Research Article
Comparative Analysis of Traditional Methods with Game of Life in Cellular Automata for Edge Detection
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 118 - Issue 17 |
| Published: May 2015 |
| Authors: Sukriti Mehrotra, Sohini Das, Shilpi Shikha, Chinmoy Kar |
10.5120/20837-3550
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Sukriti Mehrotra, Sohini Das, Shilpi Shikha, Chinmoy Kar . Comparative Analysis of Traditional Methods with Game of Life in Cellular Automata for Edge Detection. International Journal of Computer Applications. 118, 17 (May 2015), 18-24. DOI=10.5120/20837-3550
@article{ 10.5120/20837-3550,
author = { Sukriti Mehrotra,Sohini Das,Shilpi Shikha,Chinmoy Kar },
title = { Comparative Analysis of Traditional Methods with Game of Life in Cellular Automata for Edge Detection },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 118 },
number = { 17 },
pages = { 18-24 },
doi = { 10.5120/20837-3550 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Sukriti Mehrotra
%A Sohini Das
%A Shilpi Shikha
%A Chinmoy Kar
%T Comparative Analysis of Traditional Methods with Game of Life in Cellular Automata for Edge Detection%T
%J International Journal of Computer Applications
%V 118
%N 17
%P 18-24
%R 10.5120/20837-3550
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Edge detection is one of the most commonly used operations in image analysis, which is recently receiving much attention. There are various existing traditional edge detectors which are used to estimate edge magnitude and orientation. The concept of Cellular Automata (CA) also has been used for edge detection making the technique more efficient and effective. This paper includes a comparative study of the traditional techniques with the Cellular Automata (CA) technique which is based on rules of Conway's Game of Life (GoL). The comparative analysis is carried out based on a particular set of assumed parameters which are suitable enough to conclude the best performing edge detector
References
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