MATEC Web of Conferences
Volume 39, 20162015 2nd International Conference on Chemical and Material Engineering (ICCME 2015)
|Number of page(s)||5|
|Section||Alloy production and processing|
|Published online||13 January 2016|
Recognition method of metal fracture images based on Wavelet kurtosis and Relevance vector machine
1 Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang, China
2 Colleage of Engineering, Shantou University, Shantou, China
a Corresponding author: email@example.com
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.
Key words: Wavelet transform / Kurtosis / Relevance vector machine (RVM) / Feature extraction / Pattern recognition / Metal fracture / Wavelet entropy / Support vector machine (SVM)
© Owned by the authors, published by EDP Sciences, 2016
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