Pulse-coupled neural networks (PCNN) have an inherent ability to process the signals associated with the digital visual images because it is inspired from the neuronal activity in the primary visual area, V1, of the neocortex. Many examples of processing mechanisms are provided to make it clear and concise.
| code | demo |
|---|---|
figure_2.m |
The PCNN 1-D demo |
figure_4.m |
The PCNN wave demo |
figure_6.m |
The Image Histogram demo |
figure_7.m |
The Image Segmentation demo |
figure_8.m |
The Feature Extraction demo |
figure_9.m |
The Image Enhancement demo |
Algorithm_6.m |
image de-noising / restoration demo |
We appreciate it if you cite the following paper:
@Article{zhan2017computational,
author = {Zhan, K and Shi, J and Wang, H and Xie, Y and Li, Q},
title = {Computational Mechanisms of Pulse-Coupled Neural Networks: A Comprehensive Review},
journal = {Archives of Computational Methods in Engineering},
year = {2017},
volume = {24},
number = {3},
pages = {573--588},
doi = {10.1007/s11831-016-9182-3},
publisher = {Springer}
}
http://www.escience.cn/people/kzhan
If you have any questions on PCNN, feel free to contact me. (Email: ice.echo#gmail.com)