Warp-knitted fabric defect segmentation based on the non-subsampled wavelet-packet-based Contourlet transform

Dong, Xia; Jiang, Gaoming; Ma, Pibo

doi:10.1177/0040517515619356

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The HKU database was created by the Industrial Automation Research Laboratory. 16 It involves three types of regular patterned fabric: box-, star-, and dot-patterned fabric. All samples in the HKU database are 256 × 256 pixels with 24-bit depth.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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Fabric defect detection based on low-rank decomposition with factor group-sparse regularizer

Cao

Han

Xiao

2023

Textile Research Journal

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Recently, many low-rank-based methods in terms of detecting defects in fabric images have been proposed. However, there are two disadvantages of these methods. First, current low-rank based methods use the nuclear norm as the surrogate of rank, which causes inefficient optimization process and sub-optimal performance. Second, low-rank defective regions cannot be detected by low-rank based models. Thus, we propose a factor group-sparse regularized low-rank decomposition model (FGSRLRD) to solve these problems. This method takes the factor group-sparse regularizer as the surrogate of rank, which is more efficient as singular value decomposition (SVD) is not applied in the optimization process. Better performance is achieved as the factor group-sparse regularizer is a more accurate approximation of the rank. In addition, the weight matrix generated by the lightweight autoencoder is incorporated into the object function of FGSRLRD to guide locating defective regions. Besides, as low-rank defective regions cannot be segmented by low-rank models, this method constructs a fusion image of the prior image and the sparse image to highlight the defective regions. The performance of the proposed method is evaluated on two standard datasets, and the results indicate that the suggested method outperforms the existing state-of-the-art methods in locating the defective regions on fabric images.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…The methods involved include Fourier,12,13 Gabor, 14,15 and Wavelet transforms. 16 The Fourier transform is insensitive to tiny flaws as a global transform, 17 while both the Gabor and Wavelet transforms can provide frequency and local spatial information. The accuracy of such transformations is affected by the filter responses to any defects present.…”

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confidence: 99%