Skip to main content

Advertisement

Log in

Incremental variations of image moments for nonlinear image registration

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this paper, we use image moments to solve the problem of estimating deformation fields given a pair of images as input. We use a single family of polynomials to parameterize the deformation field and to define image moments. In this way, variations in image moments can be represented by a set of linear equations. We solve these equations iteratively for the deformation parameters between two shapes. Our approach improves existing moment-based registration methods in both robustness to noise and convergence rate. In addition, our method does not rely on solving the correspondence problem. We have extensively tested our new method on both synthetically deformed MPEG-7 shapes and real-world biomedical images.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Domokos C., Kato Z.: Parametric estimation of affine deformations of planar shapes. Pattern Recognit. 43(3), 569–578 (2010)

    Article  MATH  Google Scholar 

  2. Liu, W., Ribeiro, E.: A meshless method for variational nonrigid 2-D shape registration. In: International Symposium on Visual Computing (2010)

  3. Ming M.K.: Visual pattern recognition by moment invariants. Trans. Inf. Theory IT-8, 179–187 (1962)

    Article  Google Scholar 

  4. Flusser J., Kautsky J., Šroubek F.: Implicit moment invariants. Int. J. Comput. Vis. 86(1), 72–86 (2010)

    Article  MathSciNet  Google Scholar 

  5. Liu, W., Ribeiro, E.: Estimating nonrigid shape deformation using moments. In: IEEE International Conference on Pattern Recognition (2010)

  6. Zitova B., Flusser J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)

    Article  Google Scholar 

  7. De Castro E., Morandi C.: Registration of translated and rotated images using finite Fourier transforms. In: IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 700–703 (1987)

    Google Scholar 

  8. Chen Q.-s., Defrise M., Deconinck F.: Symmetric phase-only matched filtering of fourier-mellin transforms for image registration and recognition. In: IEEE Trans. Pattern Anal. Mach. Intell. 16, 1156–1168 (1994)

    Google Scholar 

  9. Lehmann, T.M.: A two-stage algorithm for model-based registration of medical images. In: IEEE International Conference on Pattern Recognition, p. 344. IEEE Computer Society (1998)

  10. Sato J., Hollinghurst N.J.: Image registration using multi-scale texture moments. Image Vis. Comput. 13(5), 496–513 (1995)

    Article  Google Scholar 

  11. Le Moigne J., Campbell W.J., Cromp R.F.: An automated parallel image registration technique based on the correlation of wavelet features. In: IEEE Trans. Geosci. Remote Sens. 40(8), 1849–1864 (2002)

    Google Scholar 

  12. Zokai S., Wolberg G.: Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations. In: IEEE Trans. Image Process. 14(10), 1422–1434 (2005)

    MathSciNet  Google Scholar 

  13. Bentoutou Y., Taleb N., Kpalma K., Ronsin J.: An automatic image registration for applications in remote sensing. In: IEEE Trans. Geosci. Remote Sens. 43(9), 2127 (2005)

    Google Scholar 

  14. Shen D., Davatzikos C.: HAMMER: hierarchical attribute matching mechanism for elastic registration. In: IEEE Trans. Med. Imaging 21(11), 1421–1439 (2003)

    Google Scholar 

  15. Lucchese L., Doretto G., Cortelazzo G.M.: A frequency domain technique for range data registration. In: IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1468–1484 (2002)

    Google Scholar 

  16. Ito, K., Aoki, T., Kosuge, E., Kawamata, R., Kashima, I.: Medical image registration using phase-only correlation for distorted dental radiographs. In: IEEE International Conference on Pattern Recognition, pp. 1–4 (2008)

  17. Crespo J.B.F.P., Aguiar P.M.Q.: Revisiting complex moments for 2-D shape representation and image normalization. In: IEEE Trans. Image Process. 20(10), 2896–2911 (2011)

    MathSciNet  Google Scholar 

  18. Hoey, J., Little, J.J.: Bayesian clustering of optical flow fields. In: IEEE International Conference on Computer Vision, vol. 2, p. 1086. IEEE Computer Society (2003)

  19. Inc Ebrary: Moments and Moment Invariants in Pattern Recognition. Wiley, New York (2009)

    Book  MATH  Google Scholar 

  20. Davies B.: Integral Transforms and Their Applications. Springer, New York (2002)

    Book  MATH  Google Scholar 

  21. Mehtre B.M., Kankanhalli M.S. et al.: Shape measures for content based image retrieval: a comparison. Inf. Process. Manag. 33(3), 319–337 (1997)

    Article  Google Scholar 

  22. Tian, Y., Narasimhan, S.G.: A globally optimal data-driven approach for image distortion estimation. In: IEEE International Conference on Computer Vision and Pattern Recognition (2010)

  23. Rudin W.: Real and Complex Analysis. Tata McGraw-Hill, New York (2006)

    Google Scholar 

  24. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence, vol. 3, pp. 674–679 (1981)

  25. Thakoor N., Gao J., Jung S.: Hidden Markov model-based weighted likelihood discriminant for 2-D shape classification. In: IEEE Trans. Image Process. 16(11), 2707–2719 (2007)

    MathSciNet  Google Scholar 

  26. Liu, R., Li, S., Tan, C.L., Pang, B.C., Lim, C.C.T., Lee, C.K., Tian, Q., Zhang, Z.: Fast traumatic brain injury ct slice indexing via anatomical feature classification. In: IEEE International Conference on Image Processing, pp. 4377–4380 (2010)

  27. Rueckert D., Sonoda L.I., Hayes C., Hill D.L.G., Leach M.O., Hawkes D.J.: Nonrigid registration using free-form deformations: application to breast MR images. In: IEEE Trans. Med. Imaging 18(8), 712–721 (1999)

    Google Scholar 

  28. Chang S.G., Yu B., Vetterli M.: Adaptive wavelet thresholding for image denoising and compression. In: IEEE Trans. Image Process. 9(9), 1532–1546 (2002)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eraldo Ribeiro.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, W., Ribeiro, E. Incremental variations of image moments for nonlinear image registration. SIViP 8, 423–432 (2014). https://doi.org/10.1007/s11760-012-0304-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-012-0304-6

Keywords

Navigation