Binary Image Thinning Using Autowaves Generated by PCNN
Neural Processing Letters
Letters: A class of binary images thinning using two PCNNs
Neurocomputing
Feature Extraction using Unit-linking Pulse Coupled Neural Network and its Applications
Neural Processing Letters
Classification Using Multi-valued Pulse Coupled Neural Network
Neural Information Processing
Review article: Review of pulse-coupled neural networks
Image and Vision Computing
An Automatic Parameter Adjustment Method of Pulse Coupled Neural Network for Image Segmentation
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
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Humans do not stare at an image, they foveate. Their eyes move about points of interest within the image collecting clues as to the content of the image. Object shape is one of the driving forces of foveation. These foveation points are generally corners and, to a lesser extent, the edges. The pulse-coupled neural network (PCNN) has the inherent ability to segment an image. The corners and edges of the PCNN segments are similar to the foveation points. Thus, it is a natural extension of PCNN technology to use it as a foveation engine. The paper presents theory and examples of foveation through the use of a PCNN, and also demonstrates that it can be quite useful in image recognition