A biologically inspired computational vision front-end based on a self-organised pseudo-randomly tessellated artificial retina

Balasuriya, S. and Siebert, J.P. (2005) A biologically inspired computational vision front-end based on a self-organised pseudo-randomly tessellated artificial retina. In: IEEE International Joint Conference on Neural Networks (IJCNN '05), Montréal, 31 July-4 August 2005, pp. 3069-3074. ISBN 9780780390485 (doi:10.1109/IJCNN.2005.1556415)

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Publisher's URL: http://dx.doi.org/10.1109/IJCNN.2005.1556415

Abstract

This paper considers the construction of a biologically inspired front-end for computer vision based on an artificial retina pyramid with a self-organised pseudo-randomly tessellated receptive field tessellation. The organisation of photoreceptors and receptive fields in biological retinae locally resembles a hexagonal mosaic, whereas globally these are organised with a very densely tessellated central foveal region which seamlessly merges into an increasingly sparsely tessellated periphery. In contrast, conventional computer vision approaches use a rectilinear sampling tessellation which samples the whole field of view with uniform density. Scale-space interest points which are suitable for higher level attention and reasoning tasks are efficiently extracted by our vision front-end by performing hierarchical feature extraction on the pseudo-randomly spaced visual information. All operations were conducted on a geometrically irregular foveated representation (data structure for visual information) which is radically different to the uniform rectilinear arrays used in conventional computer vision.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Siebert, Dr Jan
Authors: Balasuriya, S., and Siebert, J.P.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Publisher:Institute of Electrical and Electronics Engineers
ISBN:9780780390485
Copyright Holders:Copyright © 2005 Institute of Electrical and Electronics Engineers
First Published:First published in Proceedings 2005 IEEE International Joint Conference on Neural Networks. IJCNN '05.
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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