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A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model

by Deepak Ranjan Nayak, Sumit Kumar Sahu, Jahangir Mohammed
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 10
Year of Publication: 2013
Authors: Deepak Ranjan Nayak, Sumit Kumar Sahu, Jahangir Mohammed
10.5120/14614-2869

Deepak Ranjan Nayak, Sumit Kumar Sahu, Jahangir Mohammed . A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model. International Journal of Computer Applications. 84, 10 ( December 2013), 27-33. DOI=10.5120/14614-2869

@article{ 10.5120/14614-2869,
author = { Deepak Ranjan Nayak, Sumit Kumar Sahu, Jahangir Mohammed },
title = { A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 10 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 27-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number10/14614-2869/ },
doi = { 10.5120/14614-2869 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:00:34.603870+05:30
%A Deepak Ranjan Nayak
%A Sumit Kumar Sahu
%A Jahangir Mohammed
%T A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 10
%P 27-33
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cellular Automata (CA) are common and most simple models of parallel computations. Edge detection is one of the crucial task in image processing, especially in processing biological and medical images. CA can be successfully applied in image processing. This paper presents a new method for edge detection of binary images based on two dimensional twenty five neighborhood cellular automata. The method considers only linear rules of CA for extraction of edges under null boundary condition. The performance of this approach is compared with some existing edge detection techniques. This comparison shows that the proposed method to be very promising for edge detection of binary images. All the algorithms and results used in this paper are prepared in MATLAB.

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Index Terms

Computer Science
Information Sciences

Keywords

CA TFNCA Edge Detection Neighborhood Linear Rule Null- Boundary.

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