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Image recognition using new set of separable three-dimensional discrete orthogonal moment invariants

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Abstract

In this paper, we propose new sets of 3D separable discrete orthogonal moment invariants, named Racah-Tchebichef-Krawtchouk Moment Invariants (RTKMI), Racah-Krawtchouk-Krawtchouk Moment Invariants (RKKMI) and Racah-Racah-Kr-awtchouk Moment Invariants (RRKMI), for 3D image recognition. The basis functions of these new sets of moment invariants are represented by multivariate discrete orthogonal polynomials. We also present theoretical framework to derive their Rotation, Scaling and Translation (RST) invariants based on the 3D geometric moment invariants. Accordingly, the performance of these proposed separable moment invariants is evaluated under heterogeneous databases and through several appropriate experiments, including 3D image invariance against geometric deformations, local feature extraction, computation time and recognition accuracy, in comparison with the traditional moment invariants. The obtained results showed that our proposed separable moment invariant are very efficient in terms of object recognition, numerical stability and local feature extraction, and can be highly useful for computer vision applications.

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Abbreviations

RTKMI ::

Racah-Tchebichef-Krawtchouk Moment Invariants

RKKMI ::

Racah-Krawtchouk-Krawtchouk Moment Invariants

RRKMI ::

Racah-Racah-Krawtch-ouk Moment Invariants

TTTMI ::

Tchebichef-Tchebichef-Tchebichef Moment Invariants

KKKMI ::

Krawtchouk-Krawtchouk-Krawtchouk Moment Invariants

RRRMI ::

Racah- Racah- Racah Moment Invariants

DOMI ::

Discrete Orthogonal Moment Invariant

DOM ::

Discrete Orthogonal Moment

References

  1. Abdalbari A, Ren J, Green M (2016) Seeds classification for image segmentation based on 3-D affine moment invariants. Biomed Eng Lett 6:224–233. https://doi.org/10.1007/s13534-016-0225-3

    Article  Google Scholar 

  2. Abramowitz M, Stegun IA (1964) Handbook of mathematical functions: with formulas, graphs, and mathematical tables Courier Corporation

  3. Abu-Mostafa YS, Psaltis D (1985) Image normalization by complex moments. IEEE Trans Pattern Anal Mach Intell, pp 46–55

    Article  Google Scholar 

  4. Batioua I, Benouini R, Zenkouar K, El Fadili H (2017) Image analysis using new set of separable two-dimensional discrete orthogonal moments based on Racah polynomials. EURASIP Journal on Image and Video Processing 2017:20

    Article  Google Scholar 

  5. Batioua I, Benouini R, Zenkouar K, Zahi A, El Fadili H (2017) 3D image analysis by separable discrete orthogonal moments based on Krawtchouk and Tchebichef polynomials Pattern Recognition

  6. Bayraktar B, Bernas T, Robinson JP, Rajwa B (2007) A numerical recipe for accurate image reconstruction from discrete orthogonal moments. Pattern Recogn 40:659–669

    Article  Google Scholar 

  7. Beals R, Wong R (2016) Special functions and orthogonal polynomials. Cambridge University Press

  8. Bhatnagar G, Wu QMJ, Raman B (2013) Discrete fractional wavelet transform and its application to multiple encryption. Inf Sci 223:297–316

    Article  MathSciNet  Google Scholar 

  9. Bouziane A, Chahir Y, Molina M, Jouen F (2013) Unified framework for human behaviour recognition: An approach using 3D Zernike moments. Neurocomputing 100:107–116. https://doi.org/10.1016/j.neucom.2011.12.042

    Article  Google Scholar 

  10. Broggio D, Moignier A, Ben Brahim K, Gardumi A, Grandgirard N, Pierrat N, Chea M, Derreumaux S, Desbree A, Boisserie G, Aubert B, Mazeron JJ, Franck D (2013) Comparison of organs’ shapes with geometric and Zernike 3D moments. Comput Methods Prog Biomed 111:740–754. https://doi.org/10.1016/j.cmpb.2013.06.005

    Article  Google Scholar 

  11. Bujack R, Hlawitschka M, Scheuermann G, Hitzer E, Customized TRS (2014) Invariants for 2D vector fields via moment normalization. Pattern Recogn Lett 46:46–59. https://doi.org/10.1016/j.patrec.2014.05.005

