Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machine
The useful information extracted from fracture images is the most fundamental problem of quantitative analysis and intelligent diagnosis of metal fracture. The pattern recognition or classification is the critical issue of failure analysis of metal fracture. In this paper, combining wavelet transfor...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2016-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1051/matecconf/20163902004 |
| _version_ | 1818343965493035008 |
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| author | Sun Yi Li Zhinong Yan Jingwen |
| author_facet | Sun Yi Li Zhinong Yan Jingwen |
| author_sort | Sun Yi |
| collection | DOAJ |
| description | The useful information extracted from fracture images is the most fundamental problem of quantitative analysis and intelligent diagnosis of metal fracture. The pattern recognition or classification is the critical issue of failure analysis of metal fracture. In this paper, combining wavelet transform, kurtosis with relevance vector machine (RVM), a new recognition method based on wavelet kurtosis and RVM, which is named wavelet kurtosis-RVM, is proposed. In the proposed method, wavelet kurtosis is used as a feature vector, and RVM as a classifier. The proposed method has been successfully applied to the recognition of fracture images. The proposed method is also compared with the wavelet entropy-RVM recognition method and wavelet kurtosis-SVM recognition method. The experiment result shows that the proposed method is very effective. Compared with the Wavelet entropy-RVM recognition method, Wavelet kurtosis is more sensitive to the texture change of metal fracture and suitable for feature extraction of metal fracture. Compared with the Wavelet kurtosis-SVM recognition method, The proposed method and Wavelet kurtosis-SVM recognition method have the same good recognition rate. However, in the recognition speed, the Wavelet kurtosis-RVM recognition method is obviously superior to the Wavelet kurtosis-SVM recognition method, especially in the increase of training samples. |
| first_indexed | 2024-12-13T16:38:58Z |
| format | Article |
| id | doaj.art-f163453d09c84c46b860821c2cdf4118 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-13T16:38:58Z |
| publishDate | 2016-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-f163453d09c84c46b860821c2cdf41182022-12-21T23:38:19ZengEDP SciencesMATEC Web of Conferences2261-236X2016-01-01390200410.1051/matecconf/20163902004matecconf_iccme2016_02004Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machineSun Yi0Li Zhinong1Yan Jingwen2Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong UniversityKey Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong UniversityColleage of Engineering, Shantou UniversityThe useful information extracted from fracture images is the most fundamental problem of quantitative analysis and intelligent diagnosis of metal fracture. The pattern recognition or classification is the critical issue of failure analysis of metal fracture. In this paper, combining wavelet transform, kurtosis with relevance vector machine (RVM), a new recognition method based on wavelet kurtosis and RVM, which is named wavelet kurtosis-RVM, is proposed. In the proposed method, wavelet kurtosis is used as a feature vector, and RVM as a classifier. The proposed method has been successfully applied to the recognition of fracture images. The proposed method is also compared with the wavelet entropy-RVM recognition method and wavelet kurtosis-SVM recognition method. The experiment result shows that the proposed method is very effective. Compared with the Wavelet entropy-RVM recognition method, Wavelet kurtosis is more sensitive to the texture change of metal fracture and suitable for feature extraction of metal fracture. Compared with the Wavelet kurtosis-SVM recognition method, The proposed method and Wavelet kurtosis-SVM recognition method have the same good recognition rate. However, in the recognition speed, the Wavelet kurtosis-RVM recognition method is obviously superior to the Wavelet kurtosis-SVM recognition method, especially in the increase of training samples.http://dx.doi.org/10.1051/matecconf/20163902004Wavelet transformKurtosisRelevance vector machine (RVM)Feature extractionPattern recognitionMetal fractureWavelet entropySupport vector machine (SVM) |
| spellingShingle | Sun Yi Li Zhinong Yan Jingwen Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machine MATEC Web of Conferences Wavelet transform Kurtosis Relevance vector machine (RVM) Feature extraction Pattern recognition Metal fracture Wavelet entropy Support vector machine (SVM) |
| title | Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machine |
| title_full | Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machine |
| title_fullStr | Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machine |
| title_full_unstemmed | Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machine |
| title_short | Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machine |
| title_sort | recognition method of metal fracture images based on wavelet kurtosis and relevance vector machine |
| topic | Wavelet transform Kurtosis Relevance vector machine (RVM) Feature extraction Pattern recognition Metal fracture Wavelet entropy Support vector machine (SVM) |
| url | http://dx.doi.org/10.1051/matecconf/20163902004 |
| work_keys_str_mv | AT sunyi recognitionmethodofmetalfractureimagesbasedonwaveletkurtosisandrelevancevectormachine AT lizhinong recognitionmethodofmetalfractureimagesbasedonwaveletkurtosisandrelevancevectormachine AT yanjingwen recognitionmethodofmetalfractureimagesbasedonwaveletkurtosisandrelevancevectormachine |