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Invariant Recognition of Visual Objects: Some Emerging Computational Principles

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2012
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 X user
googleplus
1 Google+ user

Readers on

mendeley
40 Mendeley
citeulike
1 CiteULike
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Title
Invariant Recognition of Visual Objects: Some Emerging Computational Principles
Published in
Frontiers in Computational Neuroscience, January 2012
DOI 10.3389/fncom.2012.00060
Pubmed ID
Authors

Evgeniy Bart, Jay Hegdé

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
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 %
United States 3 8%
Germany 1 3%
Unknown 36 90%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 28%
Researcher 9 23%
Student > Ph. D. Student 8 20%
Professor 3 8%
Other 1 3%
Other 2 5%
Unknown 6 15%
Readers by discipline Count As %
Computer Science 11 28%
Psychology 8 20%
Neuroscience 5 13%
Agricultural and Biological Sciences 4 10%
Linguistics 2 5%
Other 4 10%
Unknown 6 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 May 2013.
All research outputs
#14,732,278
of 22,675,759 outputs
Outputs from Frontiers in Computational Neuroscience
#762
of 1,336 outputs
Outputs of similar age
#159,230
of 244,088 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#36
of 69 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,336 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 36th percentile – i.e., 36% 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 244,088 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.