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EDGE DETECTION FOR IMAGE USING CELLULAR AUTOMATA
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Author(s):
DR . MAHESH KUMAR , MANOJ KUMAR , MAYANK SHARMA
Vol - 5, Issue- 8 ,
Page(s) : 31 - 37
(2014 )
DOI : https://doi.org/10.32804/IRJMST
Abstract
ABSTRACT: The computer science field has increased expectation on technologies of computations based on Cellular Automata (CA) theory. In the area of image signal processing, several methods of Edge Detection are proposed, such as Canny, Sobel, Zero-cross, Laplacian, Laplacian of Gaussian, Suzan, and others. Many edge detection techniques are available till now and provide good results. For more better results a new method (Improved cellular automata - ICA) has been proposed for edge detection using a CA Algorithm based on 2D cellular automata. The results of edge detection are confirmed with graphical examples. Out of 1024 rules, rule number 124 has been used in this work as it provides clear and continuous edges.
- Haralick, Robert M., Digital step edges from Zero crossing of Second Directional Derivatives, Pattern Analysis and Machine Intelligence, IEEE Transactions, PAMI 6 , Issue: 1, Page(s): 58- 68, Jan. 1984.
- Shohei Sato and Hitoshi Kanoh, “Evolutionary Design of Edge Detector Using Rule - Changing Cellular Automata”, Nature and Biologically Inspired Computing (NaBIC), Second World Congress, Page(s): 60 - 65, 15-17 Dec. 2010.
- FaselQadir, Khan K. A, “Investigations of Cellular Automata Linear Rules for Edge Detection”, I. J. Computer Network and Information Security, Vol. 3, Pages 47-53, 2012.
- ParwinderKaurDhillon, “A Novel framework to Image Edge Detection using Cellular Automata”, IJCA Special Issue on Confluence 2012 - The Next Generation Information Technology Summit Confluence (1):1-5, September 2012.
- KhadijehMirzaei, HomayunMotameni and RasulEnayatifar, “New method for edge detection and de noising via Fuzzy Cellular Automata”, International Journal of Physical Sciences Volume 6 (13), Pages 3175-3180, 4 July, 2011.
- DjemelZiou, Salvatore Tabbone, “Edge Detection Techniques - An Overview”, International Journal of Pattern Recognition and Image Analysis, 1998.
- Leung, CC; Chen, WF; Kwok, PCK; Chan, FHY, “Brain tumor boundary detection in MR image with generalized fuzzy operator”, International Conference on Image Processing Proceedings, Barcelona, Spain, Volume 2, Pages 1057-1060, 14-17, 2003.
- Leung, CC; Chen, WF; Kwok, PCK; Chan, FHY, “Brain tumor boundary detection in MR image with generalized fuzzy operator”, International Conference on Image Processing Proceedings, Barcelona, Spain, Volume 2, Pages 1057-1060, 14-17, 2003.
- Marcel Prastawa, Elizabeth Bullitt, Sean Ho, “A brain tumor segmentation framework based on outlier detection”, Medical Image Analysis, Volume 8, Pages 275–283, 2004.
- S. Murugavalli and V. Rajamani, “An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique”, Journal of Computer Science, Volume 3 (11), Pages 841-846, 2007.
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