Name
Abstract | ||
|---|---|---|
Synchronization of coupled oscillators is observed at multiple levels of neural systems, and has been shown to play an important function in visual perception. We propose a computing system based on locally coupled oscillator networks for image segmentation. The system can serve as the preprocessing front-end of an image processing pipeline where the common frequencies of clusters of oscillators reflect the segmentation results. To demonstrate the feasibility of our design, the system is simulated and tested on a human face image dataset and its performance is compared with traditional intensity threshold based algorithms. Our system shows both better performance and higher noise tolerance than traditional methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CNNA.2014.6888657 | Cellular Nanoscale Networks and their Applications |
Keywords | Field | DocType |
neural systems,neural oscillator network model,local coupled oscillator network synchronization,noise tolerance,image segmentation,image processing pipeline,coupled circuits,frequency locking,computing system,visual perception,oscillators,neural nets,couplings,mathematical model,face,chemicals,synchronization | Oscillation,Computer science,Image processing,Image segmentation,Artificial intelligence,Visual perception,Computer vision,Synchronization,Pattern recognition,Segmentation,Preprocessor,Machine learning,Computing systems | Journal |
Volume | ISSN | Citations |
abs/1405.2362 | 2165-0179 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Fang | 1 | 0 | 0.34 |
| Matthew J. Cotter | 2 | 3 | 0.83 |
| Donald M. Chiarulli | 3 | 213 | 24.91 |
| Steven P. Levitan | 4 | 288 | 60.98 |