Bibliographic details

Computers in Industry, 65(2):325-332
doi: 10.1016/j.compind.2013.12.001


We present a two-step computer vision procedure for detecting and characterizing impurities in paper. The method is based on a preliminary classification step to differentiate defective paper patches (i.e.: with impurities) from non-defective ones (i.e.: with no impurities), followed by a thresholding step to separate the impurities from the background. This approach permits to avoid the artifacts that occurs when thresholding is applied to paper samples that contain no impurities. We discuss and compare different solutions and methods to implement the procedure. We experimentally validate the proposed approach on a datasets of 11 paper classes and show that average accuracy beyond 96% and 99% can be obtained in the two steps, respectively.

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