Application of clustering methods to anomaly detection in fibrous media

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 e...

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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 537; no. 2; pp. 22001 - 22007
Main Authors Dresvyanskiy, Denis, Karaseva, Tatiana, Mitrofanov, Sergei, Redenbach, Claudia, Schwaar, Stefanie, Makogin, Vitalii, Spodarev, Evgeny
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.05.2019
Subjects
Adaptive algorithms
Anomalies
Clustering
Cubes
Maximization
Optimization
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Summary: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. The spatial Stochastic Expectation Maximization algorithm shows its effectiveness in comparison to Adaptive Weights Clustering.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/537/2/022001