Alignment and Statistical Analysis of 2D Small-scale Paper Property Maps

The relationship between printability and paper structure based on registration, alignment and analysis of 2D property maps of unprinted and printed paper has been studied. Surface topography, optical formation and intensity of the print were all measured and the point-by-point probabilistic interdependencies of these properties statistically characterised. The 2D measurements of the paper properties and the print quality were aligned with a point-mapping based registration procedure. This alignment provides a large amount of multivariate pointwise data and thus permits reliable estimates of the joint probability density functions (pdfs) that are efficiently parameterized through Gaussian mixtures. Assuming the interdependency to be only probabilistic and non-Gaussian, it is possible to derive full conditional pdfs instead of regression models and to investigate how the shape of the conditional pdfs - e.g. tails - depends on the conditioning variable. These pdfs were used to form anomaly maps that locate defects (for example, print defects) and their causes. The methods and the usefulness of the analyses were demonstrated with results on newsprint samples.