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Efficient on-line signature recognition based on multi-section vector quantization

Overview of attention for article published in Pattern Analysis and Applications, March 2010
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About this Attention Score

  • Among the highest-scoring outputs from this source (#43 of 349)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
33 Dimensions

Readers on

mendeley
14 Mendeley
Title
Efficient on-line signature recognition based on multi-section vector quantization
Published in
Pattern Analysis and Applications, March 2010
DOI 10.1007/s10044-010-0176-8
Authors

Marcos Faundez-Zanuy, Juan Manuel Pascual-Gaspar

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 7%
Spain 1 7%
Unknown 12 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 29%
Researcher 3 21%
Lecturer > Senior Lecturer 2 14%
Student > Master 2 14%
Professor 1 7%
Other 2 14%
Readers by discipline Count As %
Computer Science 7 50%
Engineering 5 36%
Mathematics 1 7%
Unknown 1 7%
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 August 2012.
All research outputs
#7,863,403
of 23,842,189 outputs
Outputs from Pattern Analysis and Applications
#43
of 349 outputs
Outputs of similar age
#35,600
of 95,618 outputs
Outputs of similar age from Pattern Analysis and Applications
#1
of 1 outputs
Altmetric has tracked 23,842,189 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 349 research outputs from this source. They receive a mean Attention Score of 1.9. This one has gotten more attention than average, scoring higher than 61% 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 95,618 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them