A Binary Neural Shape Matcher using Johnson Counters and Chain Codes

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Title of host publicationBICS
DatePublished - 10 Mar 2006
Original languageEnglish

Abstract

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.

Bibliographical note

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

    Research areas

  • Neural, Associative Memory, Shape Matcher, BinaryEncoding

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