Detect the Edges in Satellite Image Using CED Algorithm

Authors

  • N. Siranjeevi Research Scholar, Periyar University, Salem
  • BM Alaudeen Department of Computer Science, Manonmaniam Sundaranar University, Tirunelveli, India

DOI:

https://doi.org/10.37591/rrdms.v2i3.541

Keywords:

Edges, satellite image, canny edge detection method, canny–deriche detector

Abstract

Edge detection is that the name for a collection of mathematical strategies that aim at distinguishing points in a very digital image at that the image brightness changes sharply or, a lot of formally, has discontinuities. The points at that image brightness changes sharply square measure generally organized into a collection of flexuous line segments termed edges. Constant drawback of finding discontinuities in 1D signal is understood as step and also the drawback of finding signal discontinuities over time is understood as modification detection. Edge detection could be a elementary tool in image process, machine vision and pc vision, notably within the areas of feature detection and have extraction [1].

Cite this Article
Siranjeevi N, Alaudeen BM. Detect the edges in satellite image using ced algorithm. Research & Reviews: Discrete Mathematical Structures. 2015; 2(3): 30–35p. 

References

Umbaugh Scott E. Digital image processing and analysis : human and computer vision applications with CVIPtools. 2010. 2. Barrow HG, Tenenbaum JM. Interpreting line drawings as three-dimensional surfaces. Artificial Intelligence. 1981. 3. Lindeberg Tony. Edge detection. in Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer. 2010. 4. Lindeberg T. Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision. 1998; 2: 117–156p. 5. Zhang W, Bergholm F. Multi-scale blur estimation and edge type classification for scene analysis. 1987. International Journal of Computer Vision. 1997; 24(3): 219–250p.

Ziou D, Tabbone S. Edge Detection Techniques: An Overview. 1989. 7. Park JM, Lu Y. Edge detection in grayscale, color, and range images. In B. W. Wah (editor) Encyclopedia of Computer Science and Engineering. 2008. 8. Canny J. A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence. 1986. 9. Haralick R. Digital step edges from zero crossing of second directional derivatives. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1984. 10. Kimmel R Bruckstein AM. On regularized Laplacian zero crossings and other optimal edge integrators. International Journal of Computer Vision. 2003; 53(3): 225–243. 11. Shapiro LG, Stockman GC. Computer Vision. London: Prentice Hall; 2001. 12. Deriche R. Using Canny's criteria to derive an optimal edge detector recursively implemented. Int. J. Computer Vision. 1987. 13. Lindeberg T. Discrete derivative approximations with scale-space properties: A basis for low-level feature extraction. Journal of Mathematical Imaging and Vision. 1993, 3(4): 349–376p.

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Published

2015-12-17

Issue

Vol. 2 No. 3 (2015)

Section

Research Articles

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