IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v421y2022ics0096300322000121.html
   My bibliography  Save this article

Novel quaternion discrete shifted Gegenbauer moments of fractional-orders for color image analysis

Author

Listed:
  • Hosny, Khalid M.
  • Darwish, Mohamed M.

Abstract

Orthogonal moments (OMs) are used to extract features from color images. OMs with fractional orders are better than the OMs with integer orders due to their ability to extract fine features. This paper defined novel quaternion orthogonal shifted Gegenbauer moments (FrQSGMs) of fractional orders for color image analysis and recognition. Since both shifted Gegenbauer polynomials and the input digital images are defined in the domain [0, 1] × [0, 1], the proposed FrQSGMs did not need any image mapping or image interpolation. The invariance to geometric transformations of the proposed FrQSGMs is derived by expressing these moments in geometric moment invariants of fractional order. We conduct various experiments to test the accuracy, invariance to RST, sensitivity to noise, recognition of similar color images, and computational times. The proposed descriptors outperformed the existing orthogonal moments with fractional orders.

Suggested Citation

  • Hosny, Khalid M. & Darwish, Mohamed M., 2022. "Novel quaternion discrete shifted Gegenbauer moments of fractional-orders for color image analysis," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    • Handle: RePEc:eee:apmaco:v:421:y:2022:i:c:s0096300322000121
    DOI: 10.1016/j.amc.2022.126926
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322000121
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2022.126926?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Deng, An-Wen & Gwo, Chih-Ying, 2018. "Fast and stable algorithms for high-order Pseudo Zernike moments and image reconstruction," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 239-253.
    2. Wang, Xiang-yang & Li, Wei-yi & Yang, Hong-ying & Wang, Pei & Li, Yong-wei, 2015. "Quaternion polar complex exponential transform for invariant color image description," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 951-967.
    3. Wang, Chunpeng & Hao, Qixian & Ma, Bin & Li, Jian & Gao, Hongling, 2021. "Fractional-order quaternion exponential moments for color images," Applied Mathematics and Computation, Elsevier, vol. 400(C).
    4. Liang Hua & Yujian Qiang & Juping Gu & Ling Chen & Xinsong Zhang & Hairong Zhu, 2015. "Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, September.
    5. Kashkari, Bothayna S.H. & Syam, Muhammed I., 2016. "Fractional-order Legendre operational matrix of fractional integration for solving the Riccati equation with fractional order," Applied Mathematics and Computation, Elsevier, vol. 290(C), pages 281-291.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Atanacković, Teodor M. & Janev, Marko & Pilipović, Stevan, 2018. "Non-linear boundary value problems involving Caputo derivatives of complex fractional order," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 326-342.
    2. Zeid, Samaneh Soradi, 2019. "Approximation methods for solving fractional equations," Chaos, Solitons & Fractals, Elsevier, vol. 125(C), pages 171-193.
    3. Hong-Jiang Wu & Ying-Ying Zhang & Han-Yu Li, 2023. "Expectation identities from integration by parts for univariate continuous random variables with applications to high-order moments," Statistical Papers, Springer, vol. 64(2), pages 477-496, April.
    4. Ahmad Sami Bataineh & Osman Rasit Isik & Abedel-Karrem Alomari & Mohammad Shatnawi & Ishak Hashim, 2020. "An Efficient Scheme for Time-Dependent Emden-Fowler Type Equations Based on Two-Dimensional Bernstein Polynomials," Mathematics, MDPI, vol. 8(9), pages 1-17, September.
    5. Navickas, Z. & Telksnys, T. & Marcinkevicius, R. & Ragulskis, M., 2017. "Operator-based approach for the construction of analytical soliton solutions to nonlinear fractional-order differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 625-634.
    6. Al-Mdallal, Qasem M. & Abu Omer, Ahmed S., 2018. "Fractional-order Legendre-collocation method for solving fractional initial value problems," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 74-84.
    7. Haifa Bin Jebreen & Ioannis Dassios, 2022. "A Biorthogonal Hermite Cubic Spline Galerkin Method for Solving Fractional Riccati Equation," Mathematics, MDPI, vol. 10(9), pages 1-14, April.
    8. Muhammed I. Syam & Mohammed Abu Omar, 2018. "A Numerical Method for Solving a Class of Nonlinear Second Order Fractional Volterra Integro-Differntial Type of Singularly Perturbed Problems," Mathematics, MDPI, vol. 6(4), pages 1-22, March.
    9. Bota, Constantin & Căruntu, Bogdan, 2017. "Analytical approximate solutions for quadratic Riccati differential equation of fractional order using the Polynomial Least Squares Method," Chaos, Solitons & Fractals, Elsevier, vol. 102(C), pages 339-345.
    10. Singh, Harendra & Srivastava, H.M., 2019. "Jacobi collocation method for the approximate solution of some fractional-order Riccati differential equations with variable coefficients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1130-1149.
    11. Mohamed Abd Elaziz & Khalid M Hosny & Ahmad Salah & Mohamed M Darwish & Songfeng Lu & Ahmed T Sahlol, 2020. "New machine learning method for image-based diagnosis of COVID-19," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:421:y:2022:i:c:s0096300322000121. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.