Application of Clustering Methods to Anomaly Detection in Fibrous Media

Denis Dresvyanskiy, Tatiana Karaseva, Sergei Mitrofanov, Claudia Redenbach, Stefanie Schwaar, Vitalii Makogin, Evgeny Spodarev

The paper considers the problem of anomaly detection in 3D images of fibre materials. The spatial Stochastic Expectation Maximisation algorithm and Adaptive Weights Clustering are applied to solve this problem. The initial 3D grey scale image was divided into small cubes subject to clustering. For each cube clustering attributes values were calculated: mean local direction and directional entropy. Clustering is conducted according to the given attributes. The proposed methods are tested on the simulated images and on real fibre materials.

Knowledge Graph



Sign up or login to leave a comment