Name
Abstract | ||
|---|---|---|
For an object classification system, the most critical obstacles toward real-world applications are often caused by large intra-class variability, arising from different lightings, occlusion, and corruption, in limited sample sets. Most methods in the literature would fail when the training samples are heavily occluded, corrupted or have significant illumination or viewpoint variations. Besides, m... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2017.2766446 | IEEE Transactions on Image Processing |
Keywords | DocType | Volume |
Dictionaries,Training,Noise measurement,Lighting | Journal | 27 |
Issue | ISSN | Citations |
2 | 1057-7149 | 4 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Homa Foroughi | 1 | 31 | 3.65 |
| Ray Nilanjan | 2 | 541 | 55.39 |
| Hong Zhang | 3 | 582 | 74.33 |