Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Performance Evaluation of Image Edge Detection Techniques
Maher I. Rajab
Pages - 170 - 185     |    Revised - 31-10-2016     |    Published - 01-12-2016
Volume - 10   Issue - 5    |    Publication Date - December 2016  Table of Contents
Image Edge Detection, Gaussian Noise, Gaussian Smoothing.
The success of an image recognition procedure is related to the quality of the edges marked. The aim of this research is to investigate and evaluate edge detection techniques when applied to noisy images at different scales. Sobel, Prewitt, and Canny edge detection algorithms are evaluated using artificially generated images and comparison criteria: edge quality (EQ) and map quality (MQ). The results demonstrated that the use of these criteria can be utilized as an aid for further analysis and arbitration to find the best edge detector for a given image.
1 Google Scholar 
2 refSeek 
3 Scribd 
4 SlideShare 
5 PdfSR 
1 M. Ramalho. "Edge detection using neural network arbitration." Ph.D. Thesis, University of Nottingham, UK, 1996.
2 M. Garcý´a-Silvente, J.A. Garcý´a, J. Fdez-Valdivia, A. Garrido. "A new edge detector integrating scale-spectrum information." Image and Vision Computing, vol. 15, pp. 913-923, 1997.
3 M. Juneja, P. Sandhu. "Performance Evaluation of Edge Detection Techniques for Images in Spatial Domain." International Journal of Computer Theory and Engineering, vol. 1, no. 5, 2009.
4 I. Singh, A. Oberoi, M. Oberoi. "Performance Evaluation of Edge Detection Techniques for Square, Hexagon and Enhanced Hexagonal Pixel Images, International Journal of Computer Applications, vol. 121, no.12, 2015.
5 M. Sonka, V. Hlavac, R. Boyle. Image Processing, Analysis, and Machine Vision. Toronto, CA: Thomson, 2008, pp. 132-146.
6 D. Marr, E. Hildreth. "Theory of Edge Detection." Royal Society of London B, vol. 207, no. 1167, pp. 187-217, Feb. 29, 1980.
7 R.C. Gonzalez, P. Wintz. Digital Signal Processing. Addison-Wesley Publishing Company, 1993.
8 E. Trucco, A. Verri. Introductory Techniques for 3-D Computer Vision. Prentice Hall, 1998.
9 W.H. Press, B.P. Flannery, S.A. Teukolsky, W.T. Vetterling. Numerical Recipes in Pascal, Cambridge University Press, 1989.
10 M. Ramalho, and K.M. Curtis. "Neural Network Arbitration of Edge Maps," in Transputer Applications and Systems '94; A. Gloria., M. Jane., D. Marini. (Eds), IOS Press, Netherlands, 1994.
11 J. Canny. "A computational approach to edge detection." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, 1986.
12 W.K. Pratt. Digital Image Processing, New York: Wiley InterSciencce, 1991.
13 P.K. Shaoo, S. Soltani, A.K.C. Wong. "A survey of thresholding techniques." Computer Vision, Graphics, and Image Processing, vol. 41, pp. 233-260, 1998.
14 S. Lakshmi, V. Sankaranarayanan (2010), "A study of Edge Detection Techniques for Segmentation Computing Approaches.", IJCA Special Issue on Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications CASCT. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=
15 C.S. Panda, S. Patnaik (2009), "Filtering Corrupted Image and Edge Detection in Restored Grayscale Image Using Derivative Filters.", International Journal of Image Processing (IJIP). Vol. 3 (3), 105-119. Available: http://www.cscjournals.org/manuscript/Journals/IJIP/Volume3/Issue3/IJIP-28.pdf
Associate Professor Maher I. Rajab
College of Computer & Information Systems Computer Engineering Department Umm Al-Qura University Makkah 21955, Saudi Arabia - Saudi Arabia