Yue, Shigang and Rind, F. C. (2006) Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement. IEEE transactions on neural networks, 17 (3). pp. 705-716. ISSN 1045-9227
Full text not available from this repository.Abstract
The lobula giant movement detector (LGMD) is an identified neuron in the locust brain that responds most strongly to the images of an approaching object such as a predator. Its computational model can cope with unpredictable environments without using specific object recognition algorithms. In this paper, an LGMD-based neural network is proposed with a new feature enhancement mechanism to enhance the expanded edges of colliding objects via grouped excitation for collision detection with complex backgrounds. The isolated excitation caused by background detail will be filtered out by the new mechanism. Offline tests demonstrated the advantages of the presented LGMD-based neural network in complex backgrounds. Real time robotics experiments using the LGMD-based neural network as the only sensory system showed that the system worked reliably in a wide range of conditions; in particular, the robot was able to navigate in arenas with structured surrounds and complex backgrounds.
| Item Type: | Article |
|---|---|
| Additional Information: | The lobula giant movement detector (LGMD) is an identified neuron in the locust brain that responds most strongly to the images of an approaching object such as a predator. Its computational model can cope with unpredictable environments without using specific object recognition algorithms. In this paper, an LGMD-based neural network is proposed with a new feature enhancement mechanism to enhance the expanded edges of colliding objects via grouped excitation for collision detection with complex backgrounds. The isolated excitation caused by background detail will be filtered out by the new mechanism. Offline tests demonstrated the advantages of the presented LGMD-based neural network in complex backgrounds. Real time robotics experiments using the LGMD-based neural network as the only sensory system showed that the system worked reliably in a wide range of conditions; in particular, the robot was able to navigate in arenas with structured surrounds and complex backgrounds. |
| Keywords: | Lobula giant movement detector (LGMD), Robotics, Neural network |
| Subjects: | G Mathematical and Computer Sciences > G400 Computer Science H Engineering > H671 Robotics |
| Divisions: | College of Sciences > Faculty of Science > Lincoln School of Computer Science |
| Depositing User: | Jill Partridge |
| Date Deposited: | 24 Sep 2007 |
| Last Modified: | 18 Jul 2011 16:17 |
| URI: | http://eprints.lincoln.ac.uk/id/eprint/1219 |
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