Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks
In order to achieve efficient recognition of 3D images and reduce the complexity of network parameters, we proposed a novel 3D image recognition method combining deep neural networks with fractional-order Chebyshev moments. Firstly, the fractional-order Chebyshev moment (FrCM) unit, consisting of Ch...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/7/2352 |
| _version_ | 1797211932081520640 |
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| author | Lin Gao Xuyang Zhang Mingrui Zhao Jinyi Zhang |
| author_facet | Lin Gao Xuyang Zhang Mingrui Zhao Jinyi Zhang |
| author_sort | Lin Gao |
| collection | DOAJ |
| description | In order to achieve efficient recognition of 3D images and reduce the complexity of network parameters, we proposed a novel 3D image recognition method combining deep neural networks with fractional-order Chebyshev moments. Firstly, the fractional-order Chebyshev moment (FrCM) unit, consisting of Chebyshev moments and the three-term recurrence relation method, is calculated separately using successive integrals. Next, moment invariants based on fractional order and Chebyshev moments are utilized to achieve invariants for image scaling, rotation, and translation. This design aims to enhance computational efficiency. Finally, the fused network embedding the FrCM unit (FrCMs-DNNs) extracts depth features to analyze the effectiveness from the aspects of parameter quantity, computing resources, and identification capability. Meanwhile, the Princeton Shape Benchmark dataset and medical images dataset are used for experimental validation. Compared with other deep neural networks, FrCMs-DNNs has the highest accuracy in image recognition and classification. We used two evaluation indices, mean square error (MSE) and peak signal-to-noise ratio (PSNR), to measure the reconstruction quality of FrCMs after 3D image reconstruction. The accuracy of the FrCMs-DNNs model in 3D object recognition was assessed through an ablation experiment, considering the four evaluation indices of accuracy, precision, recall rate, and F1-score. |
| first_indexed | 2024-04-24T10:34:20Z |
| format | Article |
| id | doaj.art-322adde2009c4587891fea3809ca81ce |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:34:20Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-322adde2009c4587891fea3809ca81ce2024-04-12T13:26:51ZengMDPI AGSensors1424-82202024-04-01247235210.3390/s24072352Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural NetworksLin Gao0Xuyang Zhang1Mingrui Zhao2Jinyi Zhang3School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaIn order to achieve efficient recognition of 3D images and reduce the complexity of network parameters, we proposed a novel 3D image recognition method combining deep neural networks with fractional-order Chebyshev moments. Firstly, the fractional-order Chebyshev moment (FrCM) unit, consisting of Chebyshev moments and the three-term recurrence relation method, is calculated separately using successive integrals. Next, moment invariants based on fractional order and Chebyshev moments are utilized to achieve invariants for image scaling, rotation, and translation. This design aims to enhance computational efficiency. Finally, the fused network embedding the FrCM unit (FrCMs-DNNs) extracts depth features to analyze the effectiveness from the aspects of parameter quantity, computing resources, and identification capability. Meanwhile, the Princeton Shape Benchmark dataset and medical images dataset are used for experimental validation. Compared with other deep neural networks, FrCMs-DNNs has the highest accuracy in image recognition and classification. We used two evaluation indices, mean square error (MSE) and peak signal-to-noise ratio (PSNR), to measure the reconstruction quality of FrCMs after 3D image reconstruction. The accuracy of the FrCMs-DNNs model in 3D object recognition was assessed through an ablation experiment, considering the four evaluation indices of accuracy, precision, recall rate, and F1-score.https://www.mdpi.com/1424-8220/24/7/2352fractional orderdeep neural networkChebyshev momentsimage recognition |
| spellingShingle | Lin Gao Xuyang Zhang Mingrui Zhao Jinyi Zhang Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks Sensors fractional order deep neural network Chebyshev moments image recognition |
| title | Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks |
| title_full | Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks |
| title_fullStr | Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks |
| title_full_unstemmed | Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks |
| title_short | Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks |
| title_sort | recognition of 3d images by fusing fractional order chebyshev moments and deep neural networks |
| topic | fractional order deep neural network Chebyshev moments image recognition |
| url | https://www.mdpi.com/1424-8220/24/7/2352 |
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