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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| Published in | IOP conference series. Materials Science and Engineering Vol. 537; no. 2; pp. 22001 - 22007 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bristol
IOP Publishing
01.05.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1757-8981 1757-899X 1757-899X |
| DOI | 10.1088/1757-899X/537/2/022001 |
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| 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. The spatial Stochastic Expectation Maximization algorithm shows its effectiveness in comparison to Adaptive Weights Clustering. |
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| AbstractList | 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. |
| Author | Dresvyanskiy, Denis Redenbach, Claudia Spodarev, Evgeny Mitrofanov, Sergei Schwaar, Stefanie Karaseva, Tatiana Makogin, Vitalii |
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| Cites_doi | 10.1016/j.compositesa.2016.12.028 10.1016/j.compscitech.2017.10.023 10.1046/j.1365-2818.2002.01009.x 10.1137/1103036 10.5566/ias.1489 10.1007/s11009-017-9603-2 10.1017/apr.2016.87 |
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| References_xml | – volume: 97 start-page: 83 year: 2017 ident: MSE_537_2_022001bib1 article-title: Individual fibre segmentation from 3d x-ray computed tomography for characterising the fibre orientation in unidirectional composite materials publication-title: Composites Part A: Applied Science and Manufacturing doi: 10.1016/j.compositesa.2016.12.028 – volume: 156 start-page: 305 year: 2018 ident: MSE_537_2_022001bib2 article-title: X-ray computed tomography of polymer composites publication-title: Composites Science and Technology doi: 10.1016/j.compscitech.2017.10.023 – volume: 206 start-page: 41 year: 2002 ident: MSE_537_2_022001bib9 article-title: Automated reconstruction of curvilinear fibres from 3d datasets acquired by x-ray microtomography publication-title: Journal of microscopy doi: 10.1046/j.1365-2818.2002.01009.x – ident: MSE_537_2_022001bib4 – start-page: 35 year: 2014 ident: MSE_537_2_022001bib10 article-title: Geometric and mechanical modeling of fiber-reinforced composites – year: 2007 ident: MSE_537_2_022001bib3 – volume: 3 start-page: 428 year: 1958 ident: MSE_537_2_022001bib8 article-title: A simplified method of experimentally evaluating the entropy of a stationary sequence publication-title: Theory of Probability & Its Applications doi: 10.1137/1103036 – volume: 35 start-page: 167 year: 2016 ident: MSE_537_2_022001bib7 article-title: Estimating fibre direction distributions of reinforced composites from tomographic images publication-title: Image Analysis & Stereology doi: 10.5566/ias.1489 – volume: 20 start-page: 1223 year: 2017 ident: MSE_537_2_022001bib5 article-title: Entropy-based inhomogeneity detection in fiber materials publication-title: Methodology and Computing in Applied Probability doi: 10.1007/s11009-017-9603-2 – volume: 49 start-page: 258 year: 2017 ident: MSE_537_2_022001bib6 article-title: Estimation of entropy for Poisson marked point processes publication-title: Adv. in Appl. Proba doi: 10.1017/apr.2016.87 |
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| SubjectTerms | Adaptive algorithms Anomalies Clustering Cubes Maximization Optimization |
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| Title | Application of clustering methods to anomaly detection in fibrous media |
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