CFP last date
15 March 2024
Reseach Article

Edge Detection using Directional Filter Bank

by S. Anand, T. Thivya, S. Jeeva
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 4
Year of Publication: 2012
Authors: S. Anand, T. Thivya, S. Jeeva
10.5120/ijais12-450162

S. Anand, T. Thivya, S. Jeeva . Edge Detection using Directional Filter Bank. International Journal of Applied Information Systems. 1, 4 ( February 2012), 21-27. DOI=10.5120/ijais12-450162

@article{ 10.5120/ijais12-450162,
author = { S. Anand, T. Thivya, S. Jeeva },
title = { Edge Detection using Directional Filter Bank },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2012 },
volume = { 1 },
number = { 4 },
month = { February },
year = { 2012 },
issn = { 2249-0868 },
pages = { 21-27 },
numpages = {9},
url = { https://www.ijais.org/archives/volume1/number4/82-0162/ },
doi = { 10.5120/ijais12-450162 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:41:22.951876+05:30
%A S. Anand
%A T. Thivya
%A S. Jeeva
%T Edge Detection using Directional Filter Bank
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 1
%N 4
%P 21-27
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Finding edges in digital images is an essential and important task in many imaging applications. This paper describes an edge detection using multi scale Directional Filter Bank (DFB). This method tries to solve two difficulties that edge finding algorithms must face: limited directions and combining the detected edges at different scales. The directional responses of DFB that represent the edge information can be used for edge detection. The steerable and scaled DFB has been presented to obtain directional along with scaled information. From the DFB based image decomposition, the scaled information is combined by scale multiplication. Finally, to evaluate edge detector performance the sensitivity, specificity, accuracy and Figure of merit ‘F’ parameters are used to compare with classical methods and the proposed approach provides better performance.

References
  1. Mallat. S and Hwang. W L. 1992. Singularity Detection and Processing with Wavelets. IEEE Trans. Info. Theory, 617-643.
  2. Mallat S. and Zong S. 1992. Characterization of Signals from Multiscale Edges. IEEE Trans. PAMI, 14, 710-732.
  3. Xu Y. Weaver J., Healy D., and Lu J. 1994. Wavelet Transform Domain Filters: A Spatially Selective Noise Filtration Technique. IEEE Trans. Image Proc. 3, 747-758.
  4. Rosenfeld A. and Thurston M. 1970. Edge and Curve Detection for Visual Scene Analysis. IEEE Trans. Computer, 20, 562-569.
  5. Sadler B M., Pham T. and Sadler L C. 1998. Optimal and wavelet based Shock Wave Detection and Estimation, J Acoust. Soc. Am., 104(2), 955-963.
  6. Ziou D. and Tabbone S. 1993. A Multiscale Edge Detector. Pattern Recognition, 26(9), 1305-1314.
  7. Park D J., Nam K N., and Park R H. 1995. Multiresolution Edge Detection Techniques. Pattern Recognition, 28(1), 211-219.
  8. Shun-feng Ma ; Geng-feng Zheng ; Long-xu Jin ;Shuang-li Han ; Ran-feng Zhang, 2010. Directional multiscale edge detection using the contourlet transform, Advanced Computer Control (ICACC).
  9. Chen Jinlong ; Zhang Bin ; Qi Yingjian ; Jin Fei ; Si Xuan, 2010. Image Edge Detection Method Based on Multi-Structure and Multi-Scale Mathematical Morphology, Multimedia Technology (ICMT).
  10. Anand S and Jayasudha N. 2011. Edge detection using Surfacelet Transform, International Journal of Image Processing and Application, Vol.2, No.1.
  11. Roberto H. Bamberger and Mark J. T. Smith. 1992. A filter Bank for Directional Decomposition of Images: Theory and Design. IEEE Trans on Signal processing, Vol.40, No: 4.
  12. Javad Museve Niya and Ali Aghagolzadeh. 2004. Edge Detection using Directional Wavelet Transform. IEEE Melecon.
  13. Minh N. Do and Martin Vetterli. 2005. The Contourlet Transform: An Efficient Directional Multiresolution Image Representation. IEEE Trans. On Image Processing, Vol. 14, No. 12.
  14. Arthur L. da Cunha, Jianping Zhou and Minh N. Do. 2006. The Nonsubsampled Contourlet Transform: Theory, Design, and Applications. IEEE Trans. on Image Processing, Vol. 15, No. 10.
  15. Yue Lu and Minh N. Do. 2007. Multidimensional Directional Filter Banks and Surfacelets. IEEE Trans. on Image processing, Vol: 16, NO: 4.
  16. Sheng Yi, Demetrio Labate, Glenn R. Easley, and Hamid Krim. 2009. A Shearlet Approach to Edge Analysis and Detection. IEEE Trans. on Image Processing, Vol. 18, No. 5.
  17. Canny J. 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., vol. 1–8, no. 6, pp. 679–698.
  18. Bowyer K., Kranenburg C., and Dougherty S. 2001. Edge detector evaluation using empirical ROC curves. Computer. Vision. Image Underst., Vol. 84, no. 1, pp. 77–103.
  19. Wei Jiang, Kin-Man Lam, and Ting-Zhi Shen. 2009. Efficient Edge Detection Using Simplified Gabor Wavelets. IEEE Trans. on Systems, Man, and Cybernetics- Part B: Cybernetics, Vol. 39, No. 4.
Index Terms

Computer Science
Information Sciences

Keywords

Edge Detection Directional filter bank Scale multiplication