White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

A binary neural shape matcher using Johnson Counters and chain codes

Hodge, V.J., O'Keefe, S. and Austin, J. (2009) A binary neural shape matcher using Johnson Counters and chain codes. Neurocomputing. pp. 693-703. ISSN 0925-2312

Full text available as:
[img] Text (HodgeOKeefeAustin_FinalSubmitted.pdf)
HodgeOKeefeAustin_FinalSubmitted.pdf

Download (393Kb)

Abstract

Images may be matched as whole images or using shape matching. Shape matching requires: identifying edges in the image, finding shapes using the edges and representing the shapes using a suitable metric. A Laplacian edge detector is simple and efficient for identifying the edges of shapes. Chain codes describe shapes using sequences of numbers and may be matched simply, accurately and flexibly. We couple this with the efficiency of a binary associative-memory neural network. We demonstrate shape matching using the neural network to index and match chain codes where the chain code elements are represented by Johnson codes.

Item Type: Article
Copyright, Publisher and Additional Information: Copyright © 2008 Elsevier B.V. This is an author produced version of a paper published in 'Neurocomputing'. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Neural, Associative memory, Binary encoding, Shape matcher, Figurative image retrieval, Artificial Intelligence, Computer Science Applications, Cognitive Neuroscience
Institution: The University of York
Academic Units: The University of York > Computer Science (York)
Depositing User: Dr Victoria Hodge
Date Deposited: 30 Jan 2009 10:21
Last Modified: 09 Jul 2014 11:05
Published Version: http://dx.doi.org/10.1016/j.neucom.2008.07.013
Status: Published
Refereed: Yes
Related URLs:
URI: http://eprints.whiterose.ac.uk/id/eprint/5431

Actions (login required)

View Item View Item