%
Unknown 43 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 33%
Student > Ph. D. Student 13 24%
Student > Master 6 11%
Professor 3 6%
Student > Postgraduate 3 6%
Other 10 19%
Unknown 1 2%
Readers by discipline Count As %
Psychology 18 33%
Agricultural and Biological Sciences 11 20%
Computer Science 7 13%
Medicine and Dentistry 4 7%
Neuroscience 4 7%
Other 8 15%
Unknown 2 4%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 30 January 2019.
All research outputs
#8,534,976
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#5,637
of 8,960 outputs
Outputs of similar age
#56,896
of 188,265 outputs
Outputs of similar age from PLoS Computational Biology
#28
of 51 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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 188,265 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 51 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.