Name
Abstract | ||
|---|---|---|
Traditional image features are not able to effectively represent railway fasteners under varied illumination and conditions. We propose the line local binary pattern encoding method that considers the relationship between the center point and its upper and lower neighborhoods. The method can effectively represent the key components of fasteners. In comparison with several state-of-the-art methods,... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LSP.2018.2825947 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Fasteners,Rail transportation,Feature extraction,Lighting,Rails,Gray-scale,Fans | Fastener,Data set,Pattern recognition,Feature (computer vision),Local binary patterns,Artificial intelligence,Mathematics,Encoding (memory) | Journal |
Volume | Issue | ISSN |
25 | 6 | 1070-9908 |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Fan | 1 | 11 | 4.96 |
| P. C. Cosman | 2 | 256 | 18.49 |
| Yun Hou | 3 | 3 | 0.74 |
| Bailin Li | 4 | 3 | 1.08 |