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Fast and Accurate Computation of 3D Charlier Moment Invariants for 3D Image Classification

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Abstract

The problem of 3D digital object invariability is encountered in image processing, especially in pattern classification/recognition. The 3D object should be correctly recognized regardless of its particular position and orientation in the scene. This paper proposes a new method to extract 3D Charlier moment invariants to translation and scaling (3DCMITS). These descriptors are extracted directly from discrete orthogonal Charlier polynomials without using 3D geometric moment invariants. This method is fast and does not require any numerical approximation compared to the indirect method based on 3D geometric moment invariants. The results show the proposed method's effectiveness in terms of speed with an improvement exceeding 99,97%. For validation purposes and as an illustration of the interest of 3DCMITS, this paper offers a classification system for 3D objects based on the proposed 3DCMITS and Support Vector Machine (SVM) classifier. The obtained results are verified with K-Nearest Neighbor (KNN) classifier and other existing works in the literature.

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Data Availability

The datasets generated in our experiments are available from McGill 3D Shape Benchmark images Database, URL link: http://www.cim.mcgill.ca/~shape/benchMark/airplane.html. (2017). Accessed 9 March 2020. The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CPs :

Charlier Polynomials

3DCMs :

3D Charlier Moments

3DCMIT :

3D Charlier Moment Invariants to Translation

3DCMIS :

3D Charlier Moment Invariants to Scaling

3DCMITS :

3D Charlier Moment Invariants to Translation and Scaling

MSE :

Mean Square Error

3DMRI :

3D Magnetic Resonance Image

ETIR :

Execution Time Improvement Ratio

SVM :

Support Vector Machine

KNN :

K-Nearest Neighbor

CRP :

Correct Recognition Percentage

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Acknowledgements

The authors would like to thank the anonymous referees for their helpful comments and suggestions.

Funding

No funding has been granted for this work.

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Authors and Affiliations

  1. Faculty of Science Dhar El Mahrez, Laboratory of Electronic Signals and Systems of Information LESSI, CED‑ST, STIC, University Sidi Mohamed Ben Abdellah, Fez, Morocco

    M. Yamni, O. El ogri, H. Karmouni & H. Qjidaa

  2. Engineering, Systems and Applications Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, BP 72, My Abdallah Avenue Km. 5 Imouzzer Road, Fez, Morocco

    A. Daoui & M. Sayyouri

  3. Faculty of Medicine and Pharmacy, Clinical Neuroscience Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    M. Maaroufi & B. Alami

  4. Department of Radiology and Clinical Imaging, University Hospital of Fez, Fez, Morocco

    M. Maaroufi & B. Alami

  5. Department of Biophysics and Clinical MRI Methods, Sidi Mohamed Ben Abdellah University, Km 2.200, Sidi Hrazem Road, BP. 893, 30000, Fez, Morocco

    B. Alami

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  1. M. Yamni
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Contributions

Category 1: Conception and design of study was performed by M. Y, H K. Acquisition of data was performed by Mohamed Yamni, H K, A D, O E ogri, M S, H Q. Analysis and/or interpretation of data was performed by M. Y, H. K, M. S, H. Q, M. M, B. A. Category 2: M. Y, H. K., A. D., O. E. ogri drafted the manuscript. M. Y, M. S, H. Q, M. M, B. A. revised the manuscript critically for important intellectual content. Category 3: M. Y, H. K, A. D, O E. ogri, M. S, H. Q, M. M, B. A. were responsible for the approval of the version of the manuscript to be published.

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Correspondence to M. Yamni.

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Yamni, M., Daoui, A., El ogri, O. et al. Fast and Accurate Computation of 3D Charlier Moment Invariants for 3D Image Classification. Circuits Syst Signal Process 40, 6193–6223 (2021). https://doi.org/10.1007/s00034-021-01763-0

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