Name
Title | ||
|---|---|---|
Extracting statistically significant behaviour from fish tracking data with and without large dataset cleaning. |
Abstract | ||
|---|---|---|
Extracting a statistically significant result from video of natural phenomenon can be difficult for two reasons: (i) there can be considerable natural variation in the observed behaviour and (ii) computer vision algorithms applied to natural phenomena may not perform correctly on a significant number of samples. This study presents one approach to clean a large noisy visual tracking dataset to all... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1049/iet-cvi.2016.0462 | IET Computer Vision |
Keywords | Field | DocType |
aquaculture,computer vision,data handling,estimation theory,image denoising,learning (artificial intelligence),pattern classification | Data mining,Pattern recognition,Outlier,Data binning,Eye tracking,Artificial intelligence,Deep learning,Cluster analysis,Mathematics,Trajectory,Statistical hypothesis testing,Underwater | Journal |
Volume | Issue | ISSN |
12 | 2 | 1751-9632 |
Citations | PageRank | References |
0 | 0.34 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cigdem Beyan | 1 | 86 | 10.67 |
| Vasiliki-Maria Katsageorgiou | 2 | 4 | 2.74 |
| Robert B. Fisher | 3 | 1295 | 125.38 |