Skip to main content

Complete Image Partitioning on Spiral Architecture

  • Conference paper
  • First Online:
Parallel and Distributed Processing and Applications (ISPA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2745))

Abstract

Uniform image partitioning has been achieved on Spiral Architecture, which plays an important role in parallel image processing on many aspects such as uniform data partitioning, load balancing, zero data exchange between the processing nodes et al. However, when the number of partitions is not the power of seven like 49, each sub-image except one is split into a few fragments which are mixed together. We could not tell which fragments belong to which sub-image. It is an unacceptable flaw to parallel image processing. This paper proposes a method to resolve the problem mentioned above. From the experimental results, it is shown that the proposed method correctly identifies the fragments belonging to the same sub-image and successfully collects them together to be a complete sub-image. Then, these sub-images can be distributed into the different processing nodes for further processing.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Pitas, I.: Parallel Algorithm for Digital Image Processing. Computer Vision and Neural Network, John Wiley & Sons, Chichester, England (1993)

    Google Scholar 

  2. Squyres, J.M., Lumsdaine, A., Stevenson, R.L.: A Cluster-based Parallel Image Processing Toolkit. Proceedins of the IS&T Conference on Image and Video Processing, (San Joes, CA, 1995) 228–239

    Google Scholar 

  3. You, J., Zhu, W.P., Cohen, H.A., Pissaloux, E.: Fast Object Recognition by Parallel Image Matching on a Distributed System. Proceedings of the 17th IEEE Symposium on Parallel and Distributed Processing (1995) 78–85

    Google Scholar 

  4. Koelbel, C.H., Loveman, D.B., Schreiber, R.S., Jr., G.L. S., Zosel, M.E.: The High Performance Fortran Handbook. MIT Press, Cambridge, MA. (1994)

    Google Scholar 

  5. Oberhuber, M.: Distributed High-Performance Image Processing on the Internet. PhD Thesis, Graz University of Technology, Austria (1998)

    Google Scholar 

  6. Sheridan, P., Hintz, T., Moore, W.: Spiral Architecture in Machine Vision. Proceedings of the Australian Occam and Transputer Conference (1991)

    Google Scholar 

  7. Schwartz, E.: Computational Anatomy and Functional Architecture of Striate Cortex: A Spatial Mapping Approach to Perceptual Coding. Vision Research 20 (1980) 645–669

    Article  Google Scholar 

  8. Wu, Q., He, X., Hintz, T.: Distributed Image Processing on Spiral Architecture. Proceedings of the 5th International Conference on Algorithm and Architectures for Parallel Processing, (Beijing, China, 2002) 84–91

    Google Scholar 

  9. Sheridan, P., Hintz, T., Alexander, D.: Pseudo-invariant Image Transformations on a Hexagonal Lattice. Image and Vision Computing, 18(11)(2000). 907–917

    Article  Google Scholar 

  10. Spiral Architecture for Machine Vision. PhD Thesis, University of Technology, Sydney (1996)

    Google Scholar 

  11. He, X., Hintz, T., Szewcow, U.: Affine Integral Invariants and Object Recognition. Proceedings of the High Performance Computing Conference, (Singapore, 1998), 419–423

    Google Scholar 

  12. Wu, Q., He, X., Hintz, T.: Image Rotation without Scaling on Spiral Architecture. Journal of WSCG, 10(2)(2002) 515–520

    Google Scholar 

  13. Bharadwaj, V., Li., X., Ko, C.C.: Efficient Partitioning and Scheduling of Computer Vision and Image Processing Data on Bus Networks Using Divisible Load Analysis. Image and Vision Computing, 18 (2000). 919–938

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wu, Q., He, X., Hintz, T., Ye, Y. (2003). Complete Image Partitioning on Spiral Architecture. In: Guo, M., Yang, L.T. (eds) Parallel and Distributed Processing and Applications. ISPA 2003. Lecture Notes in Computer Science, vol 2745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-37619-4_31

Download citation

  • DOI: https://doi.org/10.1007/3-540-37619-4_31

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40523-8

  • Online ISBN: 978-3-540-37619-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics