15 November 2012 Feature extraction based on contourlet transform and its application to surface inspection of metals
Yonghao Ai, Ke Xu
Author Affiliations +
Abstract
Surface defects that affect the quality of metals are an important factor. Machine vision systems commonly perform surface inspection, and feature extraction of defects is essential. The rapidity and universality of the algorithm are two crucial issues in actual application. A new method of feature extraction based on contourlet transform and kernel locality preserving projections is proposed to extract sufficient and effective features from metal surface images. Image information at certain direction is important to recognition of defects, and contourlet transform is introduced for its flexible direction setting. Images of metal surfaces are decomposed into multiple directional subbands with contourlet transform. Then features of all subbands are extracted and combined into a high-dimensional feature vector, which is reduced to a low-dimensional feature vector by kernel locality preserving projections. The method is tested with a Brodatz database and two surface defect databases from industrial surface-inspection systems of continuous casting slabs and aluminum strips. Experimental results show that the proposed method performs better than the other three methods in accuracy and efficiency. The total classification rates of surface defects of continuous casting slabs and aluminum strips are up to 93.55% and 92.5%, respectively.
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2012/$25.00 © 2012 SPIE
Yonghao Ai and Ke Xu "Feature extraction based on contourlet transform and its application to surface inspection of metals," Optical Engineering 51(11), 113605 (15 November 2012). https://doi.org/10.1117/1.OE.51.11.113605
Published: 15 November 2012
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CITATIONS
Cited by 23 scholarly publications.
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KEYWORDS
Feature extraction

Aluminum

Metals

Image classification

Databases

Inspection

Computed tomography

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