Table of Contents
ISRN Signal Processing
Volume 2011, Article ID 672353, 9 pages
Research Article

Edge-Detection in Noisy Images Using Independent Component Analysis

1Department of ECE, Concordia University, 1455 de Maisonneuve West, Montreal, QC, Canada H3G 1M8
2EECS Department, University of Toledo, MS 308, 2801 W. Bancroft Street, Toledo, OH 43606, USA

Received 20 January 2011; Accepted 21 February 2011

Academic Editor: F. Palmieri

Copyright © 2011 Kaustubha Mendhurwar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Canny, “A computation approach to edge-detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986. View at Google Scholar · View at Scopus
  2. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice-Hall, Upper Saddle River, NJ, USA, 3rd edition, 2008.
  3. M. Pathegama and Ö. Göl, “Edge-based image segmentation,” World Academy of Science, Engineering and Technology, vol. 33, no. 2, pp. 164–167, 2005. View at Google Scholar
  4. W. K. Pratt, Digital Image Processing, John Wiley & Sons, Hoboken, NJ, USA, 2007.
  5. A. Hyvärinen and E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Computation, vol. 9, no. 7, pp. 1483–1492, 1997. View at Google Scholar · View at Scopus
  6. A. Hyvarinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 626–634, 1999. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 13, no. 4-5, pp. 411–430, 2000. View at Publisher · View at Google Scholar · View at Scopus
  8. X. Han, S. Dai, J. Li, and G. Xia, “Edge-detection algorithm based on ICA-domain shrinkage in noisy images,” Science in China F, vol. 51, no. 9, pp. 1349–1359, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. W. Chen, X. Y. Zeng, and H. Lu, “Edge-detection and texture segmentation based on independent component analysis,” in Proceedings of the 16th International Conference on Pattern Recognition, pp. 351–354, Quebec, Canada, August 2002. View at Scopus
  10. S. Hornillo-Mellado, R. Martín-Clemente, J. I. Acha, and C. G. Puntonet, “Application of independent component analysis to edge-detection and watermarking,” Lecture Notes in Computer Science, vol. 2687, pp. 273–280, 2003. View at Google Scholar · View at Scopus
  11. X. H. Han, Y. W. Chen, and Z. Nakao, “Robust edge-detection by independent component analysis in noisy images,” IEICE Transactions on Information and Systems, vol. E87-D, no. 9, pp. 2204–2211, 2004. View at Google Scholar · View at Scopus
  12. P. Hoyer, Independent component analysis in image denoising, M.S. thesis, Helsinki University of Technology, 1999.
  13. P. Comon, “Independent component analysis, a new concept?” Signal Processing, vol. 36, no. 3, pp. 287–314, 1994. View at Google Scholar · View at Scopus
  14. C. Jutten and J. Herault, “Blind separation of sources—part I: an adaptive algorithm based on neuromimetic architecture,” Signal Processing, vol. 24, no. 1, pp. 1–10, 1991. View at Google Scholar · View at Scopus
  15. A. J. Bell and T. J. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution,” Neural Computation, vol. 7, no. 6, pp. 1129–1159, 1995. View at Google Scholar · View at Scopus
  16. J. Shlens, A Tutorial on Principal Component Analysis, Center for Neural Science, New York University, New York, NY, USA, 2005.
  17. Helsinki University of Technology, “The FastICA Package for MATLAB,” 2005,
  18. J. Stone, Independent Component Analysis: A Tutorial, MIT Press, Cambridge, Mass, USA, 2004.
  19. A. Olmos and F. A. A. Kingdom, “McGill calibrated colour image database,” 2004,
  20. A. J. Bell and T. J. Sejnowski, “The 'independent components' of natural scenes are edge filters,” Vision Research, vol. 37, no. 23, pp. 3327–3338, 1997. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Hyvärinen, “Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation,” Neural Computation, vol. 11, no. 7, pp. 1739–1768, 1999. View at Google Scholar · View at Scopus
  22. A. Hyvärinen, P. O. Hoyer, and E. Oja, “Image denoising by sparse code shrinkage,” in Intelligent Signal Processing, IEEE Press, New York, NY, USA, 2000. View at Google Scholar
  23. A. Buades, B. Coll, and J. M. Morel, “A non local algorithm for image denoising,” IEEE Computer Vision & Pattern Recognition, vol. 2, pp. 60–65, 2005. View at Google Scholar
  24. M. Roushdy, “Comparative study of edge-detection algorithms applying on the gray scale noisy image using morphological filter,” GVIP Journal, vol. 6, no. 4, pp. 17–23, 2006. View at Google Scholar