Deriving Sparse Coefficients of Wavelet Pyramid Taking Clues from Hough Transform

  • Jignesh K. Bhavsar
  • Suman K. Mitra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

Many applications like image compression requires sparse representation of image. To represent the image by sparse coefficients of transform is an old age technique. Still research is going on for better sparse representation of an image. A very recent technique is based on learning the basis for getting sparse coefficients. But learned basis are not guaranteed to span l 2 space, which is required for reconstruction. In this paper we are presenting a new technique to choose steerable basis of wavelet pyramid which gives sparse coefficients and better reconstruction. Here selection of steerable basis is based on clues from Hough transform.

Keywords

Orientation Direction Band Pass Sparse Representation Hough Transformation Sparse Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Tropp, J.A., Laska, J.N., Duarte, M.F., Romberg, J.K., Baraniuk, R.G.: Beyond nyquist: Efficient sampling of sparse bandlimited signals. CoRR (2009)Google Scholar
  2. 2.
    Li, Y., Cichocki, A., ichi Amari, S., Shishkin, S., Cao, J., Gu, F., Cao, J., Gu, F.: Sparse representation and its applications in blind source separation. In: Seventeenth Annual Conference on Neural Information Processing Systems, NIPS 2003 (2003)Google Scholar
  3. 3.
  4. 4.
    Sallee, P., Olshausen, B.A., Lewicki, M.S.: Learning sparse image codes using a wavelet pyramid architecture  13, 887–893 (2001)Google Scholar
  5. 5.
    Freeman, W.T., Edward, H.A.Y.: The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 891–906 (1991)CrossRefGoogle Scholar
  6. 6.
    Karasaridis, A., Simoncelli, E.: A filter design technique for steerable pyramid image transforms, pp. 2387–2390 (1996)Google Scholar
  7. 7.
    Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)CrossRefGoogle Scholar
  8. 8.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jignesh K. Bhavsar
    • 1
  • Suman K. Mitra
    • 1
  1. 1.Dhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia

Personalised recommendations