Edge detection using cellular automata

Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 and Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766 2014. Edge detection using cellular automata. Rosin, Paul L., Adamatzky, Andrew and Sun, Xianfang, eds. Cellular Automata in Image Processing and Geometry, Emergence, Complexity and Computation, vol. 10. Springer, pp. 85-103. (10.1007/978-3-319-06431-4_5)

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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.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer
ISBN:	9783319064307
ISSN:	21947287
Date of First Compliant Deposit:	23 December 2016
Date of Acceptance:	1 January 2014
Last Modified:	02 Nov 2022 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/97081

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