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2014 | 24 | 2 | 417-428
Tytuł artykułu

Bivariate hahn moments for image reconstruction

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a new set of bivariate discrete orthogonal moments which are based on bivariate Hahn polynomials with non-separable basis. The polynomials are scaled to ensure numerical stability. Their computational aspects are discussed in detail. The principle of parameter selection is established by analyzing several plots of polynomials with different kinds of parameters. Appropriate parameters of binary images and a grayscale image are obtained through experimental results. The performance of the proposed moments in describing images is investigated through several image reconstruction experiments, including noisy and noise-free conditions. Comparisons with existing discrete orthogonal moments are also presented. The experimental results show that the proposed moments outperform slightly separable Hahn moments for higher orders.
Rocznik
Tom
24
Numer
2
Strony
417-428
Opis fizyczny
Daty
wydano
2014
otrzymano
2013-05-01
poprawiono
2013-11-09
poprawiono
2013-12-13
Twórcy
autor
  • Lab of Image Science and Technology, School of Computer Science and Engineering, Southeast University, 210096 Nanjing, China
  • School of Mathematics and Information Technology, Xiaozhuang University, 211171 Nanjing, China
autor
  • School of Mathematics and Information Technology, Xiaozhuang University, 211171 Nanjing, China
Bibliografia
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  • Wang, Z. and Bovik, A.C. (2009). Mean squared error: Love it or leave it? A new look at signal fidelity measures, IEEE Signal Processing Magazine 26(1): 98-117.
  • Wang, Z., Bovik, A.C., Sheikh, H.R. and Simoncelli, E.P. (2004). Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing 13(4): 600-612.
  • Hunek, W.P and Latawiec, K.J. (2011). A study on new right/left inverses of nonsquare polynomial matrices, International Journal of Applied Mathematics and Computer Science 21(2): 331-348, DOI: 10.2478/v10006-011-0025-y.
  • Xu, Y. (2004). On discrete orthogonal polynomials of several variables, Advances in Applied Mathematics 33(3): 615-632.
  • Xu, Y. (2005). Second-order difference equations and discrete orthogonal polynomials of two variables, International Mathematics Research Notices 2005(8): 449-475.
  • Yap, P.T., Paramesran, R. and Ong, S.H. (2003). Image analysis by Krawtchouk moments, IEEE Transactions on Image Processing 12(11): 1367-1377.
  • Yap, P. T., Paramesran, R. and Ong, S. H. (2007). Image analysis using Hahn moments, IEEE Transactions on Pattern Analysis and Machine Intelligence 29(11): 2057-2062.
  • Zhang, D. and Lu, G. (2001). Content-based shape retrieval using different shape descriptors: A comparative study, Proceedings of the International Conference on Intelligent Multimedia and Distance Education, ICIMADE01, Fargo, ND, USA, pp. 1-9.
  • Zhou, J., Shu, H., Zhu, H., Toumoulin, C. and Luo, L. (2005). Image analysis by discrete orthogonal Hahn moments, in J.S. Marques, N. Pérez de la Blanca and P. Pina (Eds.), Image Analysis and Recognition, Springer, Berlin/Heidelberg, pp. 524-531.
  • Zhu, H. (2012). Image representation using separable two-dimensional continuous and discrete orthogonal moments, Pattern Recognition 45(4): 1540-1558.
  • Zhu, H., Liu, M., Li, Y., Shu, H. and Zhang, H. (2011). Image description with nonseparable two-dimensional Charlier and Meixner moments, International Journal of Pattern Recognition and Artificial Intelligence 25(1): 37-55.
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv24i2p417bwm
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