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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A neural computational model for bottom-up attention with invariant and overcomplete representation
|
|---|---|
| Published in |
BMC Neuroscience, November 2012
|
| DOI | 10.1186/1471-2202-13-145 |
| Pubmed ID | |
| Authors |
Zou Qi, Zhao Songnian, Wang Zhe, Huang Yaping |
| Abstract |
An important problem in selective attention is determining the ways the primary visual cortex contributes to the encoding of bottom-up saliency and the types of neural computation that are effective to model this process. To address this problem, we constructed a two-layered network that satisfies the neurobiological constraints of the primary visual cortex to detect salient objects. We carried out experiments on both synthetic images and natural images to explore the influences of different factors, such as network structure, the size of each layer, the type of suppression and the combination strategy, on saliency detection performance. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 6% |
| Unknown | 16 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 4 | 24% |
| Researcher | 4 | 24% |
| Professor | 2 | 12% |
| Professor > Associate Professor | 2 | 12% |
| Student > Bachelor | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 4 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 18% |
| Engineering | 3 | 18% |
| Psychology | 2 | 12% |
| Computer Science | 2 | 12% |
| Neuroscience | 2 | 12% |
| Other | 2 | 12% |
| Unknown | 3 | 18% |
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 29 November 2012.
All research outputs
#20,174,175
of 22,687,320 outputs
Outputs from BMC Neuroscience
#1,051
of 1,240 outputs
Outputs of similar age
#245,628
of 277,026 outputs
Outputs of similar age from BMC Neuroscience
#23
of 32 outputs
Altmetric has tracked 22,687,320 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,240 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 1st percentile – i.e., 1% 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 277,026 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.