Name
Abstract | ||
|---|---|---|
This work investigates one bio-inspired collision detection system based on fly visual neural structures, in which collision alarm is triggered if an approaching object in a direct collision course appears in the field of view of a camera or a robot, together with the relevant time region of collision. One such artificial system consists of one artificial fly visual neural network model and one collision detection mechanism. The former one is a computational model to capture membrane potentials produced by neurons. The latter one takes the outputs of the former one as its inputs, and executes three detection schemes: (i) identifying when a spike takes place through the membrane potentials and one threshold scheme; (ii) deciding the motion direction of a moving object by the Reichardt detector model; and (iii) sending collision alarms and collision regions. Experimentally, relying upon a series of video image sequences with different scenes, numerical results illustrated that the artificial system with some striking characteristics is a potentially alternative tool for collision detection. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2014.11.033 | Neurocomputing |
Keywords | Field | DocType |
Fly visual neural systems,Artificial neural network,Collision detection,Collision region,Reichardt correlator | Field of view,Computer vision,Collision detection,Pattern recognition,Computer science,ALARM,Motion direction,Collision,Artificial intelligence,Artificial neural network,Robot,Detector | Journal |
Volume | ISSN | Citations |
153 | 0925-2312 | 5 |
PageRank | References | Authors |
0.47 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuhong Zhang | 1 | 186 | 16.41 |
| Shigang Yue | 2 | 402 | 56.70 |
| Guopeng Zhang | 3 | 5 | 0.47 |