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Edge Detection Using Modified Directional Coefficient Mask in AWGN

  • Chang-Young Lee
  • Nam-Ho Kim
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 235)

Abstract

Image segmentation is required for the analysis of images and edge is one of the essential elements of image segmentation. Edge contains image information and it is applied in various fields of image processing. Typical methods of edge detection include Sobel, Prewitt and Roberts method and such methods have the advantage of simple realization and fast processing speed as they process images with mask in spatial area. However, when images are degraded by the addition of AWGN, an error of detecting edge in noise areas occur. Therefore, in this paper a new edge detection algorithm with excellent edge detection characteristics which effectively removes AWGN is proposed.

Keywords

Edge detection Noise denoising Prewitt mask 

References

  1. 1.
    Ma S, Zheng G, Jin L, Han S, Zhang R (2010) Directional multiscale edge detection using the contourlet transform: advanced computer control, ICACC 2010, vol 2, pp 58–62Google Scholar
  2. 2.
    Govindarajan B, Panetta K, Agaian S (2008) Progressive edge detection on multi-bit images using polynomial-based binarization: proceedings of the ICMLC 2008, pp 3714–3719Google Scholar
  3. 3.
    Wu J, Yin Z, Xiong Y (2007) The fast multilevel fuzzy edge detection of blurry images: signal processing letters. IEEE 14(5):344–347Google Scholar
  4. 4.
    Liu J, Jiang Y-D, Zhao Y-X, Zhu J, Wang Y (2009) Crack edge detection of coal CT images based on LS-SVM: machine learning and cybernetics, 2009 international conference on, vol 4, pp 2398–2403Google Scholar
  5. 5.
    Chang BK, Kim TY, Lee YK (2012) A novel approach to general linearly constrained adaptive arrays. J Inf Commun Convergence Eng (JICCE) 10(2):108–116Google Scholar
  6. 6.
    Tao J, Klette R (2012) Tracking of 2D or 3D irregular movement by a family of unscented kalman filters. J Inf Commun Convergence Eng (JICCE) 10(3):307–314Google Scholar
  7. 7.
    Yinyu G, Kim NH (2012) A study on wavelet-based image denoising using a modified adaptive thresholding method. J Inf Commun Convergence Eng (JICCE) 10(1):45–52Google Scholar
  8. 8.
    Gonzalez RC, Woods RE, Eddins SL (2003) Digital image processing using MATLAB, Prentice-Hall, Upper Saddle RiverGoogle Scholar
  9. 9.
    Gonzalez RC, Woods RE (2007) Digital image processing, 3rd edn. Prentice-Hall, Upper Saddle RiverGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Control and Instrumentation EngineeringPukyong National UniversityBusanKorea

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