    Article  Google Scholar 

  12. Chen D-Y, Tian X-P, Shen Y-T, Ouhyoung M (2003) On visual similarity based 3D model retrieval. In: Computer graphics forum. Wiley Online Library, pp 223–232

  13. Cheng H, Chung SM (2016) Orthogonal moment-based descriptors for pose shape query on 3D point cloud patches. Pattern Recogn 52:397–409. https://doi.org/10.1016/j.patcog.2015.09.028

    Article  Google Scholar 

  14. Chong C-W, Raveendran P, Mukundan R (2004) Translation and scale invariants of Legendre moments. Pattern Recogn 37:119–129

    Article  Google Scholar 

  15. Comtet L (2012) Advanced combinatorics: The art of finite and infinite expansions. Springer Science & Business Media

  16. Erdelyi A, Magnus V, Oberhettinger F, Tricomi F (1953) Higher Transcendental Functions. McGraw Hill

  17. Fang R, Godil A, Li X, Wagan A (2008) A new shape benchmark for 3D object retrieval. Advances in Visual Computing, pp 381–392

  18. Flusser J, Suk T, Zitova B (2016) 2D and 3D Image Analysis by Moments. Wiley, Hoboken

    Book  Google Scholar 

  19. Galvez JM, Canton M (1993) Normalization and shape recognition of three-dimensional objects by 3D moments. Pattern Recogn 26:667–681. https://doi.org/10.1016/0031-3203(93)90120-L

    Article  Google Scholar 

  20. Hao Y, Li Q, Mo H, et al. (2018) AMI-Net: convolution neural networks with affine moment invariants. IEEE Signal Process Lett 25:1064–1068

    Article  Google Scholar 

  21. Hmimid A, Sayyouri M, Qjidaa H (2015) Fast computation of separable two-dimensional discrete invariant moments for image classification. Pattern Recogn 48:509–521

    Article  Google Scholar 

  22. Hosny KM (2012) Fast computation of accurate Gaussian-Hermite moments for image processing applications. Digit Signal Process 22:476–485

    Article  MathSciNet  Google Scholar 

  23. Karakasis EG, Papakostas GA, Koulouriotis DE, Tourassis VD (2013) Generalized dual Hahn moment invariants. Pattern Recogn 46:1998–2014

    Article  Google Scholar 

  24. Koehl P (2012) Fast recursive computation of 3d geometric moments from surface meshes. IEEE Trans Pattern Anal Mach Intell 34:2158–2163. https://doi.org/10.1109/TPAMI.2012.23

    Article  Google Scholar 

  25. Li E, Mo H, Xu D, Li H (2019) Image projective invariants. IEEE Trans Pattern Anal Mach Intell 41:1144–1157

    Article  Google Scholar 

  26. Mallahi ME, Mesbah A, Fadili HE, Zenkouar K, Qjidaa H (2015) . Image analysis by discrete orthogonal Tchebichef Moments for 3D Object Representation Tchebichef moments computation 14:513–525

    Google Scholar 

  27. Mangin JF, Poupon F, Duchesnay E, Riviere D, Cachia A, Collins DL, Evans AC, Regis J (2004) Brain morphometry using 3D moment invariants. Med Image Anal 8:187–196. https://doi.org/10.1016/j.media.2004.06.016

    Article  Google Scholar 

  28. Mesbah A, Berrahou A, Hammouchi H, et al. (2019) Lip reading with Hahn convolutional neural networks. Image Vis Comput 88:76–83

    Article  Google Scholar 

  29. Mukundan R (2004) Some computational aspects of discrete orthonormal moments. IEEE Trans On Image Process 13:1055–1059

    Article  MathSciNet  Google Scholar 

  30. Mukundan R, Ong SH, Lee PA (2001) Image analysis by Tchebichef moments. IEEE Transactions on Image Processing 10:1357–1364

    Article  MathSciNet  Google Scholar 

  31. Nie L, Yan S, Wang M, Hong§ R, Chua T-S Harvesting Visual Concepts for Image Search with Complex Queries. MM’12, October 29-November 2, 2012, Nara, Japan. Copyright 2012 ACM 978-1-4503-1089-5/12/10

  32. Nikiforov AF, Suslov SK (1986) Classical orthogonal polynomials of a discrete variable on nonuniform lattices. Lett Math Phys 11:27–34

