Detecting anomalies in fibre systems using 3-dimensional image data

Statistics and Computing - We consider the problem of detecting anomalies in the directional distribution of fibre materials observed in 3D images. We divide the image into a set of scanning...     

Anisotropy analysis of pressed point processes

This paper introduces methods for the detection of anisotropies which are caused by compression of regular 3D point patterns. Isotropy tests based on directional summary statistics and estimators for the compression factor are developed. Using simulated data, the dependence of the power of these methods on the intensity, the degree of regularity, and the compression strength is studied. Finally, our methods are applied to the point patterns of centers of air pores extracted from tomographic images of ice cores. This way the presence of anisotropies in the ice caused by the compression of the ice sheet and an increase of their strength with increasing depth are shown.     

On a Mixture Model for Directional Data on the Sphere

We consider mixtures of general angular central Gaussian distributions as models for multimodal directional data. We prove consistency of the maximum‐likelihood estimates of model parameters and convergence of their numerical approximations based on an expectation–maximization algorithm. Then, we focus on mixtures of special angular central Gaussian distributions and discuss the details of a fast numerical algorithm, which allows to fit multimodal distributions to massive data, occurring, for example, in the study of the microstructure of materials. We illustrate the applicability with some data from fibre composites and from ceramic foams.     

Estimation of Fibre Length Distributions from Fibre Endpoints

Estimating the fibre length distribution in composite materials is of practical relevance in materials science. We propose an estimator for the fibre length distribution using the point process of fibre endpoints as input. Assuming that this point process is a realization of a Neyman–Scott process, we use results for the reduced second moment measure to derive a consistent and unbiased estimator for the fibre length distribution. We introduce various versions of the estimator taking anisotropy or errors in the observation into account. The estimator is evaluated using a heuristic for its mean squared error as well as a simulation study. Finally, the estimator is applied to the fibre endpoint process extracted from a tomographic image of a glass fibre composite.