Skip to main content

Improving Piecewise-Linear Registration Through Mesh Optimization

  • Conference paper
  • 2308 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4478))

Abstract

Piecewise-linear methods accomplish the registration by dividing the images in corresponding triangular patches, which are individually mapped through affine transformations. For this process to be successful, every pair of corresponding patches must lie on projections of a 3D plane surface; otherwise, the registration may generate undesirable artifacts, such as broken lines, which diminish the registration quality. This paper presents a new technique for improving the registration consistency by automatically refining the topology of the corresponding triangular meshes used by this method. Our approach iteratively modifies the connectivity of the meshes by swapping edges. For detecting the edges to be swapped, we analyze the local registration consistency before and after applying the action, employing for that the mutual information (MI), a metric for registration consistency significantly more robust than other well-known metrics such as normalized cross correlation (NCC) or sum of square differences (SSD). The proposed method has been successfully tested with different sets of test images, both synthetic and real.

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. Zitová, B., Flusser, J.: Image registration methods: A survey. Image and Vision Computing 21(11), 977–1000 (2003)

    Article  Google Scholar 

  2. Goshtasby, A.: Piecewise linear mapping functions for image registration. Pattern Recognition 19(6), 459–466 (1986)

    Article  Google Scholar 

  3. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)

    MATH  Google Scholar 

  4. Shewchuk, J.R.: Lecture notes on Delaunay mesh generation. Technical Report 3, University of California at Berkeley (1999)

    Google Scholar 

  5. Hoppe, H.: Progressive meshes. In: Computer Graphics (Annual Conference Series), vol. 30, pp. 99–108 (1996)

    Google Scholar 

  6. Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Mesh optimization. In: Computer Graphics (Annual Conference Series), vol. 27, pp. 19–26 (1993)

    Google Scholar 

  7. Morris, D.D., Kanade, T.: Image-consistent surface triangulation. In: Computer Vision and Pattern Recognition (CVPR 2000), pp. 332–338 (2000)

    Google Scholar 

  8. Vogiatzis, G., Torr, P., Cipolla, R.: Bayesian stochastic mesh optimisation for 3D reconstruction. In: British Machine Vision Conference (BMVC 2003), pp. 711–718 (2003)

    Google Scholar 

  9. Viola, P., Wells, W.M.: Alignment by maximization of mutual information. International Journal of Computer Vision 24(2), 137–154 (1997)

    Article  Google Scholar 

  10. Xu, G., Zhang, Z.: Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach, 1st edn. Kluwer Academic Publishers, Norwell (1996)

    MATH  Google Scholar 

  11. Spanier, E.H.: Algebraic Topology, 1st edn. McGraw-Hill, New York (1966)

    MATH  Google Scholar 

  12. Cover, T.M., Thomas, J.A.: Elements of Information Theory, 1st edn. John Wiley & Sons, Inc., New York (1991)

    MATH  Google Scholar 

  13. Chen, H.M., Varshney, P.K., Arora, M.K.: Performance of mutual information similarity measure for registration of multitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 41(11), 2445–2454 (2003)

    Article  Google Scholar 

  14. Strang, G.: Introduction to Applied Mathematics, 1st edn. Wellesley-Cambridge Press, Wellesley (1986)

    MATH  Google Scholar 

  15. Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. International Journal of Computer Vision 61(1), 103–112 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Joan Martí José Miguel Benedí Ana Maria Mendonça Joan Serrat

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Arévalo, V., González, J. (2007). Improving Piecewise-Linear Registration Through Mesh Optimization. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72849-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72848-1

  • Online ISBN: 978-3-540-72849-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics