Name
Title | ||
|---|---|---|
A biologically inspired spiking model of visual processing for image feature detection |
Abstract | ||
|---|---|---|
To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2015.01.011 | Neurocomputing |
Keywords | Field | DocType |
image feature detection,spiking neural networks | Receptive field,Feature detection (computer vision),Computer science,Digital image,Artificial intelligence,Spiking neural network,Computer vision,Visual processing,Pattern recognition,Feature (computer vision),Feature extraction,Real image,Machine learning | Journal |
Volume | Issue | ISSN |
158 | C | 0925-2312 |
Citations | PageRank | References |
9 | 0.54 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dermot Kerr | 1 | 50 | 13.84 |
| T. Martin Mcginnity | 2 | 518 | 66.30 |
| Sonya Coleman | 3 | 216 | 36.84 |
| Marine Clogenson | 4 | 13 | 1.68 |