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Combined Invariants to Convolution and Rotation and their Application to Image Registration

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Advances in Pattern Recognition — ICAPR 2001 (ICAPR 2001)

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Abstract

A new class of moment-based features invariant to image rotation, translation, and also to convolution with an unknown point-spread function is introduced in this paper. These features can be used for the recognition of objects captured by a nonideal imaging system of unknown position and blurring parameters. Practical applications to the registration of satellite images is presented.

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References

  1. Y. S. Abu-Mostafa and D. Psaltis. Recognitive aspects of moment invariants. IEEE Trans. Pattern Analysis and Machine Intelligence, 6:698–706, 1984.

    Google Scholar 

  2. K. Arbter, W. E. Snyder, H. Burkhardt, and G. Hirzinger. Application of affineinvariant Fourier descriptors to recognition of 3-D objects. IEEE Trans. Pattern Analysis and Machine Intelligence, 12:640–647, 1990.

    Article  Google Scholar 

  3. L. G. Brown. A survey of image registration techniques. ACM Computing Surveys, 24:325–376, 1992.

    Article  Google Scholar 

  4. J. Flusser. Object matching by means of matching likelihood coeffcients. Pattern Recognition Letters, 16:893–900, 1995.

    Article  Google Scholar 

  5. J. Flusser and T. Suk. Degraded image analysis: An invariant approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6):590–603, 1998.

    Article  Google Scholar 

  6. J. Flusser and B. Zitová. Combined invariants to linear filtering and rotation. Intl. J. Pattern Recognition Art. Intell., 13(8):1123–1136, 1999.

    Article  Google Scholar 

  7. J. Flusser and T. Suk. Pattern recognition by affine moment invariants. Pattern Recognition, 26:167–174, 1993.

    Article  MathSciNet  Google Scholar 

  8. J. Flusser and T. Suk. Degraded image analysis: An invariant approach. IEEE Trans. Pattern Analysis and Machine Intelligence, 20:590–603, 1998.

    Article  Google Scholar 

  9. L. M. G. Fonseca and B. S. Manjunath. Registration techniques for multisensor remotely sensed imagery. Photogrammetric Eng. Remote Sensing, 62:1049–1056, 1996.

    Google Scholar 

  10. M. K. Hu. Visual pattern recognition by moment invariants. IRE Trans. Information Theory, 8:179–187, 1962.

    Google Scholar 

  11. D. Kundur and D. Hatzinakos. Blind image deconvolution. IEEE Signal Processing Magazine, 13(3):43–64, 1996.

    Article  Google Scholar 

  12. W. G. Lin and S. Wang. A note on the calculation of moments. Pattern Recognition Letters, 15:1065–1070, 1994.

    Article  Google Scholar 

  13. J. L. Mundy and A. Zisserman. Geometric Invariance in Computer Vision. MIT Press, 1992.

    Google Scholar 

  14. C. A. Rothwell, A. Zisserman, D. A. Forsyth, and J. L. Mundy. Canonical frames for planar object recognition. Proc. 2nd European Conf. Computer Vision, pages 757–772, Springer, 1992.

    Google Scholar 

  15. M. I. Sezan and A. M. Tekalp. Survey of recent developments in digital image restoration. Optical Engineering, 29:393–404, 1990.

    Article  Google Scholar 

  16. T. Suk and J. Flusser. Vertex-based features for recognition of projectively deformed polygons. Pattern Recognition, 29:361–367, 1996.

    Article  Google Scholar 

  17. M. R. Teague. Image analysis via the general theory of moments. J. Optical Soc. of America, 70:920–930, 1980.

    Article  MathSciNet  Google Scholar 

  18. C. H. Teh and R. T. Chin. On image analysis by the method of moments. IEEE Trans. Pattern Analysis and Machine Intelligence, 10:496–513, 1988.

    Article  MATH  Google Scholar 

  19. I. Weiss. Projective invariants of shapes. Proc. Image Understanding Workshop, pages 1125–1134, Cambridge, Mass., 1988.

    Google Scholar 

  20. B. Zitová, J. Kautsky, G. Peters, and J. Flusser. Robust detection of significant points in multiframe images. Pattern Recognition Letters, 20(2):199–206, 1999.

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod vodárenskou věží 4, 182 08, Prague 8, Czech Republic

    Jan Flusser & Barbara Zitová

Authors
  1. Jan Flusser
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  2. Barbara Zitová
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Exeter, EX4 4PT, Exeter, UK

    Sameer Singh

  2. Computational Intelligence Group, Tuiuti University of Parana, Curitiba, Brazil

    Nabeel Murshed

  3. Institute of Computer Aided Automation PRIP-Group 1832, Vienna University of Technology, Favoritenstr. 9/2/4, 1040, Wien, Austria

    Walter Kropatsch

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© 2001 Springer-Verlag Berlin Heidelberg

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Flusser, J., Zitová, B. (2001). Combined Invariants to Convolution and Rotation and their Application to Image Registration. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_37

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  • DOI: https://doi.org/10.1007/3-540-44732-6_37

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41767-5

  • Online ISBN: 978-3-540-44732-0

  • eBook Packages: Springer Book Archive

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