Skip to main content

A Swift and Memory Efficient Hough Transform for Systems with Limited Fast Memory

  • Conference paper
Image Analysis and Recognition (ICIAR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5627))

Included in the following conference series:

Abstract

This paper focuses on implementation of a speedy Hough Transform (HT) which considers the memory constraints of the system. Because of high memory demand, small systems (DSPs, tiny robots) cannot realize efficient implementation of HT. Keeping this scenario in mind, the paper discusses an effective and memory-efficient method of employing the HT for extraction of line features from a gray scale image. We demonstrate the use of a circular buffer for extraction of image edge pixels and store the edge image in a manner that is different from the conventional way. Approximation of the two dimensional Hough Space by a one dimensional array is also discussed. The experimental results reveal that the proposed algorithm produces better results, on small and large systems, at a rapid pace and is economical in terms of memory usage.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Novak, G., Mahlknecht, S.: TINYPHOON a tiny autonomous mobile robot. In: Proceedings of the IEEE International Symposium on Industrial Electronics, pp. 1533–1538 (June 2005)

    Google Scholar 

  2. Dao, N., You, B.J., Oh, S.R., Hwangbo, M.: Visual self-localization for indoor mobile robots using natural lines. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2003, vol. 2, pp. 1252–1257 (2003)

    Google Scholar 

  3. Burns, J.B., Hanson, A.R., Riseman, E.M.: Extracting straight lines. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(4), 425–455 (1986)

    Article  Google Scholar 

  4. Guru, D.S., Shekar, B.H., Nagabhushan, P.: A simple and robust line detection algorithm based on small eigenvalue analysis. Pattern Recognition Letters 25(1), 1–13 (2004)

    Article  Google Scholar 

  5. Climer, S., Bhatia, S.K.: Local lines: A linear time line detector. Pattern Recognition Letters 24, 2291–2300 (2003)

    Article  MATH  Google Scholar 

  6. Kälviäinen, H., Hirvonen, P., Xu, L., Oja, E.: Probabilistic and non-probabilistic hough transforms: Overview and comparisons. Image and Vision Computing 13(4), 239–252 (1995)

    Article  Google Scholar 

  7. Kälviäinen, H., Hirvonen, P.: An extension to the randomized hough transform exploiting connectivity. Pattern Recognition Letters 18(1), 77–85 (1997)

    Article  Google Scholar 

  8. Gatos, B., Perantonis, S.J., Papamarkos, N.: Accelerated hough transform using rectangular image decomposition. Electronic Letters 32(8), 730–732 (1996)

    Article  Google Scholar 

  9. Guil, N., Guil, N., Zapata, E.L.: A parallel pipelined hough transform. In: Fraigniaud, P., Mignotte, A., Robert, Y., Bougé, L. (eds.) Euro-Par 1996. LNCS, vol. 1124, pp. 131–138. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  10. Pan, Li, Hamdi: An improved constant-time algorithm for computing the radon and hough transforms on a reconfigurable mesh. IEEETSMC: IEEE Transactions on Systems, Man, and Cybernetics 29 (1999)

    Google Scholar 

  11. Bais, A., Khan, M.U.K., Yahya, K.M., Sablatnig, R., Hassan, G.M.: Memory efficient vision based line feature extraction for tiny mobile robots. In: Proceedings of International Conference on Image Analysis and Recognition (ICIAR), July 2009 (to appear)

    Google Scholar 

  12. Duda, R., Hart, P.: Use of the Hough transformation to detect lines and curves in the pictures. Communications of the Association for Computing Machinery 15(1), 11–15 (1972)

    Article  MATH  Google Scholar 

  13. Immerkær, J.: Some remarks on the straight line hough transform. Pattern Recognition Letters 19(12), 1133–1135 (1998)

    Article  MATH  Google Scholar 

  14. Analog Devices: ADSP-BF537 Blackfin Processor Hardware Reference, Preliminary Revision 1.1 (January 2005)

    Google Scholar 

  15. Singleton, R.: A method for computing the fast fourier transform with auxiliary memory and limited high speed storage. IEEE Transaction on Audio and Electroacoustics (15), 91–98 (1967)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Khan, M.U.K., Bais, A., Yahya, K.M., Hassan, G.M., Arshad, R. (2009). A Swift and Memory Efficient Hough Transform for Systems with Limited Fast Memory. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02611-9_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02610-2

  • Online ISBN: 978-3-642-02611-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics