3D image analysis by separable discrete orthogonal moments based on Krawtchouk and Tchebichef polynomials

•This paper introduces new sets of separable discrete moments for 3D image analysis.•This paper provides the process for deriving 3D moment invariants (scale, translation, rotation).•Numerical experiments are performed to demonstrate its validity and superiority. In this paper, we introduce new sets...

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Bibliographic Details
Published inPattern recognition Vol. 71; pp. 264 - 277
Main Authors Batioua, Imad, Benouini, Rachid, Zenkouar, Khalid, Zahi, Azeddine, Hakim, El Fadili
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.11.2017
Subjects
3D image analysis
Krawtchouk moments
Moment invariants
Multivariate discrete orthogonal polynomials
Numerical stability
Object classification
Separable moments
Tchebichef moments
3D image analysis
TTKMI
KKKM
Separable moments
Moment invariants
KKKMI
Object classification
TTKM
Multivariate discrete orthogonal polynomials
Krawtchouk moments
Tchebichef moments
ROI
TKKMI
TTTMI
Numerical stability
TKKM
TTTM
MSE
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Summary:•This paper introduces new sets of separable discrete moments for 3D image analysis.•This paper provides the process for deriving 3D moment invariants (scale, translation, rotation).•Numerical experiments are performed to demonstrate its validity and superiority. In this paper, we introduce new sets of separable discrete moments for 3D image analysis, named: TKKM (Tchebichef-Krawtchouk-Krawtchouk Moments) and TTKM (Tchebichef-Tchebichef-Krawtchouk Moments). Firstly, we present a detailed comparative study between the proposed separable 3D moments and the classical ones in terms of global feature extraction capability under noisy and noise-free conditions. Also, their local feature extraction ability is examined. Secondly, our study investigates the ability of the proposed separable 3D moments in pattern recognition. For this, new sets of separable 3D discrete moment invariants are introduced. The proposed rotation, scaling and translation 3D moment invariants have been rigorously tested under different sets of mixed transforms. The obtained results show that the representation capability, in comparison with traditional Krawtchouk and Tchebichef moments, has been significantly improved by using the new proposed 3D separable moments and can be highly useful in the field of 3D image analysis.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2017.06.013