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A SOM-based Chan–Vese model for unsupervised image segmentation

Overview of attention for article published in Soft Computing, October 2015
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1 X user

Citations

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20 Mendeley
Title
A SOM-based Chan–Vese model for unsupervised image segmentation
Published in
Soft Computing, October 2015
DOI 10.1007/s00500-015-1906-z
Authors

Mohammed M. Abdelsamea, Giorgio Gnecco, Mohamed Medhat Gaber

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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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 20%
Student > Doctoral Student 3 15%
Student > Bachelor 2 10%
Student > Master 2 10%
Researcher 2 10%
Other 1 5%
Unknown 6 30%
Readers by discipline Count As %
Computer Science 6 30%
Engineering 3 15%
Physics and Astronomy 2 10%
Materials Science 1 5%
Mathematics 1 5%
Other 0 0%
Unknown 7 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 16 March 2019.
All research outputs
#19,236,357
of 23,839,820 outputs
Outputs from Soft Computing
#363
of 465 outputs
Outputs of similar age
#207,978
of 287,148 outputs
Outputs of similar age from Soft Computing
#9
of 11 outputs
Altmetric has tracked 23,839,820 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 465 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 9th percentile – i.e., 9% 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 287,148 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.