Advertisement

Feature-Driven Direct Non-Rigid Image Registration

  • Florent BrunetEmail author
  • Vincent Gay-Bellile
  • Adrien Bartoli
  • Nassir Navab
  • Rémy Malgouyres
Article

Abstract

The direct registration problem for images of a deforming surface has been well studied. Parametric flexible warps based, for instance, on the Free-Form Deformation or a Radial Basis Function such as the Thin-Plate Spline, are often estimated using additive Gauss-Newton-like algorithms. The recently proposed compositional framework has been shown to be more efficient, but cannot be directly applied to such non-groupwise warps.

Our main contribution in this paper is the Feature-Driven framework. It makes possible the use of compositional algorithms for most parametric warps such as those above mentioned. Two algorithms are proposed to demonstrate the relevance of our Feature-Driven framework: the Feature-Driven Inverse Compositional and the Feature-Driven Learning-based algorithms. As another contribution, a detailed derivation of the Feature-Driven warp parameterization is given for the Thin-Plate Spline and the Free-Form Deformation. We experimentally show that these two types of warps have a similar representational power. Experimental results show that our Feature-Driven registration algorithms are more efficient in terms of computational cost, without loss of accuracy, compared to existing methods.

Keywords

Direct registration Inverse compositional image alignment Deformable model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agarwal, A., & Triggs, B. (2006). Recovering 3D human pose from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1). Google Scholar
  2. Baker, S. Matthews, I. (2004). Lucas-Kanade 20 years on: A unifying framework. International Journal of Computer Vision, 56(3), 221–255. CrossRefGoogle Scholar
  3. Bartoli, A., & Zisserman, A. (2004). Direct estimation of non-rigid registrations. In Proceedings of the British machine vision conference. Google Scholar
  4. Benhimane, S., & Malis, E. (2004). Real-time image-based tracking of planes using efficient second-order minimization. In Proceedings of the international conference on intelligent robots and systems. Google Scholar
  5. Bookstein, F. L. (1989). Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6), 567–585. CrossRefzbMATHGoogle Scholar
  6. Bregler, C., Hertzmann, A., & Biermann, H. (2000). Recovering non-rigid 3D shape from image streams. In: Proceedings of the international conference on computer vision and pattern recognition Google Scholar
  7. Charpiat, G., Faugeras, O., & Keriven, R. (2005). Image statistics based on diffeomorphic matching. In Proceedings of the international conference on computer vision. Google Scholar
  8. Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998). Active appearance models. In Proceedings of the European conference on computer vision. Google Scholar
  9. Cootes, T. F., Marsland, S., Twining, C. J., Smith, K., & Taylor, C. J. (2004). Groupwise diffeomorphic non-rigid registration for automatic model building. In Proceedings of the European conference on computer vision. Google Scholar
  10. De Boor, C. (2001). A practical guide to splines (revised edition). Berlin: Springer. zbMATHGoogle Scholar
  11. Fornefett, M., Rohr, K., & Stiehl, H. (1999). Elastic registration of medical images using radial basis functions with compact support. In Proceedings of the international conference on computer vision and pattern recognition. Google Scholar
  12. Gay-Bellile, V., Perriollat, M., Bartoli, A., & Sayd, P. (2006). Image registration by combining thin-plate splines with a 3D morphable model. In Proceedings of the international conference on image processing. Google Scholar
  13. Gay-Bellile, Bartoli A. V, & Sayd, P. (2007). Feature-driven direct non-rigid image registration. In Proceedings of the British machine vision conference. Google Scholar
  14. Georgel, P., Benhimane, S., & Nassir, N. (2008). A unified approach combining photometric and geometric information for pose estimation. In Proceedings of the British machine vision conference. Google Scholar
  15. Haber, E., & Modersitzki, J. (2006). Intensity gradient based registration and fusion of multi-modal images. In S. Berlin (Ed.), Lecture notes in computer science: Vol. 4191. Proceedings of the medical image computing and computer-assisted intervention (pp. 726–733). Google Scholar
  16. Johnson, H. J., & Christensen, G. E. (2002). Consistent landmark and intensity-based image registration. IEEE Transactions on Medical Imaging, 21(5), 450–461. CrossRefGoogle Scholar
  17. Joshi, S., & Miller, M. I. (2000). Landmark matching via large deformation diffeomorphisms. IEEE Transactions on Image Processing, 9, 1357–1370. CrossRefzbMATHMathSciNetGoogle Scholar
  18. Jurie, F., & Dhome, M. (2002). Hyperplane approximation for template matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 996–1000. CrossRefGoogle Scholar
  19. Lim, J., & Yang, M. H. (2005). A direct method for non-rigid motion with thin-plate spline. In Proceedings of the international conference on computer vision and pattern recognition. Google Scholar
  20. Little, JA, Hill, D. L. G., & Hawkes, D. J. (1997). Deformations incorporating rigid structures. Computer Vision and Image Understanding, 66(2), 223–232. CrossRefGoogle Scholar
  21. Matthews, I., Baker S. (2004). Active appearance models revisited. International Journal of Computer Vision, 60(2), 135–164. CrossRefMathSciNetGoogle Scholar
  22. Meyer, C. R., Boes, J. L., Kim, B., Bland, P. H., Zasadny, K. R., Kison, P. V., Koral, K., Frey, K. A., & Wahl, R. L. (1997). Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations. Medical Image Analysis, 1(3), 195–206. CrossRefGoogle Scholar
  23. Pilet, J., Lepetit, V., & Fua, P. (2005). Real-time non-rigid surface detection. In Proceedings of the international conference on computer vision and pattern recognition. Google Scholar
  24. Pizarro, D., & Bartoli, A. (2007). Shadow resistant direct image registration. In Proceedings of the Scandinavian conference on image analysis (pp. 928–937). Google Scholar
  25. Pluim, J. P. W., Maintz, J. B. A., & Viergever, M. A. (2003). Mutual-information-based registration of medical images: a survey. IEEE Transactions on Medical Imaging, 22, 986–1004. CrossRefGoogle Scholar
  26. Romdhani, S., & Vetter, T. (2003). Efficient, robust and accurate fitting of a 3D morphable model. In Proceedings of the international conference on computer vision. Google Scholar
  27. Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L., Leach, M. O., & Hawkes, D. J. (1999). Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging, 18(8), 712–721. CrossRefGoogle Scholar
  28. Szeliski, R. (2006). Image alignment and stitching: a tutorial. Foundations and Trends in Computer Graphics and Vision, 2, 1–104. CrossRefGoogle Scholar
  29. Torr, P. H. S., & Zisserman, A. (1999). Feature based methods for structure and motion estimation. In Workshop on vision algorithms: theory and practice. Google Scholar
  30. Wahba, G. (1990). CBMS-NSF regional conference series in applied mathematics: Vol. 59. Spline models for observational data. Philadelphia: SIAM. zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Florent Brunet
    • 1
    • 2
    Email author
  • Vincent Gay-Bellile
    • 3
  • Adrien Bartoli
    • 1
  • Nassir Navab
    • 2
  • Rémy Malgouyres
    • 4
  1. 1.ISIT/Université d’AuvergneClermont-FerrandFrance
  2. 2.CAMPARTUMMünchenGermany
  3. 3.CEA LISTVision and Content Engineering LaboratoryGif-sur-YvetteFrance
  4. 4.LIMOSClermont-FerrandFrance

Personalised recommendations