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Design of Steerable Filters for Feature Detection Using Canny-Like Criteria

Overview of attention for article published in IEEE Transactions on Software Engineering, August 2004
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

twitter
1 X user
patent
12 patents

Readers on

mendeley
233 Mendeley
citeulike
4 CiteULike
connotea
1 Connotea
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Article details
Title
Design of Steerable Filters for Feature Detection Using Canny-Like Criteria
Published in
IEEE Transactions on Software Engineering, August 2004
DOI 10.1109/tpami.2004.44
Pubmed ID
Authors

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Timeline Attention over time Attention Score history
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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 233 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 5 2%
Switzerland 4 2%
Italy 3 1%
India 2 <1%
United Kingdom 2 <1%
Spain 2 <1%
Germany 2 <1%
Malaysia 1 <1%
Japan 1 <1%
Other 6 3%
Unknown 205 88%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 78 33%
Researcher 49 21%
Student > Master 36 15%
Professor 12 5%
Professor > Associate Professor 12 5%
Other 27 12%
Unknown 19 8%
Readers by discipline
Readers by discipline Count As %
Computer Science 100 43%
Engineering 58 25%
Agricultural and Biological Sciences 15 6%
Biochemistry, Genetics and Molecular Biology 9 4%
Physics and Astronomy 6 3%
Other 20 9%
Unknown 25 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 04 April 2023.
All research outputs
#4,809,496
of 33,973,571 outputs
Outputs from IEEE Transactions on Software Engineering
#973
of 7,791 outputs
Outputs of similar age
#10,959
of 86,458 outputs
Outputs of similar age from IEEE Transactions on Software Engineering
#8
of 41 outputs
Altmetric has tracked 33,973,571 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,791 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one has done well, scoring higher than 87% of its peers.
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 86,458 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.