Abstract
| Original language | English |
|---|---|
| Number of pages | 21 |
| Journal | Advances in Artificial Intelligence |
| DOIs | |
| Publication status | Published - 2010 |
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Computing with biologically inspired neural oscillators : Application to colour image segmentation. / Ghani, Arfan; Belatreche, Ammar; Maguire, Liam; McGinnity, Martin; McDaid, Liam.
In: Advances in Artificial Intelligence, 2010.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Computing with biologically inspired neural oscillators
T2 - Application to colour image segmentation
AU - Ghani, Arfan
AU - Belatreche, Ammar
AU - Maguire, Liam
AU - McGinnity, Martin
AU - McDaid, Liam
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
U2 - 10.1155/2010/405073
DO - 10.1155/2010/405073
M3 - Article
JO - Advances in Artificial Intelligence
JF - Advances in Artificial Intelligence
SN - 1687-7470
ER -