Efficient 3D object classification by using direct Krawtchouk moment invariants
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 computa...
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| Published in | Multimedia tools and applications Vol. 77; no. 20; pp. 27517 - 27542 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.10.2018
Springer Nature B.V |
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| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. |
| Author | Qjidaa, Hassan Benouini, Rachid Najah, Said Batioua, Imad Zenkouar, Khalid |
| Author_xml | – sequence: 1 givenname: Rachid orcidid: 0000-0001-8586-5289 surname: Benouini fullname: Benouini, Rachid email: rachid.benouini@usmba.ac.ma organization: Laboratory of Intelligent Systems and Application (LSIA), Faculty of Sciences and Technology, University Sidi Mohamed Ben Abdellah – sequence: 2 givenname: Imad surname: Batioua fullname: Batioua, Imad organization: Laboratory of Intelligent Systems and Application (LSIA), Faculty of Sciences and Technology, University Sidi Mohamed Ben Abdellah – sequence: 3 givenname: Khalid surname: Zenkouar fullname: Zenkouar, Khalid organization: Laboratory of Intelligent Systems and Application (LSIA), Faculty of Sciences and Technology, University Sidi Mohamed Ben Abdellah – sequence: 4 givenname: Said surname: Najah fullname: Najah, Said organization: Laboratory of Intelligent Systems and Application (LSIA), Faculty of Sciences and Technology, University Sidi Mohamed Ben Abdellah – sequence: 5 givenname: Hassan surname: Qjidaa fullname: Qjidaa, Hassan organization: LESSI, Faculty of Sciences Dhar el Mehraz, Sidi Mohamed Ben Abdellah University |
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| Keywords | Direct method Moment invariants Krawtchouk moments Indirect method 3D object classification Numerical stability |
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| Snippet | 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... |
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| SubjectTerms | Classification Computer Communication Networks Computer Science Computing time Data Structures and Information Theory Experiments Image retrieval Invariants Multimedia Information Systems Noise Numerical stability Object recognition Pattern recognition Performance evaluation Robustness (mathematics) Special Purpose and Application-Based Systems |
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| Title | Efficient 3D object classification by using direct Krawtchouk moment invariants |
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