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.
|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:||01 Jan 2017 01:02|