Skip to main content
Log in

Implicit Moment Invariants

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

The use of traditional moment invariants in object recognition is limited to simple geometric transforms, such as rotation, scaling and affine transformation of the image. This paper introduces so-called implicit moment invariants. Implicit invariants measure the similarity between two images factorized by admissible image deformations. For many types of image deformations traditional invariants do not exist but implicit invariants can be used as features for object recognition. In the paper we present implicit moment invariants with respect to polynomial transform of spatial coordinates, describe their stable and efficient implementation by means of orthogonal moments, and demonstrate their performance in artificial as well as real experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Abu-Mostafa, Y. S., & Psaltis, D. (1984). Recognitive aspects of moment invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 698–706.

    Article  Google Scholar 

  • Abu-Mostafa, Y. S., & Psaltis, D. (1985). Image normalization by complex moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, 46–55.

    Article  Google Scholar 

  • Bailey, R. R., & Srinath, M. (1996). Orthogonal moment features for use with parametric and non-parametric classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 389–398.

    Article  Google Scholar 

  • Belkasim, S. O., Shridhar, M., & Ahmadi, M. (1991). Pattern recognition with moment invariants: a comparative study and new results. Pattern Recognition, 24, 1117–1138.

    Article  Google Scholar 

  • Duda, R. O., Hart, P. E., & Stork, D. (2001). Pattern classification (2nd ed.). New York: Wiley Interscience.

    MATH  Google Scholar 

  • Dudani, S. A., Breeding, K. J., & McGhee, R. B. (1977). Aircraft identification by moment invariants. IEEE Transactions on Computers, 26, 39–45.

    Article  Google Scholar 

  • El-Khaly, F., & Sid-Ahmed, M. A. (1990). Machine recognition of optically captured machine printed arabic text. Pattern Recognition, 23, 1207–1214.

    Article  Google Scholar 

  • Flusser, J. (2000). On the independence of rotation moment invariants. Pattern Recognition, 33(9), 1405–1410.

    Article  Google Scholar 

  • Flusser, J. (2002). On the inverse problem of rotation moment invariants. Pattern Recognition, 35, 3015–3017.

    Article  MATH  Google Scholar 

  • Flusser, J., & Suk, T. (1993). Pattern recognition by affine moment invariants. Pattern Recognition, 26, 167–174.

    Article  MathSciNet  Google Scholar 

  • Flusser, J., & Suk, T. (1994a). Affine moment invariants: A new tool for character recognition. Pattern Recognition Letters, 15, 433–436.

    Article  Google Scholar 

  • Flusser, J., & Suk, T. (1994b). A moment-based approach to registration of images with affine geometric distortion. IEEE Transactions on Geoscience and Remote Sensing, 32, 382–387.

    Article  Google Scholar 

  • Flusser, J., & Suk, T. (2006). Rotation moment invariants for recognition of symmetric objects. IEEE Transactions on Image Processing, 15, 3784–3790.

    Article  MathSciNet  Google Scholar 

  • Geusebroek, J. M., Burghouts, G. J., & Smeulders, A. W. M. (2005). The Amsterdam library of object images. International Journal of Computer Vision, 61(1), 103–112.

    Article  Google Scholar 

  • Golub, G., & Kautsky, J. (1983). Calculation of Gauss quadratures with multiple free and fixed knots. Numerische Mathematik, 41, 147–163.

    Article  MATH  MathSciNet  Google Scholar 

  • Gool, L. V., Moons, T., Pauwels, E., & Oosterlinck, A. (1995). Vision and Lie’s approach to invariance. Image and Vision Computing, 13, 259–277.

    Article  Google Scholar 

  • Goshtasby, A. (1988). Registration of images with geometric distortions. IEEE Transactions on Geoscience and Remote Sensing, 26, 60–64.

    Article  Google Scholar 

  • Gruber, M., & Hsu, K. Y. (1997). Moment-based image normalization with high noise-tolerance. Pattern Recognition, 19, 136–139.

    Google Scholar 

  • Gurevich, G. B. (1964). Foundations of the theory of algebraic invariants. Noordhoff: Groningen.

    MATH  Google Scholar 

  • Hilbert, D. (1993). Theory of algebraic invariants. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  • Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8, 179–187.

    Google Scholar 

  • Hupkens, T. M., & de Clippeleir, J. (1995). Noise and intensity invariant moments. Pattern Recognition, 16, 371–376.

    Article  Google Scholar 

  • Kautsky, J., & Golub, G. (1983). On the calculation of Jacobi matrices. Linear Algebra Applications, 52/53, 439–455.

    MathSciNet  Google Scholar 

  • Khotanzad, A., & Hong, Y. H. (1990). Invariant image recognition by Zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 489–497.

