Application of Clustering Methods to Anomaly Detection in Fibrous Media
Applications
2020-01-08 v1 Computational Engineering, Finance, and Science
Abstract
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.
Keywords
Cite
@article{arxiv.1810.12401,
title = {Application of Clustering Methods to Anomaly Detection in Fibrous Media},
author = {Denis Dresvyanskiy and Tatiana Karaseva and Sergei Mitrofanov and Claudia Redenbach and Stefanie Schwaar and Vitalii Makogin and Evgeny Spodarev},
journal= {arXiv preprint arXiv:1810.12401},
year = {2020}
}