Name
Abstract | ||
|---|---|---|
Deep Learning is a method which is employed for change detection as well as other image processing problems. Output extracted from various layers of the deep architecture can be employed to detect changes at different scales. In this study, output extracted from the layers of deep architecture is referred as deep features and the robustness of these features on the change detection problem are evaluated experimentally. As a result, it is observed that deep features, when used alone, could detect the change in images with steady background successfully but they were sensitive to dynamic background and camera jitter. |
Year | Venue | Keywords |
|---|---|---|
2018 | Signal Processing and Communications Applications Conference | change detection,deep learning,deep features |
Field | DocType | ISSN |
Computer vision,Change detection,Pattern recognition,Computer science,Image processing,Robustness (computer science),Feature extraction,Artificial intelligence,Jitter,Deep learning,Benchmark (computing) | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ozge Oztimur Karadag | 1 | 5 | 1.78 |
| Ozlem Erdas | 2 | 0 | 0.34 |