A method for wood surface defect detection based on mixed texture features

YIN Jian-xin; QI Heng-nian; FENG Hai-lin; DU Xiao-chen

doi:10.11833/j.issn.2095-0756.2011.06.017

Volume 28 Issue 6

Dec. 2011

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YIN Jian-xin, QI Heng-nian, FENG Hai-lin, et al. A method for wood surface defect detection based on mixed texture features[J]. Journal of Zhejiang A&F University, 2011, 28(6): 937-942. DOI: 10.11833/j.issn.2095-0756.2011.06.017

Citation:

YIN Jian-xin, QI Heng-nian, FENG Hai-lin, et al. A method for wood surface defect detection based on mixed texture features[J]. Journal of Zhejiang A&F University, 2011, 28(6): 937-942. DOI: 10.11833/j.issn.2095-0756.2011.06.017

A method for wood surface defect detection based on mixed texture features

DOI: 10.11833/j.issn.2095-0756.2011.06.017

1.
School of Information and Engineering，Zhejiang A & F University，Lin’an 311300，Zhejiang，China

Publish Date: 2011-12-20

Abstract

It is important to detect the wood surface defects using computer vision technology. In this paper，a defect detection method which can accurately and robustly determine whether there is defect on wood surface image or not is proposed based on mixed texture features. At first，gray level co-occurrence matrix(GLCM)，Gabor filtering and invariant moment method are used to extract 10 image scale，translation，rotation invariant and texture features optimally. Then，feature vectors are mixed effectively. Finally，BP artificial neural network is used to train the sample sets and detection based on the mixed texture features. Experiments show that the proposed method can detect surface defects of wood boards accurately and the average success rate of detection is 96.2%.［Ch，4 fig. 1 tab. 12 ref.］
- forest engineering,
- gray level co-occurrence matrix (GLCM),
- gabor filter,
- invariant moment,
- wood image,
- defect detection

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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A method for wood surface defect detection based on mixed texture features

1. School of Information and Engineering，Zhejiang A & F University，Lin’an 311300，Zhejiang，China

Publish Date: 2011-12-20

Key words:

forest engineering /
gray level co-occurrence matrix (GLCM) /
gabor filter /
invariant moment /
wood image /
defect detection

Abstract: It is important to detect the wood surface defects using computer vision technology. In this paper，a defect detection method which can accurately and robustly determine whether there is defect on wood surface image or not is proposed based on mixed texture features. At first，gray level co-occurrence matrix(GLCM)，Gabor filtering and invariant moment method are used to extract 10 image scale，translation，rotation invariant and texture features optimally. Then，feature vectors are mixed effectively. Finally，BP artificial neural network is used to train the sample sets and detection based on the mixed texture features. Experiments show that the proposed method can detect surface defects of wood boards accurately and the average success rate of detection is 96.2%.［Ch，4 fig. 1 tab. 12 ref.］

A method for wood surface defect detection based on mixed texture features

DOI: 10.11833/j.issn.2095-0756.2011.06.017

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Related

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