Stochastic texture analysis for measuring sheet formation variability in the industry

Abstract

Several continuous manufacturing processes use stochastic texture images for quality control and monitoring. Large amounts of pictorial data are acquired, providing important information about both the materials produced and the manufacturing processes involved. However, it is often difficult to measure objectively the similarity among such industrial stochastic images or to discriminate between the texture images of stochastic materials with distinct properties. Nowadays, the degree of discrimination required by industrial processes often goes beyond the limits of human visual perception. This paper proposes a new approach for multiresolution stochastic texture discrimination in the industry (e.g., nonwoven textiles and paper), which is focused on sheet formation properties. The wavelet transform is used to represent stochastic texture images in multiple resolutions and to describe them using local density variability as features. At each resolution, the wavelet subbands approximate image gradients. The image gradients are modeled as Gaussian colored noise, and the gradient magnitudes, as Rayleigh probability density functions. Based on this representation, a multiresolution distance measure for stochastic textures is proposed. Some experimental results are reported, and ideas for future work are presented with the conclusions.