RECOGNITION METHOD OF METAL FRACTURE IMAGES BASED ONEMPIRICAL RIDGELET-2DPCA
The empirical ridgelet transform has the ability of direction selectivity and adaptive decomposition. 2 DPCA can directly use the original image toconstruct the covariance matrix. Combined with the advantages of Empirical ridgelet transform and 2 DPCA, anidentification method ofmetal fracture based...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2019-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.04.012 |
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| Summary: | The empirical ridgelet transform has the ability of direction selectivity and adaptive decomposition. 2 DPCA can directly use the original image toconstruct the covariance matrix. Combined with the advantages of Empirical ridgelet transform and 2 DPCA, anidentification method ofmetal fracture based onempirical ridgelet-2 DPCA. At the same time, the proposed method is compared with the Ridgelet-2 DPCA, Ridgelet-PCA recognition method. The experimental results show that the bidimensional intrinsic mode Function（BIMF） component has more abundant feature information than ridgelet coefficient. 2 DPCA has more complete image structure informationthan PCA. Therefore, the proposed empirical Ridgelet-2 DPCA has achieved better recognition effect than experience Ridgelet-PCA and Ridgelet-2 DPCA recognition method. |
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| ISSN: | 1001-9669 |