DIFFERENCE OF GAUSSIANS TYPE NEURAL IMAGE FILTERING WITH SPIKING NEURONS

Sylvain Chevallier, Sonia Dahdouh

Abstract

This contribution describes a bio-inspired image filtering method using spiking neurons. Bio-inspired approaches aim at identifying key properties of biological systems or models and proposing efficient implementations of these properties. The neural image filtering method takes advantage of the temporal integration behavior of spiking neurons. Two experimental validations are conducted to demonstrate the interests of this neural-based method. The first set of experiments compares the noise resistance of a convolutional difference of Gaussians (DOG) filtering method and the neuronal DOG method on a synthetic image. The other experiment explores the edges recovery ability on a natural image. The results show that the neural-based DOG filtering method is more resistant to noise and provides a better edge preservation than classical DOG filtering method.

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Paper Citation


in Harvard Style

Chevallier S. and Dahdouh S. (2009). DIFFERENCE OF GAUSSIANS TYPE NEURAL IMAGE FILTERING WITH SPIKING NEURONS . In Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009) ISBN 978-989-674-014-6, pages 467-472. DOI: 10.5220/0002322304670472


in Bibtex Style

@conference{icnc09,
author={Sylvain Chevallier and Sonia Dahdouh},
title={DIFFERENCE OF GAUSSIANS TYPE NEURAL IMAGE FILTERING WITH SPIKING NEURONS},
booktitle={Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009)},
year={2009},
pages={467-472},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002322304670472},
isbn={978-989-674-014-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009)
TI - DIFFERENCE OF GAUSSIANS TYPE NEURAL IMAGE FILTERING WITH SPIKING NEURONS
SN - 978-989-674-014-6
AU - Chevallier S.
AU - Dahdouh S.
PY - 2009
SP - 467
EP - 472
DO - 10.5220/0002322304670472