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Multimedia Tools and Applications

, Volume 77, Issue 20, pp 27517–27542 | Cite as

Efficient 3D object classification by using direct Krawtchouk moment invariants

  • Rachid Benouini
  • Imad Batioua
  • Khalid Zenkouar
  • Said Najah
  • Hassan Qjidaa
Article
  • 84 Downloads

Abstract

In this paper, we present an efficient set of moment invariants, named Direct Krawtchouk Moment Invariants (DKMI), for 3D objects recognition. This new set of invariants can be directly derived from the Krawtchouk moments, based on algebraic properties of Krawtchouk polynomials. The proposed computation approach is effectively compared with the classical method, which rely on the indirect computation of moment invariants by using the corresponding geometric moment invariants. Several experiments are carried out so as to evaluate the performance of the newly introduced invariants. Invariability property and noise robustness are firstly investigated. Secondly, the numerical stability is discussed. Then, the performance of the proposed moment invariants as pattern features for 3D object classification is compared with the existing Geometric, Krawtchouk, Tchebichef and Hahn Moment Invariants. Finally, a comparative analysis of computational time of these moment invariants is illustrated. The obtained results demonstrate the efficiency and the superiority of the proposed method.

Keywords

Moment invariants Krawtchouk moments Direct method Indirect method 3D object classification Numerical stability 

Abbreviations

DKMI

Direct Krawtchouk Moment Invariants

TMI

Tchebichef Moment Invariants

KMI

Krawtchouk Moment Invariants

HMI

Hahn Moment Invariants

GMI

Geometric Moment Invariantsx

TRR

Time Reduction Rate

ROI

Region Of Interest

RST

Rotation, Scaling and Translation

Notes

Acknowledgments

The authors thankfully acknowledge the Laboratory of Intelligent Systems and Applications (LSIA) for his support to achieve this work.

Funding Information

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Compliance with Ethical Standards

Conflict of interests

The authors declare no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of Intelligent Systems and Application (LSIA), Faculty of Sciences and TechnologyUniversity Sidi Mohamed Ben AbdellahFezMorocco
  2. 2.LESSI, Faculty of Sciences Dhar el MehrazSidi Mohamed Ben Abdellah UniversityFezMorocco

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