Open Access Open Access  Restricted Access Subscription or Fee Access

A Directional Edge Detector Operator to Detect All Type of Edges of Image

Priya Singh, Preeti Rai


This work proposes a new directional edge detector operator to detect all type of edges of the image. Initially this thesis provides an introduction to digital image processing followed by a review of various edge detection methods. In the research part authors initially did few experiments on the famous Lena image then by these experiments authors come to two conclusions. These conclusions are basic pillars of the proposed work. (a) Image contains smoothly varying area separated by edge information. (b) Edge pixels take intensities comparatively smaller or larger than the surrounding pixels. Based on this conclusion authors have design the simplest method that works on image patches and find edge pixels. . This operator works on information aligned in four different directions. With the help of a mean filter or a given threshold the proposed method is able to detect strong edges of the images.


Directional Edge Detector Operator

Full Text:



D. Ziou, S. Tabbone, -“Edge Detection Techniques – An Overview” [accessed Oct 18, 2016].

L., Tony (2001), "Edge detection", in Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4

T. Lindeberg (1998) "Edge detection and ridge detection with automatic scale selection", International Journal of Computer Vision, 30, 2, pages 117–154.

W. Zhang and F. Bergholm (1997) "Multi-scale blur estimation and edge type classification for scene analysis", International Journal of Computer Vision, vol 24, issue 3, Pages: 219–250.

D. Ziou and S. Tabbone (1998) "Edge detection techniques: An overview", International Journal of Pattern Recognition and Image Analysis, 8(4):537–559, 1998

J. M. Park and Y. Lu (2008) "Edge detection in grayscale, color, and range images", in B. W. Wah (editor) Encyclopedia of Computer Science and Engineering, doi 10.1002/9780470050118.ecse603

Sobel, I., Feldman, G., "A 3x3 Isotropic Gradient Operator for Image Processing", presented at the Stanford Artificial Intelligence Project (SAIL) in, Available from: [accessed Oct 18, 2016].

J.M.S. Prewitt "Object Enhancement and Extraction" in "Image processing and Psychopictorics", Academic Press,1970.

Kirsch, R. (1971). "Computer determination of the constituent structure of biological images". Computers and Biomedical Research. 4: 315–328. doi:10.1016/0010-4809(71)90034-6.

J. Canny (1986) "A computational approach to edge detection", IEEE Trans. Pattern Analysis and Machine Intelligence, vol 8, pages 679-714.R. Kasturi and R. C. Jain, eds. “Computer vision: principles.” IEEE Computer Society Press, Los Alamitos, CA, 1991.

A. L. Yuille and T. A. Poggio, “Scaling theorems for zero-crossings,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, pp. 15–25, Jan. 1986.

B. G. Schunck, “Edge detection with Gaussian filters at multiple scales,” in Proc. IEEE Comp. Soc. Work. Comp. Vis., 1987, pp. 208–210.

Mitra Basu, “Gaussian-Based Edge-Detection Methods A Review,” IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications And Reviews, Vol. 32, No. 3, August 2002, pp. 252-260.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 629–639, July 1990.

D. Heric, and D. Zazula, “Combined edge detection using wavelet transform and signal registration,” Elsevier Journal of Image and Vision Computing 25 (2007) 652–662

J. Wu, Z. Yin, and Y. Xiong, “The Fast Multilevel Fuzzy Edge Detection of Blurry Images,” IEEE Signal Processing Letters, Vol. 14, No. 5, pp 344-347, 2007.

S. Lu, Z. Wang, and J. Shen, “Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement,” Elsevier Journal of Pattern Detection 36 (2003) 2395-2409.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.