skip to main content
10.1145/1185448.1185590acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesacm-seConference Proceedingsconference-collections
Article

Edge detection using wavelets

Published:10 March 2006Publication History

ABSTRACT

This paper studies the edge-detecting characteristics of the 2-D discrete wavelet transform. Our problem is to automatically detect edges. Since a common claim about the wavelet transform is that it splits images into an approximation and details, which contain edges, we use it in our experiments. First, to determine its efficacy, the 2-D discrete wavelet transform is compared to other common edge-detection methods. Also, a number of combinatorial methods for the octaves are examined in the comparison. Due to this work, a novel boundary-tracing algorithm is developed, to follow edges around an object of interest.

References

  1. The Math Works, Inc. Image Processing Toolbox User's Guide. The MathWorks, Inc, Natick, MA, 2004.Google ScholarGoogle Scholar
  2. M. Sharifi, M. Fathy, and M. T. Mahmoudi. A classified and comparative study of edge-detection algorithms. In Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC.02), pages 117--120, April 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. Marr and E. Hildreth. Theory of edge-detection. In Proceedings of the Royal Society of London. Series B, volume 207, pages 187--217, 1980.Google ScholarGoogle Scholar
  4. S. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Pattern Analysis and Machine Intelligence, 11(7):674--693, 1989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Canny. A computational approach to edge-detection. IEEE Pattern Analysis and Machine Intelligence, 8:679--698, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. R. C. Gonzalez, R. E. Woods, and S. L. Eddins. Digital Image Processing using Matlab. Pearson Prentice Hall, Saddle River, NJ, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. S. Mallat. Multifrequency channel decompositons of images and wavelet models. IEEE Trans. Acoust., Speech, Signal Processing, 37(12):2091--2110, December 1989.Google ScholarGoogle ScholarCross RefCross Ref
  8. Y. M. Stephane Jaffard and R. D. Ryan. Wavelets Tools for Science and Technology. Society for Industrial and Applied Mathematics (SIAM), 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Edge detection using wavelets

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ACM-SE 44: Proceedings of the 44th annual Southeast regional conference
      March 2006
      823 pages
      ISBN:1595933158
      DOI:10.1145/1185448

      Copyright © 2006 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 10 March 2006

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • Article

      Acceptance Rates

      Overall Acceptance Rate178of377submissions,47%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader