Name
Title | ||
|---|---|---|
Biologically Inspired Edge Detection using Spiking Neural Networks and Hexagonal Images. |
Abstract | ||
|---|---|---|
Inspired by the structure and behaviour of the human visual system, we extend existing work using spiking neural networks for edge detection with a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation before being processed with a spiking neural network with scalable hexagonally shaped receptive fields. The performance is compared with different sized receptive fields implemented on standard rectangular images. Results illustrate that using hexagonal-shaped receptive fields provides improved performance over a range of scales compared with standard rectangular shaped receptive fields and images. |
Year | Venue | Keywords |
|---|---|---|
2011 | NCTA 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NEURAL COMPUTATION THEORY AND APPLICATIONS | Spiking neural network,Edge detection,Multi-scale hexagonal receptive fields |
Field | DocType | Citations |
Receptive field,Human visual system model,Edge detection,Computer science,Digital image,Artificial intelligence,Spiking neural network,Computer vision,Pattern recognition,Hexagonal crystal system,Pixel,Machine learning,Scalability | Conference | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marine Clogenson | 1 | 13 | 1.68 |
| Dermot Kerr | 2 | 50 | 13.84 |
| T. Martin Mcginnity | 3 | 518 | 66.30 |
| Sonya Coleman | 4 | 216 | 36.84 |
| Qingxiang Wu | 5 | 101 | 9.98 |