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JACIII Vol.23 No.2 pp. 175-182
doi: 10.20965/jaciii.2019.p0175
(2019)

Paper:

Feature Extraction of Foam Nickel Surface Based on Multi-Scale Texture Analysis

Jianqi Li*,**,†, Binfang Cao*, Fangyan Nie*, and Minhan Zhu*,**

*Hunan Province Cooperative Innovation Center for the Construction & Development of Dongting Lake Ecological Economic Zone,
Hunan University of Arts and Science
3150 Dong Ting Road, Changde, Hunan 415000, China

**College of Electrical and Information Engineering, Hunan University of Arts and Science
3150 Dong Ting Road, Changde, Hunan 415000, China

Corresponding author

Received:
November 26, 2017
Accepted:
October 2, 2018
Published:
March 20, 2019
Keywords:
foam nickel, texture features, defect recognition, NSCT, DAG-SVM
Abstract

In the foam nickel process, texture is the indicator of foam nickel performance. In order to recognize foam nickel surface defects accurately and provide guidance for production operations, this paper proposes a method for extracting foam nickel image textures based on multi-scale texture analysis. First, nonsubsampled contourlet (NSCT) is used to carry out foam nickel image multi-scale decomposition, and the low-frequency and high-frequency components following decomposition are used to characterize different defect details. Then, the Haralick eigenvalue, which measures the foam nickel image texture information at each sub-band, is calculated. The KPCA and optimal DAG-SVM are adopted in order to reduce the parameter dimension and clarify defects. Tests are carried out on the foam nickel surface image samples, including crack, scratch, pollution, leakage, and indentation tests. The results indicate that the method proposed in this paper can extract different pieces of detailed texture information and can achieve a defect-identifying accuracy of up to 88.9%.

Cite this article as:
J. Li, B. Cao, F. Nie, and M. Zhu, “Feature Extraction of Foam Nickel Surface Based on Multi-Scale Texture Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.2, pp. 175-182, 2019.
Data files:
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Last updated on Nov. 08, 2019