Dimensionality reduction for classification of stochastic fibre radiographs

Dodson, CTJ and Sampson, WW (2013) Dimensionality reduction for classification of stochastic fibre radiographs. In: Geometric Science of Information GSI2013. Lecture Notes in Computer Science, 8085 (8085). Springer, Heidelberg, pp. 158-165. ISBN ISBN 978-3-642-40020-9 (In Press)

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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.

Item Type: Book Section
Uncontrolled Keywords: Dimensionality reduction, fibre networks, fibre clusters, spatial covariance, trivariate Gaussian, radiographic images, simulations
Subjects: MSC 2010, the AMS's Mathematics Subject Classification > 53 Differential geometry
MSC 2010, the AMS's Mathematics Subject Classification > 60 Probability theory and stochastic processes
Depositing User: Prof CTJ Dodson
Date Deposited: 09 Jul 2013
Last Modified: 20 Oct 2017 14:13
URI: http://eprints.maths.manchester.ac.uk/id/eprint/1996

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