Name
Abstract | ||
|---|---|---|
In order to solve the ghosts, the halo effect as well as the lower signal-to-noise ratio problems more effectively, this paper presents a spatially related fuzzy ART neural network. We introduce a laterally-inspirited learning mode into the background modeling stage. At first, we combine the region-based feature with the intensity-based feature to train the spatially related fuzzy ART neural network by the laterally-inspirited learning mode. Then two spatially related fuzzy ART neural networks are configured as master-slave pattern to build the background models and detect the infrared targets alternately. Experiments have been carried out and the results demonstrate that the proposed approach is robust to noise, and can eliminate the ghosts and the halo effect effectively. It can detect the targets effectively without much more post-process. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23321-0_9 | ADVANCES IN COMPUTER SCIENCE, ENVIRONMENT, ECOINFORMATICS, AND EDUCATION, PT I |
Keywords | Field | DocType |
infrared target detection,laterally-inspirited learning mode,spatially related fuzzy ART neural network,master-slave pattern | Pattern recognition,Computer science,Fuzzy logic,Artificial intelligence,Infrared,Artificial neural network,Halo effect | Conference |
Volume | Issue | ISSN |
214 | PART 1 | 1865-0929 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| BingWen Chen | 1 | 0 | 0.34 |
| WenWei Wang | 2 | 0 | 0.34 |
| Qianqing Qin | 3 | 58 | 10.53 |