Skip to main content

Sub-pixel Edge Fitting Using B-Spline

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4418))

Abstract

In this paper we propose an algorithm for the sub-pixel edge detection using a B-spline model. In contrast to the usual methods which are generally sensitive to local perturbations, our approach is based on a global computation of the edge using a Maximum Likelihood rule. In the proposed algorithm the likelihood of the observations is explicitly computed, it ensures the filtering of the noisiest data. Experiments are given and show the adequacy and effectiveness of this algorithm.

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

Buying options

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tabatabai, A.J., Mitchell, R.: Edge localisation to sub-pixel values in digital imagery. IEEE Trans. Patt. Anal. Mach. Intell. 6, 188–201 (1984)

    Article  Google Scholar 

  2. Lyvers, E.P., Mitchell, O.R.: Precision edge contrast and orientation estimation. IEEE Trans. Patt. Anal. Mach. Intell. 10, 927–937 (1988)

    Article  Google Scholar 

  3. Lyvers, E.P., et al.: Sub-pixel measurements using a moment based edge operator. IEEE Trans. Patt. Anal. Mach. Intell. 11, 1293–1309 (1989)

    Article  Google Scholar 

  4. Shan, Y., Boon, G.W.: Sub-pixel localisation of edges with non-uniform blurring: a finite closed-form approach. Image and Vision computing 18, 1015–1023 (2000)

    Article  Google Scholar 

  5. Cheng, S.-C., Wu, T.-L.: Subpixel edge detection of color images by principal axis analysis and moment-preserving principle. Patt. Recog. 38, 527–537 (2005)

    Article  MATH  Google Scholar 

  6. Ghosal, S., Mehrotra, R.: Orthogonal moment operators for sub-pixel edge detection. Patt. Recog. 26, 295–306 (1993)

    Article  Google Scholar 

  7. Ying-Donga, Q., et al.: A fast subpixel edge detection method using Sobel-Zernike moments operator. Image and Vision Computing 23, 11–17 (2005)

    Article  Google Scholar 

  8. Nomura, Y., et al.: Edge location to sub-pixel precision and analysis. System Comput. in Japan 22, 70–80 (1991)

    Google Scholar 

  9. Hussmann, S., Ho, T.H.: A high-speed subpixel edge detector implementation inside a FPGA. Real-Time Imaging 9, 361–368 (2003)

    Article  Google Scholar 

  10. Baba, M., Ohtani, K.: A novel subpixel edge detection system for dimension measurement and object localization using an analogue-based approach. Journal of Optics A: Pure & Applied Optics 3, 276–283 (2001)

    Article  Google Scholar 

  11. Truchetet, F., Nicolier, F., Laligant, O.: Subpixel edge detection for dimensional controle by artificial vision. J. Electronic Imaging 10, 234–239 (2001)

    Article  Google Scholar 

  12. Jensen, K., Anastassiou, D.: Subpixel edge localization and the interpolation of still images. IEEE Trans. Image Processing 4, 285–295 (1995)

    Article  Google Scholar 

  13. Deriche, R., Giruadon, G.: A computational approach for corner and vertex detection. Int. J. Comp. Vision 10, 101–124 (1993)

    Article  Google Scholar 

  14. Wang, H., Brady, M.: Real-time corner detection algorithm for motion estimation. Image and Vision Computing 13, 695–703 (1995)

    Article  Google Scholar 

  15. Chen, F.-L., Lin, S.-W.: Subpixel estimation of circle parameters using orthogonal circular detector. Computer Vision & Image Underst. 78, 206–221 (2000)

    Article  Google Scholar 

  16. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comp. Vision 1, 321–331 (1988)

    Article  Google Scholar 

  17. Brigger, P., Hoeg, J., Unser, M.: B-splines snakes: A flexible tool for parametric contour detection. IEEE Trans. Image processing 9, 1484–1496 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  18. Meegama, R.G.N., Rajapakse, J.C.: NURBS. Image and Vision Computing 21, 551–562 (2003)

    Article  Google Scholar 

  19. Venkatesh, S., Kisworo, M., West, G.A.: Detection of curved edges at sub-pixel accuracy using deformable models. In: Proceedings of IEEE conf. Vis. Image Signal Process, pp. 304–312. IEEE Computer Society Press, Los Alamitos (1995)

    Google Scholar 

  20. de Boor, C.: A Practical Guide to Splines. Springer, New York (1978)

    MATH  Google Scholar 

  21. Devernay, F.: A Non-Maxima Suppression Method for Edge Detection with Sub-Pixel Accuracy. INRIA Sophia Antipolis Research Report Nb. 2724 (1995)

    Google Scholar 

  22. Bouchara, F.: Efficient algorithm for computation of the second-order moment of the subpixel-edge position. Applied Optics 43, 4550–4558 (2004)

    Article  Google Scholar 

  23. Deriche, R.: Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int. J. Comp. Vision 1, 167–187 (1987)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

André Gagalowicz Wilfried Philips

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Bouchara, F., Bertrand, M., Ramdani, S., Haydar, M. (2007). Sub-pixel Edge Fitting Using B-Spline. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2007. Lecture Notes in Computer Science, vol 4418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71457-6_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71457-6_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71456-9

  • Online ISBN: 978-3-540-71457-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics