Skip to main content

Parallel High-Level Image Processing on a Standard PC

  • Conference paper
  • First Online:
Book cover Computational Science and Its Applications — ICCSA 2003 (ICCSA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2667))

Included in the following conference series:

  • 766 Accesses

Abstract

Streaming SIMD Extensions (SSE) is a unique feature embedded in the Pentium III and Pentium IV classes of microprocessors. By fully exploiting SSE, parallel algorithms can be implemented on a standard personal computer and a significant speedup can be achieved comparing to sequential code. PCs, mainly employing Intel Pentium processors, are the most commonly available and inexpensive solutions to many applications. Therefore, the performance of SSE in common image and signal processing algorithms has been studied extensively in the literature. Nevertheless, most of the studies concerned with low-level image processing algorithms, which involves pixels in pixels out type of operations. In this paper, we study higher-level image processing algorithms where image features and recognition is the output of the operations. Hough transform and Geometric hashing techniques are commonly used algorithms for this purpose. Here, their implementation using SSE are presented.

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. AltiVec Programming Environments Manual, Motorola (2001)

    Google Scholar 

  2. The Complete Guide to MMX Technology, Intel Corporation, McGraw-Hill (1997)

    Google Scholar 

  3. Conte G., Tommesani S., Zanichelli F.: The Long and Winding Road to High-Performance Image Processing with MMX/SSE, IEEE Int’l Workshop for Computer Architectures for Machine Perception (2000) 302–310

    Google Scholar 

  4. Fung Y. F., Ercan M. F., Ho T.K. and Cheung W.L.: A Parallel Computation of Power System Equations, Lecture Notes in Computer Science 2150 (2001) 371–374

    Google Scholar 

  5. Hecker Y.C. and Bolle R. M.: On Geometric Hashing and the Generalized Hough Transform, IEEE Trans. on Systems, Man and Cybernetics 24 (1994) 1328–1338

    Article  Google Scholar 

  6. Intel C/C++ Compiler Class Libraries for SIMD Operations User’s Guide (2000)

    Google Scholar 

  7. Lamdan Y. and Wolfson H., Geometric Hashing: A General and Efficient Model Based Recognition Scheme, Int. Conf. on Computer Vision (1988) 218–249

    Google Scholar 

  8. Nguyen H. and John L.: Exploiting SIMD Parallelism in DSP and Multimedia Algorithms Using the AltiVec Technology, Proc. of 13th ACM Int. Conf. on Supercomputing (1999) 11–20

    Google Scholar 

  9. Niittylahti J., Lemmetti J. and Helovuo J.: High-performance Implementation of Wavelet Algorithms on a Standard PC, Microprocessors and Microsystems 26 (2002) 173–179

    Article  Google Scholar 

  10. Rigoutsos I. and Hummel R.: Implementation of Geometric Hashing on the Connection Machine, Proc. of Workshop on Directions in Automated CAD-Based Vision (1991) 76–84

    Google Scholar 

  11. Strey A. and Bange M.: Performance Analysis of Intel’s MMX and SSE: A Case Study, Lecture Notes in Computer Science 2150 (2001) 142–147

    Google Scholar 

  12. Using MMX Instructions in a Fast IDCT Algorithm for MPEG Decoding, Intel Application Note AP-528 (1998)

    Google Scholar 

  13. VIS Instruction Set User Manual, Sun Microsystems Inc (2001)

    Google Scholar 

  14. Wang C.L. Prasanna V. K., Kim H. J. and Khokhar A.A.: Scalable Data Parallel Implementations of Object Recognition Using Geometric Hashing, Journal of Parallel and Distributed Computing 21 (1994) 96–109

    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

Ercan, M.F., Fung, Y.F. (2003). Parallel High-Level Image Processing on a Standard PC. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds) Computational Science and Its Applications — ICCSA 2003. ICCSA 2003. Lecture Notes in Computer Science, vol 2667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44839-X_79

Download citation

  • DOI: https://doi.org/10.1007/3-540-44839-X_79

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40155-1

  • Online ISBN: 978-3-540-44839-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics