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Image processing using 3-state cellular automata

Rosin, Paul L. 2010. Image processing using 3-state cellular automata. Computer Vision and Image Understanding 114 (7) , pp. 790-802. 10.1016/j.cviu.2010.02.005

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Abstract

This paper describes the application of cellular automata (CA) to various image processing tasks such as denoising and feature detection. Whereas our previous work mainly dealt with binary images, the current work operates on intensity images. The increased number of cell states (i.e. pixel intensities) leads to a vast increase in the number of possible rules. Therefore, a reduced intensity representation is used, leading to a three state CA that is more practical. In addition, a modified sequential floating forward search mechanism is developed in order to speed up the selection of good rule sets in the CA training stage. Results are compared with our previous method based on threshold decomposition, and are found to be generally superior. The results demonstrate that the CA is capable of being trained to perform many different tasks, and that the quality of these results is in many cases comparable or better than established specialised algorithms.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: Cellular automata; Threshold decomposition; State reduction; Feature selection; Denoising.
Additional Information: Pdf uploaded in accordance with the publisher’s policy at http://www.sherpa.ac.uk/romeo/issn/1077-3142/ (accessed 27/10/2014)
Publisher: Elsevier
ISSN: 1077-3142
Date of First Compliant Deposit: 30 March 2016
Last Modified: 05 Jun 2017 02:08
URI: http://orca.cf.ac.uk/id/eprint/27594

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