A binary neural shape matcher using Johnson Counters and chain codes

Authors:
Victoria J. Hodge;Simon O'Keefe;Jim Austin
Affiliations:
Advanced Computer Architecture Group, Department of Computer Science, University of York, York YO10 5DD, UK;Department of Computer Science, University of York, York YO10 5DD, UK;Department of Computer Science, University of York, York YO10 5DD, UK
Venue:
Neurocomputing
Year:
2009

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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.