Image-based classification of paper surface quality using wavelet texture analysis

The characteristics of paper surface play a central role in the overall quality of paper produced in modern paper machines. Among the surface features, paper formation, i.e., the level of homogeneity in the distribution of fibres on the surface of paper, is a key quality parameter, being currently monitored off-line, at low sampling rates relatively to the high production speeds achieved with modern paper machines. Therefore, in this paper, we address the problem of assessing the quality of paper formation, on-line, in situ, in an autonomous, efficient, objective and fast way, using features derived from images collected by a specially designed sensor, coupled with proper classification methodologies. The results obtained clearly demonstrate the potential of the proposed assessment approach either for the more complex three-class classification problem as well as for less demanding, but still important in practice, two-class “Accept”/“Reject” or “Pass”/“Fail” problem.