Wavelet texture analysis of on-line acquired images for paper formation assessment and monitoring

Paper formation (the distribution and intermixing of fibres in a paper sheet), plays a central role in paper products, and is usually evaluated off-line, with a significant delay relative to the high production rates achieved in modern paper machines. In this paper, we address an approach for evaluating and monitor paper formation using images acquired with an especially designed sensor, in-line, in-situ and in real time. The methodology essentially consists of applying wavelet texture analysis to raw images, in order to compute a wavelet signature for each image, based on which their discrimination, according to the formation quality level, can be made. A PCA analysis of such features confirms the different formation quality levels defined a priori after visual inspection, and, furthermore, suggests a new subclass for abnormal samples, related to the bulkiness of fibre flocks. A multivariate statistical process control framework, based on such PCA description (PCA-MSPC), is proposed to monitor formation quality, which provides quite good results when applied to the available images, as analyzed with the ROC curve for the method and confirmed with a Monte Carlo simulation study using subimages with 1/4 of the size of the original ones.