A Binary Neural Shape Matcher using Johnson Counters and Chain Codes

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationBICS
Publication statusPublished - 10 Mar 2006
EventBrain Inspired Cognitive Systems 2006 - Island of Lesvos, Greece
Duration: 10 Oct 200614 Oct 2006


ConferenceBrain Inspired Cognitive Systems 2006
CityIsland of Lesvos

Bibliographical note

See also http://eprints.whiterose.ac.uk/5431/


  • Neural
  • Associative Memory
  • Shape Matcher
  • BinaryEncoding

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