Table of Contents
Advances in Artificial Intelligence
Volume 2010, Article ID 405073, 21 pages
Research Article

Computing with Biologically Inspired Neural Oscillators: Application to Colour Image Segmentation

Intelligent Systems Research Centre, School of Computing and Intelligent Systems, Faculty of Computing Engineering, University of Ulster, Magee Campus, Northland Road, Northern Ireland, Derry BT48 7JL, UK

Received 2 December 2009; Accepted 26 February 2010

Academic Editor: Abbes Amira

Copyright © 2010 Ammar Belatreche et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper investigates the computing capabilities and potential applications of neural oscillators, a biologically inspired neural model, to grey scale and colour image segmentation, an important task in image understanding and object recognition. A proposed neural system that exploits the synergy between neural oscillators and Kohonen self-organising maps (SOMs) is presented. It consists of a two-dimensional grid of neural oscillators which are locally connected through excitatory connections and globally connected to a common inhibitor. Each neuron is mapped to a pixel of the input image and existing objects, represented by homogenous areas, are temporally segmented through synchronisation of the activity of neural oscillators that are mapped to pixels of the same object. Self-organising maps form the basis of a colour reduction system whose output is fed to a 2D grid of neural oscillators for temporal correlation-based object segmentation. Both chromatic and local spatial features are used. The system is simulated in Matlab and its demonstration on real world colour images shows promising results and the emergence of a new bioinspired approach for colour image segmentation. The paper concludes with a discussion of the performance of the proposed system and its comparison with traditional image segmentation approaches.