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VHDL based Hardware Architecture of a High Performance Image Edge Detection Algorithm

by Hanene Rouabeh, Chokri Abdelmoula, Mohamed Masmoudi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 12
Year of Publication: 2014
Authors: Hanene Rouabeh, Chokri Abdelmoula, Mohamed Masmoudi
10.5120/15935-5208

Hanene Rouabeh, Chokri Abdelmoula, Mohamed Masmoudi . VHDL based Hardware Architecture of a High Performance Image Edge Detection Algorithm. International Journal of Computer Applications. 91, 12 ( April 2014), 37-43. DOI=10.5120/15935-5208

@article{ 10.5120/15935-5208,
author = { Hanene Rouabeh, Chokri Abdelmoula, Mohamed Masmoudi },
title = { VHDL based Hardware Architecture of a High Performance Image Edge Detection Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 12 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number12/15935-5208/ },
doi = { 10.5120/15935-5208 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:12:35.365910+05:30
%A Hanene Rouabeh
%A Chokri Abdelmoula
%A Mohamed Masmoudi
%T VHDL based Hardware Architecture of a High Performance Image Edge Detection Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 12
%P 37-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This article presents the software and hardware implementation of a low cost and high performance image edge detection algorithm. This algorithm will be used as part of a complete vision based driver assistance system. The main challenge consists in realizing a real-time implementation of edge detection algorithm that contributes in increasing the performance of the whole system. The software implementation of the developed algorithm using MATLAB tool is discussed in this paper, as well as the hardware architecture developed using VHDL language. Test results for both implementations were presented and compared to other edge detection operators. Computational time and other features comparison have shown the effectiveness of the proposed approach.

References
  1. Y. Ramadevi, T. Sridevi, B. Poornima and B. Kalyani 2010. Segmentation and Object Rcognition Using Edge Detection Techniques. International Journal of Computer Science & Information Technology (IJCSIT), Vol 2, No 6, December 2010
  2. Huili Zhao, Guofeng Qin and Xingjian Wang 2010. Improvement of Canny Algorithm Based on Pavement Edge Detection. 3rd International Congress on Image and Signal Processing (CISP2010)
  3. G. T. Shrivakchan and Dr. C. Chandrasekar 2012. A Comparison of various Edge Detection Techniques used in Image Processing. International Journal of Computer Science, Issues , Vol. 9, Isuue 5, No 1, September 2012
  4. Raman Maini & Dr. Himanshu Aggarwal. Study and Comparison of Various Image Edge Detection Techniques. International Journal of Image Processing (IJIP), Volume (3):Issue (1)
  5. Peer M. A. , Fasel Qadir and Khan K. A 2012. Investigations of Cellular Automata Game of Life Rules for Noise Filtering and Edge Detection. I. J. Information Engineering and Electronic Business, 2012, 2, 22-28
  6. Tapas Kumar and G. Sahoo 2010. A Novel Method of Edge Detection using Cellular Automata. International Journal of Computer Applications (0975 – 8887) Volume 9– No. 4, November 2010
  7. Farhad Soleimanian Gharehchopogh and Samira Ebrahimi 2012. A Novel Approach for Edge Detection in Images Based on Cellular Learning Automata. International Journal of Computer Vision and Image Processing, 2(4), 51-61, October-December 2012
  8. Deepak Ranjan Nayak, Sumit Kumar Sahu and Jahangir Mohammed 2013. A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model. International Journal of Computer Applications (0975 – 8887) Volume 84 – No 10, December 2013
  9. Selman Uguz, Ugur Sahin and Ferat Sahin 2013. Uniform Cellular Automata Linear Rules for Edge Detection. 2013 IEEE International Conference on Systems, Man, and Cybernetics
  10. Manoj Diwakar and Pawan Kumar Patel and Kunal Gupta 2013. Cellular Automata Based Edge-Detection for Brain Tumor. 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
  11. B. Gopi Chandra Kumar & Mohammad Hayath Rajvee 2012. Image Edge Detection Based On FPGA. International Journal of Image Processing and Vision Sciences ISSN (Print): 2278 – 1110, Volume-1, Issue-2, 2012
  12. Varun Sanduja and Rajeev Patial 2012. Sobel Edge Detection using Parallel Architecture based on FPGA. International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 3– No. 4, July 2012
  13. Chaithra. N. M. and K. V. Ramana Reddy 2013. Implementation of Canny Edge Detection Algorithm on FPGA and displaying Image through VGA Interface. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-6, August 2013
  14. Christos Gentsos, Calliope-Louisa Sotiropoulou, Spiridon Nikolaidis and Nikolaos Vassiliadis 2010. Real-Time Canny Edge Detection Parallel Implementation for FPGAs. 17th International Conference on Electronics, Circuits, and Systems (ICECS), Athens 2010 IEEE
  15. http://en. wikipedia. org/wiki/Von_Neumann_neighborhood
Index Terms

Computer Science
Information Sciences

Keywords

Image processing Edge detection VHDL implementation FPGA

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