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We consider the problem of detecting anomalies in the directionaldistribution of fibre materials observed in 3D images. We divide the image intoa set of scanning windows and classify them into two clusters: homogeneousmaterial and anomaly. Based on a sample of estimated local fibre directions,for each scanning window we compute several classification attributes, namelythe coordinate wise means of local fibre directions, the entropy of thedirectional distribution, and a combination of them. We also propose a newspatial modification of the Stochastic Approximation Expectation-Maximization(SAEM) algorithm. Besides the clustering we also consider testing thesignificance of anomalies. To this end, we apply a change point technique forrandom fields and derive the exact inequalities for tail probabilities of atest statistics. The proposed methodology is first validated on simulatedimages. Finally, it is applied to a 3D image of a fibre reinforced polymer.

Detecting anomalies in fibre systems using 3-dimensional image data