Dimensionality reduction for classification of stochastic fibre radiographs

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Pages158-165
    Number of pages7
    Volume8085
    ISBN (Print)9783642400193
    DOIs
    Publication statusPublished - 2013
    Event1st International SEE Conference on Geometric Science of Information, GSI 2013 - Paris
    Duration: 1 Jul 2013 → …

    Conference

    Conference1st International SEE Conference on Geometric Science of Information, GSI 2013
    CityParis
    Period1/07/13 → …

    Keywords

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

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