Application of biologically inspired neural oscillators to colour image segmentation

Abstract

This study investigates the computing capabilities and potential applications of neural oscillators to grey scale and colour image segmentation, an important task in image understanding and object recognition. A proposed neural system that combines the synergy between neural oscillators and Kohonen self-organising maps (SOM) is presented. Colour image segmentation is achieved through temporal synchronisation of neural oscillators that are mapped to pixels of the same object. Neurons are organised in a two-dimensional grid and are locally connected through excitatory connections and globally connected to a common inhibitor. Self-organising maps form the basis of a colour reduction system whose output is fed to a 2D grid of neural oscillators such as each neuron is mapped to a pixel of the input image. Both chromatic and local spatial features are used. The proposed system is simulated in Matlab and its demonstration on real world colour images shows promising results and the emergence of a new bio-inspired approach for colour image segmentation.