Region growing with pulse-coupled neural networks: an alternative to seeded region growing

Authors:
R. D. Stewart;I. Fermin;M. Opper
Affiliations:
Sch. of Eng. & Appl. Sci., Aston Univ., Birmingham, UK;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2002

Citing 0
Cited 4

A new approach for color image edge detection using improved PCNN

SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Parallel fine spatial resolution satellite sensor image segmentation based on an improved Pulse-Coupled Neural Network

International Journal of Remote Sensing
Review article: Review of pulse-coupled neural networks

Image and Vision Computing
An Iterative Thresholding Segmentation Model Using a Modified Pulse Coupled Neural Network

Neural Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

The seeded region growing (SRG) algorithm is a fast robust parameter-free method for segmenting intensity images given initial seed locations for each region. The requirement of predetermined seeds means that the model cannot operate fully autonomously. In this paper, we demonstrate a novel region growing variant of the pulse-coupled neural network (PCNN), which offers comparable performance to the SRG and is able to generate seed locations internally, opening the way to fully autonomous operation.