    Article  Google Scholar 

  • Kybic, J., & Unser, M. (2003). Fast parametric elastic image registration. IEEE Transactions on Image Processing, 12(11), 1427–1442.

    Article  Google Scholar 

  • Li, Y. (1992). Reforming the theory of invariant moments for pattern recognition. Pattern Recognition, 25, 723–730.

    Article  Google Scholar 

  • Liao, S. X., & Pawlak, M. (1996). On image analysis by moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 254–266.

    Article  Google Scholar 

  • Maitra, S. (1979). Moment invariants. Proceedings of the IEEE, 67, 697–699.

    Article  Google Scholar 

  • Mamistvalov, A. G. (1998). N-dimensional moment invariants and conceptual mathematical theory of recognition n-dimensional solids. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 819–831.

    Article  Google Scholar 

  • Mukundan, R., & Malik, N. K. (1993). Attitude estimation using moment invariants. Pattern Recognition Letters, 14, 199–205.

    Article  Google Scholar 

  • Mukundan, R., & Ramakrishnan, K. R. (1996). An iterative solution for object pose parameters using image moments. Pattern Recognition Letters, 17, 1279–1284.

    Article  Google Scholar 

  • Mundy, J., & Zisserman, A. (1992). Geometric invariance in computer vision. Cambridge: MIT Press.

    Google Scholar 

  • Pawlak, M. (1992). On the reconstruction aspects of moment descriptors. IEEE Transactions on Information Theory, 38, 1698–1708.

    Article  MATH  MathSciNet  Google Scholar 

  • Pawlak, M. (2006). Image analysis by moments: reconstruction and computational aspects. Wroclaw: Wroclaw University of Technology Press.

    MATH  Google Scholar 

  • Pizlo, Z., & Rosenfeld, A. (1992). Recognition of planar shapes from perspective images using contour-based invariants. CVGIP: Image Understanding, 56(3), 330–350.

    Article  MATH  Google Scholar 

  • Prokop, R. J., & Reeves, A. P. (1992). A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP: Graphical Models and Image Processing, 54, 438–460.

    Article  Google Scholar 

  • Reiss, T. H. (1991). The revised fundamental theorem of moment invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 830–834.

    Article  Google Scholar 

  • Schur, I. (1968). Vorlesungen uber Invariantentheorie. Berlin: Springer.

    Google Scholar 

  • Sluzek, A. (1995). Identification and inspection of 2-D objects using new moment-based shape descriptors. Pattern Recognition Letters, 16, 687–697.

    Article  Google Scholar 

  • Suk, T., & Flusser, J. (2004a). Graph method for generating affine moment invariants. In Proceedings of the 17th int. conf. pattern recognition ICPR’04 (Vol. 2, pp. 192–195). Cambridge, UK.

  • Suk, T., & Flusser, J. (2004b). Projective moment invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 1364–1367.

    Article  Google Scholar 

  • Teague, M. R. (1980). Image analysis via the general theory of moments. Journal of Optical Society of America, 70, 920–930.

    Article  MathSciNet  Google Scholar 

  • Teh, C. H., & Chin, R. T. (1988). On image analysis by the method of moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10, 496–513.

    Article  MATH  Google Scholar 

  • Tsirikolias, K., & Mertzios, B. G. (1993). Statistical pattern recognition using efficient two-dimensional moments with applications to character recognition. Pattern Recognition, 26, 877–882.

    Article  Google Scholar 

  • Wallin, A., & Kubler, O. (1995). Complete sets of complex Zernike moment invariants and the role of the pseudoinvariants. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17, 1106–1110.

    Article  Google Scholar 

  • Wang, L., & Healey, G. (1998). Using Zernike moments for the illumination and geometry invariant classification of multispectral texture. IEEE Transactions on Image Processing, 7, 196–203.

    Article  Google Scholar 

  • Weiss, I. (1988). Projective invariants of shapes. In Proc. image understanding workshop (pp. 1125–1134).

  • Wong, R. Y., & Hall, E. L. (1978). Scene matching with invariant moments. Computer Graphics and Image Processing, 8, 16–24.

    Article  Google Scholar 

  • Wong, W. H., Siu, W. C., & Lam, K. M. (1995). Generation of moment invariants and their uses for character recognition. Pattern Recognition Letters, 16, 115–123.

    Article  Google Scholar 

  • Yang, L., & Albregtsen, F. (1996). Fast and exact computation of Cartesian geometric moments using discrete Green’s theorem. Pattern Recognition, 29, 1061–1073.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Filip Šroubek.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Flusser, J., Kautsky, J. & Šroubek, F. Implicit Moment Invariants. Int J Comput Vis 86, 72–86 (2010). https://doi.org/10.1007/s11263-009-0259-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-009-0259-4

Keywords

Navigation