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A Binary Neural Shape Matcher using Johnson Counters and Chain Codes

Hodge, Victoria, O'Keefe, Simon (orcid.org/0000-0001-5957-2474) and Austin, Jim (orcid.org/0000-0001-5762-8614) (2006) A Binary Neural Shape Matcher using Johnson Counters and Chain Codes. In: BICS:. Brain Inspired Cognitive Systems 2006, 10-14 Oct 2006, Island of Lesvos. .

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In this paper, we introduce a neural network-based shape matching algorithm that uses Johnson Counter codes coupled with chain codes. Shape matching is a fundamental requirement in content-based image retrieval systems. Chain codes describe shapes using sequences of numbers. They are simple and flexible. We couple this power with the efficiency and flexibility of a binary associative-memory neural network. We focus on the implementation details of the algorithm when it is constructed using the neural network. We demonstrate how the binary associative-memory neural network can index and match chain codes where the chain code elements are represented by Johnson codes.

Item Type: Proceedings Paper
Copyright, Publisher and Additional Information: See also http://eprints.whiterose.ac.uk/5431/
Keywords: Neural,Associative Memory,Shape Matcher,BinaryEncoding
Institution: The University of York
Academic Units: The University of York > Computer Science (York)
Depositing User: Dr Victoria Hodge
Date Deposited: 08 Sep 2008 15:28
Last Modified: 17 Jun 2016 23:05
Status: Published
Refereed: No
URI: http://eprints.whiterose.ac.uk/id/eprint/4619

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