| Title |
Advances on image interpolation based on ant colony algorithm
|
|---|---|
| Published in |
SpringerPlus, April 2016
|
| DOI | 10.1186/s40064-016-2040-9 |
| Pubmed ID | |
| Authors |
Olivier Rukundo, Hanqiang Cao |
| Abstract |
This paper presents an advance on image interpolation based on ant colony algorithm (AACA) for high resolution image scaling. The difference between the proposed algorithm and the previously proposed optimization of bilinear interpolation based on ant colony algorithm (OBACA) is that AACA uses global weighting, whereas OBACA uses local weighting scheme. The strength of the proposed global weighting of AACA algorithm depends on employing solely the pheromone matrix information present on any group of four adjacent pixels to decide which case deserves a maximum global weight value or not. Experimental results are further provided to show the higher performance of the proposed AACA algorithm with reference to the algorithms mentioned in this paper. |
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 % |
|---|---|---|
| Unknown | 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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 36% |
| Lecturer | 1 | 9% |
| Other | 1 | 9% |
| Librarian | 1 | 9% |
| Student > Bachelor | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 2 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 36% |
| Engineering | 3 | 27% |
| Agricultural and Biological Sciences | 1 | 9% |
| Business, Management and Accounting | 1 | 9% |
| Unknown | 2 | 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 01 April 2016.
All research outputs
#15,365,885
of 22,858,915 outputs
Outputs from SpringerPlus
#932
of 1,849 outputs
Outputs of similar age
#180,070
of 300,229 outputs
Outputs of similar age from SpringerPlus
#96
of 182 outputs
Altmetric has tracked 22,858,915 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,849 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 35th percentile – i.e., 35% 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 300,229 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 182 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.