International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 8, Issue 2 (February 2021), Pages: 54-59

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 Original Research Paper

 Title: Image representation based on fractional order Legendre and Laguerre orthogonal moments

 Author(s): R. M. Farouk *, Qamar A. A. Awad

 Affiliation(s):

 Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-5441-1003

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2021.02.007

 Abstract:

In this paper, we have introduced new sets of fractional order orthogonal basis moments based on Fractional order Legendre orthogonal Functions (FLeFs) and Fractional order Laguerre orthogonal Functions (FLaFs) for image representation. We have generated a novel set of Fractional order Legendre orthogonal Moments (FLeMs) from fractional order Legendre orthogonal functions and a new set of Fractional order Laguerre orthogonal Moments (FLaMs) from the fractional order Laguerre orthogonal functions. The new presented sets of (FLeMs) and (FLaMs) are tested with the recently introduced Fractional order Chebyshev orthogonal Moments (FCMs). This edge detection filter can be used successfully in the gray level image and color images. The new sets of fractional moments are used to reconstruct the gray level image. The numerical results show FLeMs and FLaMs are promised techniques for image representation. The computational time of the proposed techniques is compared with the computational time of Chebyshev orthogonal Moments techniques and gives better results. Also, the fractional parameters give the flexibility of studying global features of the image at different positions of moments. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Image representation, Fractional order moments, Fractional order Legendre moments, Fractional order Laguerre moments, Edge detection, Image segmentation

 Article History: Received 13 June 2020, Received in revised form 23 August 2020, Accepted 23 September 2020

 Acknowledgment:

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

  Farouk RM and Awad QAA (2021). Image representation based on fractional order Legendre and Laguerre orthogonal moments. International Journal of Advanced and Applied Sciences, 8(2): 54-59

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 

 Tables

 Table 1 Table 2

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 References (20)

  1. Benouini R, Batioua I, Zenkouar K, Zahi A, Najah S, and Qjidaa H (2019). Fractional-order orthogonal Chebyshev moments and moment invariants for image representation and pattern recognition. Pattern Recognition, 86: 332-343. https://doi.org/10.1016/j.patcog.2018.10.001   [Google Scholar]
  2. Bhrawy A, Alhamed Y, Baleanu D, and Al-Zahrani A (2014). New spectral techniques for systems of fractional differential equations using fractional-order generalized Laguerre orthogonal functions. Fractional Calculus and Applied Analysis, 17(4): 1137-1157. https://doi.org/10.2478/s13540-014-0218-9   [Google Scholar]
  3. Fernández L, Pérez TE, and Piñar MA (2007). Second order partial differential equations for gradients of orthogonal polynomials in two variables. Journal of Computational and Applied Mathematics, 199(1): 113-121. https://doi.org/10.1016/j.cam.2005.09.029   [Google Scholar]
  4. Fernández L, Pérez TE, and Piñar MA (2011). Orthogonal polynomials in two variables as solutions of higher order partial differential equations. Journal of Approximation Theory, 163(1): 84-97. https://doi.org/10.1016/j.jat.2009.08.007   [Google Scholar]
  5. Fernández L, Pérez TE, Piñar MA, and Xu Y (2010). Krall-type orthogonal polynomials in several variables. Journal of Computational and Applied Mathematics, 233(6): 1519-1524. https://doi.org/10.1016/j.cam.2009.02.067   [Google Scholar]
  6. Flusser J, Suk T, and Zitová B (2016). 2D and 3D image analysis by moments. John Wiley and Sons, Hoboken, USA. https://doi.org/10.1002/9781119039402   [Google Scholar]
  7. Hassani H, Machado JT, and Naraghirad E (2019). Generalized shifted Chebyshev polynomials for fractional optimal control problems. Communications in Nonlinear Science and Numerical Simulation, 75: 50-61. https://doi.org/10.1016/j.cnsns.2019.03.013   [Google Scholar]
  8. Hassani H, Machado JT, Avazzadeh Z, and Naraghirad E (2020). Generalized shifted Chebyshev polynomials: Solving a general class of nonlinear variable order fractional PDE. Communications in Nonlinear Science and Numerical Simulation, 85: 105229. https://doi.org/10.1016/j.cnsns.2020.105229   [Google Scholar]
  9. Hosny KM, Darwish MM, and Aboelenan T (2020b). Novel fractional-order generic Jacobi-Fourier moments for image analysis. Signal Processing, 172: 107545. https://doi.org/10.1016/j.sigpro.2020.107545   [Google Scholar]
  10. Hosny KM, Darwish MM, and Aboelenen T (2020a). New fractional-order Legendre-Fourier moments for pattern recognition applications. Pattern Recognition, 103: 107324. https://doi.org/10.1016/j.patcog.2020.107324   [Google Scholar]
  11. Kazem S, Abbasbandy S, and Kumar S (2013). Fractional-order Legendre functions for solving fractional-order differential equations. Applied Mathematical Modelling, 37(7): 5498-5510. https://doi.org/10.1016/j.apm.2012.10.026   [Google Scholar]
  12. Papakostas GA (2014). Over 50 years of image moments and moment invariants. In: Papakostas GA (Ed.), Moments and moment invariants-Theory and applications: 3-32. Science Gate Publishing, Thrace, Greece. https://doi.org/10.15579/gcsr.vol1.ch1   [Google Scholar]
  13. Parand K and Delkhosh M (2017). The generalized fractional order of the Chebyshev functions on nonlinear boundary value problems in the semi-infinite domain. Nonlinear Engineering, 6(3): 229-240. https://doi.org/10.1515/nleng-2017-0030   [Google Scholar]
  14. Parand K, Delkhosh M, and Nikarya M (2017). Novel orthogonal functions for solving differential equations of arbitrary order. Tbilisi Mathematical Journal, 10(1): 31-55. https://doi.org/10.1515/tmj-2017-0004   [Google Scholar]
  15. Sweilam NH, Nagy AM, and El-Sayed AA (2016). On the numerical solution of space fractional order diffusion equation via shifted Chebyshev polynomials of the third kind. Journal of King Saud University-Science, 28(1): 41-47. https://doi.org/10.1016/j.jksus.2015.05.002   [Google Scholar]
  16. Teague MR (1980). Image analysis via the general theory of moments. Journal of the Optical Society of America, 70(8): 920-930. https://doi.org/10.1364/JOSA.70.000920   [Google Scholar]
  17. Xiao B, Li L, Li Y, Li W, and Wang G (2017). Image analysis by fractional-order orthogonal moments. Information Sciences, 382: 135-149. https://doi.org/10.1016/j.ins.2016.12.011   [Google Scholar]
  18. Xiao B, Luo J, Bi X, Li W, and Chen B (2020). Fractional discrete Tchebyshev moments and their applications in image encryption and watermarking. Information Sciences, 516: 545-559. https://doi.org/10.1016/j.ins.2019.12.044   [Google Scholar]
  19. Xu Y (2004). On discrete orthogonal polynomials of several variables. Advances in Applied Mathematics, 33(3): 615-632. https://doi.org/10.1016/j.aam.2004.03.002   [Google Scholar]
  20. Xu Y (2005). Second-order difference equations and discrete orthogonal polynomials of two variables. International Mathematics Research Notices, 2005(8): 449-475. https://doi.org/10.1155/IMRN.2005.449   [Google Scholar]