Fabric Defect Detection Scheme Based on Gabor Filter and PCA

Shu Min Ding; Chun Lei Li; Zhou Feng Liu

doi:10.4028/www.scientific.net/AMR.482-484.159

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 482-484Fabric Defect Detection Scheme Based on Gabor...

Fabric Defect Detection Scheme Based on Gabor Filter and PCA

Abstract:

Gabor feature is one of the features which have been used for texture classification. In this paper, we propose a novel fabric detect detection scheme based on Gabor filter and PCA. The fabric image is split into image blocks, and then different Gabor filter banks are applied into each image blocks. A feature vector is generated by concatenating all the Gabor features with different directions and scales for each image block. Principal component analysis (PCA) is adopted to reduce the dimension of the Gabor feature vector. In the end, SVM can classify each image block as non-defective and defective. Experimental results demonstrate the efficiency of our proposed algorithm. Because of its simplicity, online implementation is possible as well.

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Periodical:

Advanced Materials Research (Volumes 482-484)

Pages:

159-163

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.482-484.159

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Online since:

February 2012

Authors:

Shu Min Ding, Chun Lei Li, Zhou Feng Liu

Keywords:

Fabric Defect, Gabor, Principal Component Analysis (PCA), Support Vector Machine (SVM)

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References

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