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Adaptive Local Thresholding for Edge Detection

Published on September 2014 by Gaur Sanjay B.c., Jayashri Vajpai, Sandip Mehta
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National Conference on Advances in Technology and Applied Sciences
Foundation of Computer Science USA
NCATAS - Number 2
September 2014
Authors: Gaur Sanjay B.c., Jayashri Vajpai, Sandip Mehta
f046dc00-0a68-4eff-8a93-19d3f7031875

Gaur Sanjay B.c., Jayashri Vajpai, Sandip Mehta . Adaptive Local Thresholding for Edge Detection. National Conference on Advances in Technology and Applied Sciences. NCATAS, 2 (September 2014), 15-18.

@article{
author = { Gaur Sanjay B.c., Jayashri Vajpai, Sandip Mehta },
title = { Adaptive Local Thresholding for Edge Detection },
journal = { National Conference on Advances in Technology and Applied Sciences },
issue_date = { September 2014 },
volume = { NCATAS },
number = { 2 },
month = { September },
year = { 2014 },
issn = 0975-8887,
pages = { 15-18 },
numpages = 4,
url = { /proceedings/ncatas/number2/17952-1613/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Technology and Applied Sciences
%A Gaur Sanjay B.c.
%A Jayashri Vajpai
%A Sandip Mehta
%T Adaptive Local Thresholding for Edge Detection
%J National Conference on Advances in Technology and Applied Sciences
%@ 0975-8887
%V NCATAS
%N 2
%P 15-18
%D 2014
%I International Journal of Computer Applications
Abstract

The Edge detection technique plays very important role in computer vision systems. Edges define the boundaries between different regions in an image, which helps in matching the pattern, segment, and recognize an object. In many applications the overall performance of the system depends on the proper detection of the edges such as Text Detection, Shape detection, Finger Print Recognition, Pattern Recognition etc. Hence edge detection is a fundamental aspect of low-level image processing. In this paper, a local threshold based method is proposed to detect the edge of an object. Experimental results suggest that this approach is more efficient in comparison with other traditional techniques like Prewitt, Sobel and Canny Edge detector. In order to test the performance of the proposed technique, twenty five test images have been considered. The experimental results show that the proposed method is better than the conventional techniques.

References
  1. R. C. Gonzalez, and R. E. Woods, "Digital Image Processing". 3rd ed. Prentice Hall, 2013, pp. 692 -794.
  2. Basu M. , "Gaussian Based Edge Detection Methods-A Survey", IEEE Transaction on System, Man and Cybernetics, Vol. 32 No. 3. Aug. 2002, pp. 252-260.
  3. Sanjay B. C. Gaur, and Dr. Jayashri Vajpai, "Comparison of Edge Detection Techniques for Segmenting Car License Plates", Special Issue of International Journal of Computer Applications (0975 – 8887) on Electronics, Information and Communication Engineering - ICEICE No. 5, Dec 2011, pp. 8-12.
  4. M. B. Ahmad, and T. S. Choi , "Local Threshold and Boolean Function Based Edge Detection", IEEE Transactions on Consumer Electronics, Vol. 45, No 3. August 1999.
  5. S. Belongie, J. Malik, and J. Puzicha, "Shape Matching and Object Recognition using Shape Contexts", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 24, No 24, 509 - 522, April 2002.
  6. J. F. Canny, "A computational approach to edge detection", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679-697, 1986.
  7. N. Otsu, "A Threshold Selection Method from Gray-Level Histogram," IEEE trans. Sys. , Man and Cybernetics, vol. 9, pp 62-66, 1979.
  8. R. Medina-Carnicer, F. J. Madrid-Cuevas, N. L. Fernandez-Garcia and A. Carmona-Poyato, "Evaluation of Global Thresholding techniques in non-contextual edge detection", pattern recognition letters 26, pp. 1423-1434, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Edge Threshold Value Binary Image Image Processing Gray-level Image.