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Detecting parametric objects in large scenes by Monte Carlo sampling

Overview of attention for article published in International Journal of Computer Vision, July 2013
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

patent
3 patents

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
29 Mendeley
Title
Detecting parametric objects in large scenes by Monte Carlo sampling
Published in
International Journal of Computer Vision, July 2013
DOI 10.1007/s11263-013-0641-0
Authors

Yannick Verdié, Florent Lafarge

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 7%
Japan 1 3%
Unknown 26 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 41%
Researcher 5 17%
Student > Master 3 10%
Student > Bachelor 2 7%
Other 2 7%
Other 3 10%
Unknown 2 7%
Readers by discipline Count As %
Computer Science 18 62%
Engineering 7 24%
Earth and Planetary Sciences 2 7%
Mathematics 1 3%
Unknown 1 3%
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 06 March 2018.
All research outputs
#7,567,797
of 23,081,466 outputs
Outputs from International Journal of Computer Vision
#398
of 1,170 outputs
Outputs of similar age
#66,640
of 199,051 outputs
Outputs of similar age from International Journal of Computer Vision
#7
of 8 outputs
Altmetric has tracked 23,081,466 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,170 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 32nd percentile – i.e., 32% 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 199,051 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 50% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.