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Edge Detection Using Cellular Automata

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Cellular Automata in Image Processing and Geometry

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 10))

Abstract

Edge detection has been a long standing topic in image processing, generating hundreds of papers and algorithms over the last 50 years. Likewise, the topic has had a fascination for researchers in cellular automata, who have also developed a variety of solutions, particularly over the last ten years. CA based edge detection has potential benefits over traditional approaches since it is computationally efficient, and can be tuned for specific applications by appropriate selection or learning of rules. This chapter will provide an overview of CA based edge detection techniques, and assess their relative merits and weaknesses. Several CA based edge detection methods are implemented and tested to enable an initial comparison between competing approaches.

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Authors and Affiliations

  1. School of Computer Science & Informatics, Cardiff University, Cardiff, CF24 3AA, UK

    Paul L. Rosin & Xianfang Sun

Authors
  1. Paul L. Rosin
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  2. Xianfang Sun
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Correspondence to Paul L. Rosin .

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Editors and Affiliations

  1. School of Computer Science & Informatics, Cardiff University, Cardiff, United Kingdom

    Paul Rosin

  2. Unconventional Computing Centre, University of the West of England, Bristol, United Kingdom

    Andrew Adamatzky

  3. School of Computer Science & Informatics, Cardiff University, Cardiff, United Kingdom

    Xianfang Sun

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Rosin, P.L., Sun, X. (2014). Edge Detection Using Cellular Automata. In: Rosin, P., Adamatzky, A., Sun, X. (eds) Cellular Automata in Image Processing and Geometry. Emergence, Complexity and Computation, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-06431-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-06431-4_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06430-7

  • Online ISBN: 978-3-319-06431-4

  • eBook Packages: EngineeringEngineering (R0)

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