29 August 2016 Fabric defect detection algorithm based on Gabor filter and low-rank decomposition
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100330L (2016) https://doi.org/10.1117/12.2244861
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
In order to accurately detect the fabric defects in production process, an effective fabric detection algorithm based on Gabor filter and low-rank decomposition is proposed. Firstly, the Gabor filter features with multi-scale and multiple directions are extracted from the fabric image, then the extracted Gabor feature maps are divided into the blocks with size 16×16 by uniform sampling; secondly, we calculate the average feature vector for each block, and stack the feature vectors of all blocks into a feature matrix; thirdly, an efficient low rank decomposition model is built for feature matrix, and is divided into a low-rank matrix and a sparse matrix by the accelerated proximal gradient approach (APG). Finally, the saliency map generated by sparse matrix is segmented by the improved optimal threshold algorithm, to locate the defect regions. Experiment results show that low-rank decomposition can effectively detect fabric defect, and outperforms the state-of-the-art methods.
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Duo Zhang, Guangshuai Gao, Chunlei Li, "Fabric defect detection algorithm based on Gabor filter and low-rank decomposition", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100330L (29 August 2016); doi: 10.1117/12.2244861; https://doi.org/10.1117/12.2244861
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