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25 April 2008 Flaw detection and segmentation in textile inspection
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Abstract
We present a new method to automatically segment local defects in a woven fabric that does not require any additional defect-free reference for comparison. Firstly, the structural features of the repetition pattern of the minimal weave repeat are extracted from the Fourier spectrum of the sample under inspection. The corresponding peaks are automatically identified and removed from the fabric frequency spectrum. Secondly, we define a set of multi-scale oriented bandpass filters, adapted to the specific structure of the sample, that operate in the Fourier domain. The filter design is the key part of the method. Using the set of filters, local defects can be extracted. Thirdly, the filtered images obtained at different scales are inverse Fourier transformed, binarized and merged to obtain an output image where flaws are segmented from the fabric background. The method can be applied to fabrics of uniform color as well as to fabrics woven with threads of different colors. It is Euclidean motion invariant and texture adaptive and it is useful for automatic inspection both online and off-line. The whole process is fully automatic and can be implemented either optical or electronically. A variety of experimental results are presented and discussed.
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María S. Millán, Miquel Ralló, and Jaume Escofet "Flaw detection and segmentation in textile inspection", Proc. SPIE 7000, Optical and Digital Image Processing, 70000A (25 April 2008); https://doi.org/10.1117/12.786528
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