A stochastic shape and orientation model for fibres with an application to carbon nanotubes

Salme Kärkkäinen; Arttu Miettinen; Tuomas Turpeinen; Jukka Nyblom; Petra Pötschke; Jussi Timonen

doi:10.5566/ias.v31.p17-26

A stochastic shape and orientation model for fibres with an application to carbon nanotubes

Salme Kärkkäinen, Arttu Miettinen, Tuomas Turpeinen, Jukka Nyblom, Petra Pötschke, Jussi Timonen

Not published at VTT

Research output: Contribution to journal › Article › Scientific › peer-review

9 Citations (Scopus)

Abstract

Methods are introduced for analysing the shape and orientation of planar\nfibres from greyscale images of fibrous systems. The sequence of image\nprocessing techniques needed for segmentation of fibres is described.\nThe identified fibres were interpreted as deformed line segments for\nwhich two shape and two orientation parameters are estimated by the\nmaximum likelihood method. The methods introduced are shown to perform\nquite well for simulated systems of deformed line segments with known\nproperties. They were applied to TEM images of carbon nanotubes embedded\nin polycarbonate.

Original language	English
Pages (from-to)	17-26
Journal	Image Analysis and Stereology
Volume	31
Issue number	1
DOIs	https://doi.org/10.5566/ias.v31.p17-26
Publication status	Published - 2012
MoE publication type	Not Eligible

Keywords

2D fibre identification
Binarization
Carbon nanotubes
Deformed line segments
Multivariate von Mises distribution

Access to Document

10.5566/ias.v31.p17-26

http://www.mendeley.com/research/stochastic-shape-orientation-model-fibres-application-carbon-nanotubes

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Cite this

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title = "A stochastic shape and orientation model for fibres with an application to carbon nanotubes",

abstract = "Methods are introduced for analysing the shape and orientation of planar\nfibres from greyscale images of fibrous systems. The sequence of image\nprocessing techniques needed for segmentation of fibres is described.\nThe identified fibres were interpreted as deformed line segments for\nwhich two shape and two orientation parameters are estimated by the\nmaximum likelihood method. The methods introduced are shown to perform\nquite well for simulated systems of deformed line segments with known\nproperties. They were applied to TEM images of carbon nanotubes embedded\nin polycarbonate.",

keywords = "2D fibre identification, Binarization, Carbon nanotubes, Deformed line segments, Multivariate von Mises distribution",

author = "Salme K{\"a}rkk{\"a}inen and Arttu Miettinen and Tuomas Turpeinen and Jukka Nyblom and Petra P{\"o}tschke and Jussi Timonen",

year = "2012",

doi = "10.5566/ias.v31.p17-26",

language = "English",

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T1 - A stochastic shape and orientation model for fibres with an application to carbon nanotubes

AU - Kärkkäinen, Salme

AU - Miettinen, Arttu

AU - Turpeinen, Tuomas

AU - Nyblom, Jukka

AU - Pötschke, Petra

AU - Timonen, Jussi

PY - 2012

Y1 - 2012

N2 - Methods are introduced for analysing the shape and orientation of planar\nfibres from greyscale images of fibrous systems. The sequence of image\nprocessing techniques needed for segmentation of fibres is described.\nThe identified fibres were interpreted as deformed line segments for\nwhich two shape and two orientation parameters are estimated by the\nmaximum likelihood method. The methods introduced are shown to perform\nquite well for simulated systems of deformed line segments with known\nproperties. They were applied to TEM images of carbon nanotubes embedded\nin polycarbonate.

AB - Methods are introduced for analysing the shape and orientation of planar\nfibres from greyscale images of fibrous systems. The sequence of image\nprocessing techniques needed for segmentation of fibres is described.\nThe identified fibres were interpreted as deformed line segments for\nwhich two shape and two orientation parameters are estimated by the\nmaximum likelihood method. The methods introduced are shown to perform\nquite well for simulated systems of deformed line segments with known\nproperties. They were applied to TEM images of carbon nanotubes embedded\nin polycarbonate.

KW - 2D fibre identification

KW - Binarization

KW - Carbon nanotubes

KW - Deformed line segments

KW - Multivariate von Mises distribution

U2 - 10.5566/ias.v31.p17-26

DO - 10.5566/ias.v31.p17-26

M3 - Article

VL - 31

SP - 17

EP - 26

JO - Image Analysis and Stereology

JF - Image Analysis and Stereology

SN - 1580-3139

IS - 1

ER -