Fabric defect detection using Discrete Curvelet Transform

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With the increasing client demand for cloth selection in the fashion market, fabric texture becomes far more numerous, that brings nice challenges to correct fabric discover detection. A comparative study of the GLCM-based, wavelet-based additionally the curvelet-based techniques has also been enclosed. The high accuracy achieved by the planned technique suggests an economical resolution for fabric defect. Note that, this study is that the initial documented arrange to explore the probabilities of a brand new multiresolution analysis tool referred to as digital curvelet transform to deal with the matter of material defect. The recognizer acquires digital fabric pictures by image acquisition device and converts that image into the binary image using »Discrete Curvelet Transform». The proposed algorithmic rule is simulated in MATLAB. The performance of the proposed defect detection model was evaluated through in-depth experiments with varied kinds of real fabric samples. The planned detection model was tried to be effective and be superior to some representative detection models in terms of the detection accuracy and false alarm.




Anandan, P., & Sabeenian, R. S. (2018). Fabric defect detection using Discrete Curvelet Transform. In Procedia Computer Science (Vol. 133, pp. 1056–1065). Elsevier B.V. https://doi.org/10.1016/j.procs.2018.07.058

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