<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Bivariate Hahn moments for image reconstruction

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

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Bivariate Hahn moments for image reconstruction"}

This paper presents a new set of bivariate discrete orthogonal moments which are based on bivariate Hahn polynomials with non-separable basis. The polynomials...

Bivariate Hahn moments for image reconstruction

Online veröffentlicht: 26. Juni 2014

Seitenbereich: 417 - 428

Eingereicht: 01. Mai 2013

Akzeptiert: 13. Dez. 2013

DOI: https://doi.org/10.2478/amcs-2014-0032

Schlüsselwörter
bivariate Hahn moments, bivariate Hahn polynomials, image reconstruction, pattern recognition

© 2014 Haiyong Wu and Senlin Yan

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Bivariate Hahn moments for image reconstruction

Online veröffentlicht: 26. Juni 2014

Seitenbereich: 417 - 428

Eingereicht: 01. Mai 2013

Akzeptiert: 13. Dez. 2013

DOI: https://doi.org/10.2478/amcs-2014-0032

Schlüsselwörterbivariate Hahn moments, bivariate Hahn polynomials, image reconstruction, pattern recognition

© 2014 Haiyong Wu and Senlin Yan

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
bivariate Hahn moments, bivariate Hahn polynomials, image reconstruction, pattern recognition