Advertisement

Fast Affine Transform for Real-Time Machine Vision Applications

  • Sunyoung Lee
  • Gwang-Gook Lee
  • Euee S. Jang
  • Whol-Yul Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

In this paper, we have proposed a fast affine transform method for real-time machine vision applications. Inspection of parts by machine vision requires accurate, fast, reliable, and consistent operations, where the transform of visual images plays an important role. Image transform is generally expensive in computation for real-time applications. For example, a transform including rotation and scaling would require four multiplications and four additions per pixel, which is going to be a great burden to process a large image. Our proposed method reduces the complexity substantially by removing four multiplications per pixel, which exploits the relationship between two neighboring pixels. In addition, this paper shows that the affine transform can be performed by fixed point operations with marginal error. Two interpolation methods are also tried on top of the proposed method in order to test the feasibility of fixed point operations. Experimental results indicated that the proposed algorithm was about six times faster than conventional ones without any interpolation and five times faster with bilinear interpolation.

Keywords

Machine Vision Neighboring Pixel Point Operation Marginal Error Bilinear Interpolation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rafael, C.G., Richard, E.W.: Digital Image Processing, pp. 296–299. Addison-Wesley, Reading (1993)Google Scholar
  2. 2.
    Donald, H., Baker, M.P.: Computer Graphics C Version 2nd, pp. 184–208. Prentice-Hall, Englewood Cliffs (1997)Google Scholar
  3. 3.
    Milan, S., Vaclav, H., Roger, B.: Image Processing, Analysis and Machine Vision 2nd. PWS Publishing (1998)Google Scholar
  4. 4.
    Anil, K.J.: Fundamentals of Digital Image Processing, pp. 320–322. Prentice-Hall, Englewood Cliffs (1989)MATHGoogle Scholar
  5. 5.
    Davies, E.R.: Machine Vision, pp. 663–675. Academic Press, London (1997)Google Scholar
  6. 6.
    Castleman, K.R.: Digital Image Processing, pp. 115–137. Prentice Hall, Englewood Cliffs (1996)Google Scholar
  7. 7.
    Affine Transformations of Images: A least Squares FormulationGoogle Scholar
  8. 8.
    A Generalized Two Pass Approach to Image Geometric Transform Implementation, Theory and RamificationsGoogle Scholar
  9. 9.
    Convolution-based Interpolation for Fast, High-quality Rotation of Images Google Scholar
  10. 10.
    Fraser, D.: Comparison of High Special Frequencies of Two-pass and One-pass Geometric Transform Algorithms. Computer Vision, Graphics, Image Processing 46, 267–283 (1989)CrossRefGoogle Scholar
  11. 11.
    Paeth, A.W.: A Fast Algorithm for General Raster Rotation. In: Proc. Graphics Interface 1986, pp. 77–81 (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sunyoung Lee
    • 1
  • Gwang-Gook Lee
    • 2
  • Euee S. Jang
    • 1
  • Whol-Yul Kim
    • 2
  1. 1.Digital Media Lab., College of Information and CommunicationsHanyang UniversitySeoulKorea
  2. 2.Image Engineering Lab., College of EngineeringHanyang UniversitySeoulKorea

Personalised recommendations