CFP last date
15 January 2025
Reseach Article

Edge Detection using Skewed and Elongated Basis Functions

by S. Anand, S.Jeeva, T.Thivya
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 4
Year of Publication: 2012
Authors: S. Anand, S.Jeeva, T.Thivya
10.5120/ijais12-450163

S. Anand, S.Jeeva, T.Thivya . Edge Detection using Skewed and Elongated Basis Functions. International Journal of Applied Information Systems. 1, 4 ( February 2012), 28-34. DOI=10.5120/ijais12-450163

@article{ 10.5120/ijais12-450163,
author = { S. Anand, S.Jeeva, T.Thivya },
title = { Edge Detection using Skewed and Elongated Basis Functions },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2012 },
volume = { 1 },
number = { 4 },
month = { February },
year = { 2012 },
issn = { 2249-0868 },
pages = { 28-34 },
numpages = {9},
url = { https://www.ijais.org/archives/volume1/number4/83-0163/ },
doi = { 10.5120/ijais12-450163 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:41:11.824375+05:30
%A S. Anand
%A S.Jeeva
%A T.Thivya
%T Edge Detection using Skewed and Elongated Basis Functions
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 1
%N 4
%P 28-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposed an edge detection using Skewed and Elongated Basis functions (SEBF). In an image, edges present at any directions and any scale. Common edge detection algorithms are single scale, and limited in directions. The proposed method efficiently removes these difficulties by using SEBF that are having multiscale and non separable properties. The two dimensional non separable properties of SEBF provide multiple directions, whereas the multiscale to explore the hidden edge information at various scales. The SEBF are derived from anisotropic directionlets are applied for edge detection to effectively preserving edges in all orientations and scales. This paper uses Figure of Merit (F), accuracy, sensitivity, specificity characteristics to analyze the performances. The experimental results showed that the proposed algorithms provide improvement in all performance measure.

References
  1. Mallat S, HW ANG W L 2002 .Singularity Detection and Processing with Wavelets. IEEE Trans on Info Theory, 617-643
  2. Mitra Basu, 2002. Gaussian-Based Edge-Detection Methods—A Survey. IEEE Transactions on Systems, Man, and Cybernetic, 252-260
  3. Castleman K. R., 1996. Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall
  4. Donoho D. L., Vetterli M., DeVore R. A., and Daubechies I., 1998. Data compression and harmonic analysis. IEEE Trans. Inf. Theory, 2435–2476
  5. Vetterli M. and Kova?cevic´ J.,1995. Wavelets and Subband Coding. EnglewoodCliffs, NJ: Prentice-Hall
  6. Pennec E. L. and Mallat S.,2000. Image compression with geometric wavelets. IEEE Int. Conf. Image Processing, 661–664
  7. Stephane Mallat,Gabriel Peyre 2005. Sparse geometric image representations with bandelets, IEEE Trans. Image Process.,. 423–438.
  8. Donoho D. L.,1999. Wedgelets: Nearly-minimax estimation of edges. Ann.Statist., 859–897
  9. Romberg J. K.,.Wakin M, and Baraniuk R.,2002. Multiscale wedgelet image analysis: Fast decompositions and modeling. IEEE Int. Conf. Image Processing, 585–588
  10. Romberg J. K., Wakin M., and Baraniuk R 2003. Approximation and compression of piecewise smooth images using a wavelet/wedgelet geometric model. IEEE Int. Conf. Image Processing, Barcelona, Spain, 49–52.
  11. Wakin M., Romberg J., Hyeokho C., and Baraniuk R.,2002. Rate-distortion optimized image compression using wedgelets. IEEE Int. Conf. Image Processing, 237 -240.
  12. Wakin M., Romberg J., Choi H., and Baraniuk R., 2006. Wavelet-domain approximation and compression of piecewise smooth images. IEEE Trans. Image Process, 1071–1087
  13. Candès E. J. and Donoho D. L.,1999. Curvelets – A surprisingly effective nonadaptive representation for objects with edges, in Curve and Surface Fitting, A. Cohen, C. Rabut, and L. L. Schumaker, Eds. Nashville, TN: Vanderbilt Univ. Press
  14. Candès E. J. and Donoho D., 1999. Curvelets and curvilinear integrals, Tech. Rep., Dept. Statist., Stanford Univ., Stanford, CA
  15. Candès E. J. and Donoho D. L. 2002. New tight frames of curvelets and optimal representations of objects with smooth singularities, Tech. Rep., Dep. Statist., Stanford Univ., Stanford, CA
  16. Do M. N. and Vetterli M.,2005. The contourlet transform: An efficient directional multiresolution image representation, IEEE Trans. Image Process., 2091–2106
  17. Skodras A., Christopoulos C., and Ebrahimi T., 2001. The JPEG 2000 still image compression standard, IEEE Signal Processing Mag., 36–58
  18. Cohen A. and Daubechies I.,1993. Non-separable bidimensional wavelet bases, Revista Mat. Iberoamer., 51–137
  19. J.Kova?cevic´ 1991, Filter Banks andWavelets: Extensions and Applications,” Ph.D. dissertation, Graduate School of Arts and Sciences, Columbia Univ., New York.
  20. Kova?cevic´ J. and Vetterli M.,1992. Nonseparable multidimensional perfect reconstruction filter banks and wavelet bases for , IEEE Trans. Inf. Theory, 533–555
  21. Rosenfeld A, and Thurston M, 1970. Edge and Curve Detection for Visual Scene Analysis, IEEE Trans. Computer,562-569
  22. Sadler B M, Pham T, and Sadler L C, 1998. Optimal and wavelet based Shock Wave Detection and Estimation, J Acoust. Soc. Am., 955-963
  23. Ziou D, and Tabbone S, 1993.A Multiscale Edge Detector, PR, 26(9), 1305-1314
  24. Park D J, Nam K N, and Park R H, 1995.Multiresolution Edge Detection Techniques, Pattern Recognition, 211-219
  25. Canny J., 1986.A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell., 679–698
  26. Bowyer K., Kranenburg C., and Dougherty S.,2001. Edge detector evaluation using empirical ROC curves,Comput. Vis. Image Underst 77–103
  27. Wei Jiang, Kin-Man Lam, and Ting-Zhi Shen,2009. Efficient Edge Detection Using Simplified Gabor Wavelets, IEEE Transactions On Systems, Man, and Cybernetics- Part B: Cybernetics
Index Terms

Computer Science
Information Sciences

Keywords

Edge Detection Directionlet Basis Scale Multiplication