Application of ‘vision in the loop’ for inspection of lace fabric
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Cited by (27)
Integral images-based approach for fabric defect detection
2022, Optics and Laser TechnologyCitation Excerpt :To reduce computation time, they carried out the difference operation in the wavelet-domain instead of doing the subtraction in binary images mode. Yazdi, and King [21], Shankar and Zhong [26] used image difference method for defect detection on lace fabric and wafer surface, respectively. Their results showed that images difference method was very sensitive to noise and to shift variations, consequently there were many false alarms.
Fabric defect inspection based on isotropic lattice segmentation
2017, Journal of the Franklin InstituteFabric defect inspection based on lattice segmentation and Gabor filtering
2017, NeurocomputingCitation Excerpt :Sub-band selection for reconstruction is based on the analyses of the wavelet coefficient energies, and the defects are identified by thresholding the reconstructed images. AR-based method in [16] arranges pixels of an image to a single row and scans the pixel sequentially, the gray value of pixel under scanning is compared with the ideal value predicted by the 1D autoregressive model w. r. t. the previously-scanned pixels. The model parameters are trained by minimizing the prediction error for defect-free samples.
A timely detection of a coated board streak defect in subsampling conditions using monochrome vision system
2012, AEU - International Journal of Electronics and CommunicationsCitation Excerpt :Since the installed system uses 2D cameras it satisfies the primary requirement for detecting streaks. Systems capable of detecting a 1 mm × 1 mm size defect typically operate at a 1.5 oversampling rate [7]. Since the vision system resolution is 1 pixel/mm of coated board, this means it would operate in the subsampling condition, since Nyquist sampling theorem is not satisfied as defined in [3].
Automated fabric defect detection-A review
2011, Image and Vision ComputingCitation Excerpt :Section 7 addresses the future direction and conclusions. As observed from real implementation of fabric inspection systems, it is necessary to consider factors such as (1) contrast between defects and texture surface [20,37]: e.g., a low contrast in image easily leads to a misclassification; (2) consistency of texture background [37]: e.g., color difference and distortion along a texture affect image acquisition; (3) resolution of input image [19,37–41]: e.g., a low resolution image cannot show fine defects in fabric; (4) alignment of input image [3,42,43]: e.g., misalignment in image acquisition induces false defect detection in template matching approach; (5) size [37,44], and shape [45–47] of defects: e.g., defect of small size or defect similar to a pattern shape increases difficulties in recognition; (6) speed or computation complexity of defect detection [37,48,49]: e.g., long learning delays may not be practical; (7) lighting [13]: e.g., improper illumination yields poor resolution and contrast; and (8) image acquisition techniques: e.g., most inspection methods use digital cameras to capture images. However, alternative approaches are also available, such as near-infrared (NIR) [50], X-ray, multispectral imaging and polarimetry, which may provide extra features in defect detection.
An eigenvalue-based similarity measure and its application in defect detection
2005, Image and Vision Computing