    Article  MathSciNet  Google Scholar 

  33. Nikiforov AF, Uvarov VB, Suslov SK (1991) Classical orthogonal polynomials of a discrete variable. In: Classical Orthogonal Polynomials of a Discrete Variable, Springer, pp 18–54

  34. Papakostas GA (2014) Over 50 years of image moments and moment invariants. Moments Moment Invariants-Theor App, pp 3–32

  35. Papakostas GA, Boutalis YS, Karras DA, Mertzios BG (2009) Modified factorial-free direct methods for Zernike and pseudo-Zernike moment computation. IEEE Trans Instrum Meas 58:2121–2131

    Article  Google Scholar 

  36. Papakostas GA, Boutalis YS, Papaodysseus CN, Fragoulis DK (2008) Numerical stability of fast computation algorithms of Zernike moments. Appl Math Comput 195:326–345. https://doi.org/10.1016/j.amc.2007.04.110

    Article  MathSciNet  MATH  Google Scholar 

  37. Patil S, Ravi B (2005) Voxel-based representation, display and thickness analysis of intricate shapes. In: Ninth International Conference on Computer Aided Design and Computer Graphics (CAD-CG’05). IEEE, pp 6-pp.

  38. Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 652–660

  39. Sadjadi FA, Hall EL (1980) Three-dimensional moment invariants. IEEE Transactions On Pattern Analysis and Machine Intelligence, pp 127–136

    Article  Google Scholar 

  40. Sayyouri M, Hmimid A, Qjidaa H (2013) Improving the performance of image classification by Hahn moment invariants. JOSA A 30:2381–2394

    Article  Google Scholar 

  41. Singh C, Walia E, Upneja R (2012) Analysis of algorithms for fast computation of pseudo Zernike moments and their numerical stability. Digit Signal Process 22:1031–1043

    Article  MathSciNet  Google Scholar 

  42. Suk T, Flusser J (2011) Tensor method for constructing 3D moment invariants, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS 6855:212–219. https://doi.org/10.1007/978-3-642-23678-5_24

    Article  Google Scholar 

  43. Teague MR (1980) Image analysis via the general theory of moments. J Opt Soc Am 70:920–930. https://doi.org/10.1364/JOSA.70.000920

    Article  MathSciNet  Google Scholar 

  44. Xiao B, Zhang Y, Li L, Li W, Wang G (2016) Explicit Krawtchouk moment invariants for invariant image recognition. J Electron Imaging 25:023002–023002

    Article  Google Scholar 

  45. Xu D, Li H (2008) Geometric moment invariants. Pattern Recogn 41:240–249. https://doi.org/10.1016/j.patcog.2007.05.001

    Article  MATH  Google Scholar 

  46. Yang B, Flusser J, Suk T (2015) 3D rotation invariants of Gaussian-Hermite moments. Pattern Recogn Lett 54:18–26

    Article  Google Scholar 

  47. Yap P-T, Paramesran R, Ong S-H (2003) Image analysis by Krawtchouk moments. IEEE Trans Image Process 12:1367–1377

    Article  MathSciNet  Google Scholar 

  48. You X, Du L, Cheung Y-M, Chen Q (2010) A blind watermarking scheme using new nontensor product wavelet filter banks. IEEE Trans Image Process 19(12):3271–3284

    Article  MathSciNet  Google Scholar 

  49. Zhu H (2012) Image representation using separable two-dimensional continuous and discrete orthogonal moments. Pattern Recogn 45:1540–1558

    Article  Google Scholar 

  50. Zhu H, Shu H, Liang J, Luo L, Coatrieux J-L (2007) Image analysis by discrete orthogonal Racah moments. Signal Processing 87:687–708

    Article  Google Scholar 

  51. Zhu H, Shu H, Xia T, Luo L, Coatrieux JL (2007) Translation and scale invariants of Tchebichef moments. Pattern Recogn 40:2530–2542

    Article  Google Scholar 

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Acknowledgements

The authors would to thank the Laboratory of Intelligent Systems and Applications for his support to achieve this work.

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Correspondence to Imad Batioua.

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Batioua, I., Benouini, R. & Zenkouar, K. Image recognition using new set of separable three-dimensional discrete orthogonal moment invariants. Multimed Tools Appl 79, 13217–13245 (2020). https://doi.org/10.1007/s11042-019-08083-1

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