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Robust Reconstruction and Generalized Dual Hahn Moments Invariants Extraction for 3D Images

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Abstract

In this paper, we introduce a new set of 3D weighed dual Hahn moments which are orthogonal on a non-uniform lattice and their polynomials are numerically stable to scale, consequent, producing a set of weighted orthonormal polynomials. The dual Hahn is the general case of Tchebichef and Krawtchouk, and the orthogonality of dual Hahn moments eliminates the numerical approximations. The computational aspects and symmetry property of 3D weighed dual Hahn moments are discussed in details. To solve their inability to invariability of large 3D images, which cause to overflow issues, a generalized version of these moments noted 3D generalized weighed dual Hahn moment invariants are presented where whose as linear combination of regular geometric moments. For 3D pattern recognition, a generalized expression of 3D weighted dual Hahn moment invariants, under translation, scaling and rotation transformations, have been proposed where a new set of 3D-GWDHMIs have been provided. In experimental studies, the local and global capability of free and noisy 3D image reconstruction of the 3D-WDHMs has been compared with other orthogonal moments such as 3D Tchebichef and 3D Krawtchouk moments using Princeton Shape Benchmark database. On pattern recognition using the 3D-GWDHMIs like 3D object descriptors, the experimental results confirm that the proposed algorithm is more robust than other orthogonal moments for pattern classification of 3D images with and without noise.

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References

  1. Wu, H., Coatrieux, J. L., & Shu, H. (2013). New algorithm for constructing and computing scale invariants of 3D Tchebichef moments. Mathematical Problems in Engineering, 12, 1–8.

    MathSciNet  Google Scholar 

  2. Mademlis, A., Axenopoulos, A., Daras, P., Tzovaras, D., & Strintzis, M. G. (2006) 3D content-based search based on 3D Krawtchouk moments. In Third international symposium on 3D data processing, visualization, and transmission. Chapel Hill, NC, 14–16 June, pp. 743–749. ISBN: 0-7695-2825-2.

  3. Koekoek, R., Lesky, P. A., & Swarttouw, R. F. Hypergeometric orthogonal polynomials and their qanalogues. Springer monographs in mathematics. Library of Congress Control Number: 2010923797

  4. Nikiforov, A. F., & Uvarov, V. B. (1988). Special functions of mathematical physics. Basel: Birkhauser.

    Book  MATH  Google Scholar 

  5. Liao, S. X., & Pawlak, M. (1996). On image analysis by moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(3), 254–266.

    Article  Google Scholar 

  6. Papakostas, G. A., Karakasis, E. G., & Koulourisotis, D. E. (2008). Efficient and accurate computation of geometric moments on gray–scale images. Pattern Recognition, 41(6), 1895–1904.

    Article  MATH  Google Scholar 

  7. Mukundan, R., Ong, S. H., Lee, P. A. (2001). Discrete vs. continuous orthogonal moments for image analysis. In Internaternational conference on imaging science, systems and technology-CISST’01, Las Vegas, pp. 23–29.

  8. Mukundan, R., Ong, S. H., & Lee, P. A. (2001). Image analysis by Tchebichef moments. IEEE Transactions on Image Processing, 10(9), 1357–1364.

    Article  MathSciNet  MATH  Google Scholar 

  9. Mukundan, R. (2004). Some computational aspects of discrete orthonormal moments. IEEE Transactions on Image Processing, 13(8), 1055–1059.

    Article  MathSciNet  Google Scholar 

  10. Zhu, H., Shu, H., Zhou, J., Luo, L., & Coatrieux, J. L. (2007). Image analysis by discrete orthogonal dual Hahn moments. Pattern Recognition Letters, 28(13), 1688–1704.

    Article  Google Scholar 

  11. Karakasis, E. G., Papakostas, G. A., Koulouriot, D. E., & Tourassis, V. D. (2013). Generalized dual Hahn moment invariants. Pattern Recognition, 46, 1998–2014.

    Article  MATH  Google Scholar 

  12. Princeton, Princeton Shape Benchmark, PSB database, http://www.cim.mcgill.ca/~shape/benchMark/. 2013.

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Correspondence to Mostafa El Mallahi.

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Mesbah, A., Zouhri, A., El Mallahi, M. et al. Robust Reconstruction and Generalized Dual Hahn Moments Invariants Extraction for 3D Images. 3D Res 8, 7 (2017). https://doi.org/10.1007/s13319-016-0113-8

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  • DOI: https://doi.org/10.1007/s13319-016-0113-8

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