Edge Detection Based on Spiking Neural Network Model

Authors:
Qingxiang Wu;Martin Mcginnity;Liam Maguire;Ammar Belatreche;Brendan Glackin
Affiliations:
School of Computing and Intelligent Systems, University of Ulster at Magee Campus, Derry, BT48 7JL, Northern Ireland, UK;School of Computing and Intelligent Systems, University of Ulster at Magee Campus, Derry, BT48 7JL, Northern Ireland, UK;School of Computing and Intelligent Systems, University of Ulster at Magee Campus, Derry, BT48 7JL, Northern Ireland, UK;School of Computing and Intelligent Systems, University of Ulster at Magee Campus, Derry, BT48 7JL, Northern Ireland, UK;School of Computing and Intelligent Systems, University of Ulster at Magee Campus, Derry, BT48 7JL, Northern Ireland, UK
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

Citing 7
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Abstract

Inspired by the behaviour of biological receptive fields and the human visual system, a network model based on spiking neurons is proposed to detect edges in a visual image. The structure and the properties of the network are detailed in this paper. Simulation results show that the network based on spiking neurons is able to perform edge detection within a time interval of 100 ms. This processing time is consistent with the human visual system. A firing rate map recorded in the simulation is comparable to Sobel and Canny edge graphics. In addition, the network can separate different edges using synapse plasticity, and the network provides an attention mechanism in which edges in an attention area can be enhanced.