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Mendeley readers
Attention Score in Context
| Title |
Event-Based Color Segmentation With a High Dynamic Range Sensor
|
|---|---|
| Published in |
Frontiers in Neuroscience, April 2018
|
| DOI | 10.3389/fnins.2018.00135 |
| Pubmed ID | |
| Authors |
Alexandre Marcireau, Sio-Hoi Ieng, Camille Simon-Chane, Ryad B. Benosman |
| Abstract |
This paper introduces a color asynchronous neuromorphic event-based camera and a methodology to process color output from the device to perform color segmentation and tracking at the native temporal resolution of the sensor (down to one microsecond). Our color vision sensor prototype is a combination of three Asynchronous Time-based Image Sensors, sensitive to absolute color information. We devise a color processing algorithm leveraging this information. It is designed to be computationally cheap, thus showing how low level processing benefits from asynchronous acquisition and high temporal resolution data. The resulting color segmentation and tracking performance is assessed both with an indoor controlled scene and two outdoor uncontrolled scenes. The tracking's mean error to the ground truth for the objects of the outdoor scenes ranges from two to twenty pixels. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 35% |
| Student > Master | 4 | 15% |
| Researcher | 2 | 8% |
| Student > Bachelor | 2 | 8% |
| Professor > Associate Professor | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 6 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 10 | 38% |
| Computer Science | 8 | 31% |
| Neuroscience | 1 | 4% |
| Unknown | 7 | 27% |
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 16 April 2018.
All research outputs
#19,951,180
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#8,672
of 11,542 outputs
Outputs of similar age
#252,141
of 343,278 outputs
Outputs of similar age from Frontiers in Neuroscience
#206
of 249 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 18th percentile – i.e., 18% 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 343,278 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 249 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.