Abstract
This paper introduces a system that automatically classifies registration problems based on the type of registration required. Rather than rely on a single “best” algorithm, the proposed system is made up of a suite of image registration techniques. Image pairs are analyzed according to the types of variation that occur between them, and appropriate algorithms are selected to solve for the alignment. In the case where multiple forms of variation are detected all potentially appropriate algorithms are run, and a normalized cross correlation (NCC) of the results in their respective error spaces is performed to select which alignment is best. In 87% of the test cases the system selected the transform of the expected corresponding algorithm, either through elimination or through NCC, while in the final 13% a better transform (as calculated by NCC) was proposed by one of the other methods. By classifying the type of registration problem and choosing an appropriate method the system significantly improves the flexibility and accuracy of automatic registration techniques.
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References
Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., Cohen, M.: Interactive digital photomontage. In: SIGGRAPH 2004: ACM SIGGRAPH 2004 Papers, pp. 294–302. ACM Press, New York (2004)
Lillesand, T.M., Kiefer, R.W.: Remote Sensing and Image Interpretation, 6th edn. Wiley, Chichester (2007)
Campbell, J.B.: Introduction to remote sensing, 4th edn. Guildford Press (2008)
Maintz, J., Viergever, M.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)
Pluim, J., Maintz, J., Viergever, M.: Mutual-information-based registration of medical images: a survey. IEEE Transactions on Medical Imaging 22(8), 986–1004 (2003)
Zitová, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)
Brown, M., Lowe, D.G.: Recognising panoramas. In: Proceedings of Ninth IEEE International Conference on Computer Vision, October 16, vol. 2, pp. 1218–1225 (2003)
Flusser, J., Zitová, B., Suk, T.: Invariant-based registration of rotated and blurred images. In: Proceedings of IEEE 1999 International Geoscience and Remote Sensing Symposium, pp. 1262–1264. IEEE Computer Society Press, Los Alamitos (1999)
Zitová, B., Kautsky, J., Peters, G., Flusser, J.: Robust detection of significant points in multiframe images. Pattern Recogn. Lett. 20(2), 199–206 (1999)
Ward, G.: Robust image registration for compositing high dynamic range photographs from handheld exposures. Journal of Graphics Tools 8, 17–30 (2003)
Schechner, Y.Y., Nayar, S.K.: Generalized mosaicing: High dynamic range in a wide field of view. Int. J. Comput. Vision 53(3), 245–267 (2003)
Yang, G., Stewart, C., Sofka, M., Tsai, C.L.: Registration of challenging image pairs: Initialization, estimation, and decision. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(11), 1973–1989 (2007)
Drozd, A.L., Blackburn, A.C., Kasperovich, I.P., Varshney, P.K., Xu, M., Kumar, B.: A preprocessing and automated algorithm selection system for image registration. SPIE, Vol. 6242, 62420T (2006)
Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys 24, 325–376 (1992)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision (darpa). In: Proceedings of the 1981 DARPA Image Understanding Workshop, April 1981 pp. 121–130 (1981)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Azzari, P., Di Stefano, L., Mattoccia, S.: An evaluation methodology for image mosaicing algorithms. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 89–100. Springer, Heidelberg (2008)
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Oldridge, S., Miller, G., Fels, S. (2009). Automatic Classification of Image Registration Problems. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_22
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DOI: https://doi.org/10.1007/978-3-642-04667-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04666-7
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