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BIOLOGICALLY INSPIRED EDGE DETECTION USING SPIKING NEURAL NETWORKS AND HEXAGONAL IMAGES



Author(s)

Marine Clogenson

- CPE Lyon, France

Dermot Kerr

- University of Ulster, United Kingdom

Martin McGinnity

- University of Ulster, United Kingdom

Sonya Coleman

- University of Ulster, United Kingdom

Qingxiang Wu

- University of Ulster, United Kingdom
Keywords Spiking neural network, Edge detection, Multi-scale hexagonal receptive fields.
Abstract Inspired by the structure and behaviour of the human visual system, we extend existing work using spiking neural networks for edge detection with a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation before being processed with a spiking neural network with scalable hexagonally shaped receptive fields. The performance is compared with different sized receptive fields implemented on standard rectangular images. Results illustrate that using hexagonal-shaped receptive fields provides improved performance over a range of scales compared with standard rectangular shaped receptive fields and images.