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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Automatic landmark annotation and dense correspondence registration for 3D human facial images
|
|---|---|
| Published in |
BMC Bioinformatics, July 2013
|
| DOI | 10.1186/1471-2105-14-232 |
| Pubmed ID | |
| Authors |
Jianya Guo, Xi Mei, Kun Tang |
| Abstract |
Traditional anthropometric studies of human face rely on manual measurements of simple features, which are labor intensive and lack of full comprehensive inference. Dense surface registration of three-dimensional (3D) human facial images holds great potential for high throughput quantitative analyses of complex facial traits. However there is a lack of automatic high density registration method for 3D faical images. Furthermore, current approaches of landmark recognition require further improvement in accuracy to support anthropometric applications. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 18% |
| Hong Kong | 1 | 9% |
| United Kingdom | 1 | 9% |
| Unknown | 7 | 64% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 91% |
| Practitioners (doctors, other healthcare professionals) | 1 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Indonesia | 1 | <1% |
| India | 1 | <1% |
| Turkey | 1 | <1% |
| Italy | 1 | <1% |
| Unknown | 115 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 24% |
| Student > Master | 22 | 18% |
| Researcher | 20 | 17% |
| Student > Bachelor | 12 | 10% |
| Other | 5 | 4% |
| Other | 16 | 13% |
| Unknown | 15 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 32 | 27% |
| Medicine and Dentistry | 20 | 17% |
| Agricultural and Biological Sciences | 18 | 15% |
| Engineering | 12 | 10% |
| Biochemistry, Genetics and Molecular Biology | 7 | 6% |
| Other | 12 | 10% |
| Unknown | 18 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 10 February 2022.
All research outputs
#2,042,962
of 25,358,192 outputs
Outputs from BMC Bioinformatics
#447
of 7,677 outputs
Outputs of similar age
#16,919
of 205,239 outputs
Outputs of similar age from BMC Bioinformatics
#12
of 87 outputs
Altmetric has tracked 25,358,192 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,677 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 94% 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 205,239 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 87 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.