Name
Abstract | ||
|---|---|---|
Stereo matching is a well researched area using visibleband color cameras. Thermal images are typically lower resolution, have less texture, and are noisier compared to their visible-band counterparts and are more challenging for stereo matching algorithms. Previous benchmarks for stereo matching either focus entirely on visible-band cameras or contain only a single thermal camera. We present the Color And Thermal Stereo (CATS) benchmark, a dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth ( |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CVPR.2017.22 | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
Keywords | Field | DocType |
CATS,visible-band color cameras,visible-band counterparts,stereo matching algorithms,visible-band cameras,single thermal camera,cross-modality image pairs,high accuracy ground truth,thermally interesting objects,cross-modality disparity maps,camera alignment procedure,semiautomatic LiDAR,color-color maps,color and thermal stereo benchmark,thermal images | Stereo matching,Computer vision,Stereo camera,Thermal,Computer science,Lidar,Ground truth,Artificial intelligence,Benchmark (computing),Calibration,Computer stereo vision | Conference |
Volume | Issue | ISSN |
2017 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-5386-0458-8 | 9 | 0.48 |
References | Authors | |
22 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wayne Treible | 1 | 9 | 3.52 |
| Philip Saponaro | 2 | 17 | 3.97 |
| Scott Sorensen | 3 | 21 | 6.17 |
| Abhishek Kolagunda | 4 | 24 | 6.28 |
| michael a oneal | 5 | 10 | 0.92 |
| Brian Phelan | 6 | 9 | 0.48 |
| Kelly Sherbondy | 7 | 18 | 4.03 |
| Chandra Kambhamettu | 8 | 858 | 80.83 |