Name
Abstract | ||
|---|---|---|
This paper presents a technique that combines the occurrence of certain events, as observed by different sensors, in order to detect and classify objects. This technique explores the extent of dependence between features being observed by the sensors, and generates more informed probability distributions over the events. Provided some additional information about the features of the object, this fusion technique can outperform other existing decision level fusion approaches that may not take into account the relationship between different features. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/EUSIPCO.2018.8553412 | European Signal Processing Conference |
Keywords | Field | DocType |
Sensor Fusion,Decision Level Fusion,Event based Classification,Coupling | Data mining,Coupling,Decision level,Computer science,Fusion,Sensor fusion,Probability distribution | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siddharth Roheda | 1 | 0 | 1.35 |
| Hamid Krim | 2 | 520 | 59.69 |
| Zhi-Quan Luo | 3 | 7506 | 598.19 |
| Tianfu Wu | 4 | 331 | 26.72 |