Dimensionality reduction for classification of stochastic fibre radiographs

C. T J Dodson; W. W. Sampson

doi:10.1007/978-3-642-40020-9_16

Dimensionality reduction for classification of stochastic fibre radiographs

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

Dimensionality reduction helps to identify small numbers of essential features of stochastic fibre networks for classification of image pixel density datasets from experimental radiographic measurements of commercial samples and simulations. Typical commercial macro-fibre networks use finite length fibres suspended in a fluid from which they are continuously deposited onto a moving bed to make a continuous web; the fibres can cluster to differing degrees, primarily depending on the fluid turbulence, fibre dimensions and flexibility. Here we use information geometry of trivariate Gaussian spatial distributions of pixel density among first and second neighbours to reveal features related to sizes and density of fibre clusters. © 2013 Springer-Verlag.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\|Lect. Notes Comput. Sci.
Publisher	Springer Nature
Pages	158-165
Number of pages	7
Volume	8085
ISBN (Print)	9783642400193
DOIs	https://doi.org/10.1007/978-3-642-40020-9_16
Publication status	Published - 2013
Event	1st International SEE Conference on Geometric Science of Information, GSI 2013 - Paris Duration: 1 Jul 2013 → …

Conference

Conference	1st International SEE Conference on Geometric Science of Information, GSI 2013
City	Paris
Period	1/07/13 → …

Keywords

Dimensionality reduction
fibre clusters
fibre networks
radiographic images
simulations
spatial covariance
trivariate Gaussian

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10.1007/978-3-642-40020-9_16

Cite this

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title = "Dimensionality reduction for classification of stochastic fibre radiographs",

abstract = "Dimensionality reduction helps to identify small numbers of essential features of stochastic fibre networks for classification of image pixel density datasets from experimental radiographic measurements of commercial samples and simulations. Typical commercial macro-fibre networks use finite length fibres suspended in a fluid from which they are continuously deposited onto a moving bed to make a continuous web; the fibres can cluster to differing degrees, primarily depending on the fluid turbulence, fibre dimensions and flexibility. Here we use information geometry of trivariate Gaussian spatial distributions of pixel density among first and second neighbours to reveal features related to sizes and density of fibre clusters. {\textcopyright} 2013 Springer-Verlag.",

keywords = "Dimensionality reduction, fibre clusters, fibre networks, radiographic images, simulations, spatial covariance, trivariate Gaussian",

author = "Dodson, {C. T J} and Sampson, {W. W.}",

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language = "English",

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note = "1st International SEE Conference on Geometric Science of Information, GSI 2013 ; Conference date: 01-07-2013",

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Dodson, CTJ & Sampson, WW 2013, Dimensionality reduction for classification of stochastic fibre radiographs. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. vol. 8085, Springer Nature, pp. 158-165, 1st International SEE Conference on Geometric Science of Information, GSI 2013, Paris, 1/07/13. https://doi.org/10.1007/978-3-642-40020-9_16

Dimensionality reduction for classification of stochastic fibre radiographs. / Dodson, C. T J ; Sampson, W. W.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. Vol. 8085 Springer Nature, 2013. p. 158-165.

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AB - Dimensionality reduction helps to identify small numbers of essential features of stochastic fibre networks for classification of image pixel density datasets from experimental radiographic measurements of commercial samples and simulations. Typical commercial macro-fibre networks use finite length fibres suspended in a fluid from which they are continuously deposited onto a moving bed to make a continuous web; the fibres can cluster to differing degrees, primarily depending on the fluid turbulence, fibre dimensions and flexibility. Here we use information geometry of trivariate Gaussian spatial distributions of pixel density among first and second neighbours to reveal features related to sizes and density of fibre clusters. © 2013 Springer-Verlag.

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KW - fibre clusters

KW - fibre networks

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KW - simulations

KW - spatial covariance

KW - trivariate Gaussian

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DO - 10.1007/978-3-642-40020-9_16

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VL - 8085

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EP - 165

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.

PB - Springer Nature

T2 - 1st International SEE Conference on Geometric Science of Information, GSI 2013

Y2 - 1 July 2013

ER -

Dimensionality reduction for classification of stochastic fibre radiographs

Abstract

Conference

Keywords

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