A sequential machine vision procedure for assessing paper impurities

• We present an innovative machine vision procedure for detecting particles and impurities in paper.
• A preliminary step separates defective paper patches from the non-defective ones.
• Defective patches are further analysed to assess the extension of the impurities.
• Combination of the two steps is highly effective providing over 96% accuracy.

We present a sequential, two-step procedure based on machine vision 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 which occur when thresholding is applied to paper samples that contain no impurities. We discuss and compare different solutions and methods to implement the procedure and experimentally validate it on a datasets of 11 paper classes. The results show that a marked increase in detection accuracy can be obtained with the two-step procedure in comparison with thresholding alone.