Experiments on Robust Image Registration Using a Markov-Gibbs Appearance Model

  • Ayman El-Baz
  • Aly Farag
  • Georgy Gimel’farb
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


A new approach to align an image of a textured object with a given prototype under its monotone photometric and affine geometric transformations is experimentally compared to more conventional registration algorithms. The approach is based on measuring similarity between the image and prototype by Gibbs energy of characteristic pairwise co-occurrences of the equalized image signals. After an initial alignment, the affine transformation maximizing the energy is found by gradient search. Experiments confirm that our approach results in more robust registration than the search for the maximal mutual information or similarity of scale-invariant local features.


Image Registration Scale Invariant Feature Transform Appearance Model Gradient Search Texture Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
  2. 2.
    Holm, M.: Towards automatic rectification of satellite images using feature based matching. In: Proc. Int. Geoscience and Remote Sensing Symp., IGARSS 1991, Espoo, Finland, pp. 2439–2442 (1991)Google Scholar
  3. 3.
    Hsieh, J.W., Liao, H.Y.M., Fan, K.C., Ko, M.T., Hung, Y.P.: Image registration using a new edge-based approach. Computer Vision and Image Understanding 67, 112–130 (1997)CrossRefGoogle Scholar
  4. 4.
    Sester, M., Hild, H., Fritsch, D.: Definition of ground control features for image registration using GIS data. In: Proc. Symp. on Object Recognition and Scene Classification from Multispectral and Multisensor Pixels, CD-ROM, Columbus, Ohio (1998)Google Scholar
  5. 5.
    Roux, M.: Automatic registration of SPOT images and digitized maps. In: Proc. IEEE Int. Conf. on Image Processing ICIP 1996, Lausanne, Switzerland, pp. 625–628 (1996)Google Scholar
  6. 6.
    Hsieh, Y.C., McKeown, D.M., Perlant, F.P.: Performance evaluation of scene registration and stereo matching for cartographic feature extraction. IEEE Trans. Pattern Analysis and Machine Intelligence 14, 214–237 (1992)CrossRefGoogle Scholar
  7. 7.
    Dai, X., Khorram, S.: Development of a feature-based approach to automated image registration for multitemporal and multisensor remotely sensed imagery. In: Proc. Int. Geoscience and Remote Sensing Symp., IGARSS 1997, Singapore, pp. 243–245 (1997)Google Scholar
  8. 8.
    Shin, D., Pollard, J.K., Muller, J.P.: Accurate geometric correction of ATSR images. IEEE Trans. Geoscience and Remote Sensing 35, 997–1006 (1997)CrossRefGoogle Scholar
  9. 9.
    Mendoza, E.H., Santos, J.R., Rosa, A.N.C.S., Silva, N.C.: Land Use/land Cover Mapping in Brazilian Amazon Using Neural Network with Aster/terra Data. In: Proc. Geo-Imagery Bridging Continents, Istanbul, Turkey, pp. 123–126 (2004)Google Scholar
  10. 10.
    Growe, S., Tonjes, R.: A knowledge based approach to automatic image registration. In: Proc. IEEE Int. Conf. on Image Processing ICIP 1997, Santa Barbara, California, pp. 228–231 (1997)Google Scholar
  11. 11.
    Ton, J., Jain, A.K.: Registering landsat images by point matching. IEEE Trans. Geoscience and Remote Sensing 27, 642–651 (1989)CrossRefGoogle Scholar
  12. 12.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  13. 13.
    Pope, Theiler, J.: Automated Image Registration (AIR) of MTI Imagery. In: Proc. SPIE 5093, vol. 27, pp. 294–300 (2003)Google Scholar
  14. 14.
    Foroosh, H., Zerubia, J.B., Berthod, M.: Extension of phase correlation to subpixel registration. IEEE Trans. Image Processing 11, 188–200 (2002)CrossRefGoogle Scholar
  15. 15.
    Viola, P.: Alignment by Maximization of Mutual Information. Ph.D. dissertation, MIT, Cambridge, MA (1995)Google Scholar
  16. 16.
    Thevenaz, P., Unser, M.: Alignment An efficient mutual information optimizer for multiresolution image registration. In: Proc. IEEE Int. Conf. on Image Processing ICIP 1998, Chicago, USA, pp. 833–837 (1998)Google Scholar
  17. 17.
    Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32, 71–86 (1999)CrossRefGoogle Scholar
  18. 18.
    Gimelfarb, G., Farag, A.A.: Texture Analysis by accurate identification of simple Markov models. Cybernetics and Systems Analysis 41(1), 37–49 (2005)Google Scholar
  19. 19.
    Gimelfarb, G., Farag, A.A., El-Baz, A.: Expectation-Maximization for a linear combination of Gaussians. In: Proc. of 18th IAPR Int. Conf. on Pattern Recognition (ICPR 2004), Cambridge, UK, pp. 422–425 (August 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ayman El-Baz
    • 1
  • Aly Farag
    • 1
  • Georgy Gimel’farb
    • 2
  1. 1.Computer Vision and Image Processing LaboratoryUniversity of LouisvilleLouisville
  2. 2.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand

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