Acquainted Non-convexity Multiresolution Based Optimization for Affine Parameter Estimation in Image Registration

  • J. Dinesh Peter
  • V. K. Govindan
  • Abraham T. Mathew
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5370)


Affine parameter estimation technique applied to image registration is found useful in obtaining reliable fusion of same object’s images taken from different modalities, into single image with strong features. Usually, the minimization in affine parameter estimation technique can be done by least squares in a quadratic way. However, this will be sensitive to the presence of outliers. Therefore, affine parameter estimation technique for image registration calls for methods that are robust enough to withstand the influence of outliers. Progressively, some robust estimation techniques demanding non-quadratic and non-convex potentials adopted from statistical literature have been used for solving these. Addressing the minimization of error function in a factual framework for finding the global optimal solution, the minimization can begin with the convex estimator at the coarser level and gradually introduce non-convexity i.e., from soft to hard redescending non-convex estimators when the iteration reaches finer level of multiresolution pyramid. Comparison has been made to find the performance results of proposed method with the registration results found using different robust estimators.


Image Registration Motion estimation Affine parameter estimation outliers Robust M-estimators 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Woods, R.P.: Within-modality registration using intensity-based cost functions. In: Bankman, I. (ed.) Handbook of Medical Imaging: Processing and Analysis, vol. 33, Academic Press, New York (2000)Google Scholar
  2. 2.
    Rossman, M.A., Candocia, F., Adjouadi, M., Jayakar, P., Yaylali, I.: Application of Affine Transformations for the Co-registration of SPECT Images. In: Proceedings of the Fourth IASTED International Conference on Signal and Image Processing (SIP), Kauai, Hawaii, August 12-14, pp. 595–600 (2002)Google Scholar
  3. 3.
    Periaswamy, S., Farid, H.: Elastic Registration in the Presence of Intensity Variations. IEEE Transactions on Medical Imaging 22(7), 865–874 (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    Black, M.J., Anandan, P.: The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields. Computer Vision and Image Understanding 63(1), 75–104 (1996)CrossRefGoogle Scholar
  5. 5.
    Negahdaripour, S.: Revised definition of optical flow: integration of radiometric and geometric cues for dynamic scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(9), 961–979 (1996)CrossRefGoogle Scholar
  6. 6.
    Kim, Y.-H., Martinez, A.M., Kak, A.C.: Robust Motion Estimation under Varying Illumination. Image and Vision Computing 23(4), 365–375 (2005)CrossRefGoogle Scholar
  7. 7.
    Besl, P.J., McKay, N.D.: A Method for Registration of 3-D Shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  8. 8.
    Hajnal, J.V., Nadeem, S., Soar, E.J., Oatridge, A., Young, I.R., Bydder, G.M.: A Registration and Interpolation Procedure for Subvoxel Matching of Serially Acquired MR Images. Journal of Computer Assisted Tomography 19(2), 289–296 (1995)CrossRefGoogle Scholar
  9. 9.
    Blake, A., Zisserman, A.: Visual Reconstruction. MIT Press, Cambridge (1987)Google Scholar
  10. 10.
    Dahyot, R., Charbonnier, P., Heitz, F.: Robust visual recognition of colour images. In: Proceedings of CVPR, vol. 1, pp. 685–690 (2000)Google Scholar
  11. 11.
    Hebert, T., Leahy, R.: A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors. IEEE Transactions on Medical Imaging 8, 194–202 (1990)CrossRefGoogle Scholar
  12. 12.
    Farid, H., Simoncelli, E.P.: Optimally rotation-equivariant directional derivative kernels. In: Sommer, G., Daniilidis, K., Pauli, J. (eds.) CAIP 1997. LNCS, vol. 1296, pp. 207–214. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  13. 13.
    Johnson, K.A., Becker, J.A.: The whole brain Atlas,
  14. 14.
    Wolberg, G.: Digital image warping. IEEE Computer Society Press, Los Alamitos (1994)Google Scholar
  15. 15.
    Holden, M., Hill, D.L.G., Denton, E.R.E., Jarosz, J.M., Cox, T.C.S., Rohlfing, T., Goodey, J., Hawkes, D.J.: Voxel Similarity Measures for 3D Serial MR Brain Image Registration. IEEE Transactions on Medical Imaging 19(2), 94–102 (2000)CrossRefGoogle Scholar
  16. 16.
    Charbonnier, P., Blanc-Fraud, L., Aubert, G., Barlaud, M.: Deterministic edge-preserving regularization in computed imaging. IEEE Transactions on Image Processing 6(2), 298–311 (1997)CrossRefGoogle Scholar
  17. 17.
    Holland, P.W., Welsch, R.E.: Robust regression using iteratively reweighed least-squares. Communications in Statistics: Theory and Methods A6, 813–827 (1977)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • J. Dinesh Peter
    • 1
  • V. K. Govindan
    • 1
  • Abraham T. Mathew
    • 1
  1. 1.National Institute of Technology CalicutIndia

Personalised recommendations