Shape and Size from the Mist: A Deformable Model for Particle Characterization

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2010

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Shape and Size from the Mist : A Deformable Model for Particle Characterization. / Dahl, Anders Lindbjerg; Jørgensen, Thomas Martini; Gundu, Phanindra Narayan; Larsen, Rasmus.

Proceedings of VISAPP 2010. Vol. 5 2010.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2010

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Dahl, Anders Lindbjerg; Jørgensen, Thomas Martini; Gundu, Phanindra Narayan; Larsen, Rasmus / Shape and Size from the Mist : A Deformable Model for Particle Characterization.

Proceedings of VISAPP 2010. Vol. 5 2010.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2010

Bibtex

@inbook{6ca0328cfe524ffe870479876bfdbbf8,
title = "Shape and Size from the Mist: A Deformable Model for Particle Characterization",
keywords = "Particle Analysis, Deconvolution, Depth Estimation, Microscopic Imaging",
author = "Dahl, {Anders Lindbjerg} and Jørgensen, {Thomas Martini} and Gundu, {Phanindra Narayan} and Rasmus Larsen",
year = "2010",
volume = "5",
booktitle = "Proceedings of VISAPP 2010",

}

RIS

TY - GEN

T1 - Shape and Size from the Mist

T2 - Proceedings of VISAPP 2010

AU - Dahl,Anders Lindbjerg

AU - Jørgensen,Thomas Martini

AU - Gundu,Phanindra Narayan

AU - Larsen,Rasmus

PY - 2010

Y1 - 2010

N2 - Process optimization often depends on the correct estimation of particle size, their shape and their concentration. In case of the backlight microscopic system, which we investigate here, particle images suffer from out-of-focus blur. This gives a bias towards overestimating the particle size when particles are behind or in front of the focus plane. In most applications only in-focus particles get analyzed, but this weakens the statistical basis and requires either particle sampling over longer time or results in uncertain predictions. We propose a new method for estimating the size and the shape of the particles, which includes out-of-focus particles. We employ particle simulations for training an inference model predicting the true size of particles from image observations. This also provides depth information, which can be used in concentration predictions. Our model shows promising results on real data with ground truth depth, shape and size information. The outcome of our approach is a reliable particle analysis obtained from shorter sampling time.

AB - Process optimization often depends on the correct estimation of particle size, their shape and their concentration. In case of the backlight microscopic system, which we investigate here, particle images suffer from out-of-focus blur. This gives a bias towards overestimating the particle size when particles are behind or in front of the focus plane. In most applications only in-focus particles get analyzed, but this weakens the statistical basis and requires either particle sampling over longer time or results in uncertain predictions. We propose a new method for estimating the size and the shape of the particles, which includes out-of-focus particles. We employ particle simulations for training an inference model predicting the true size of particles from image observations. This also provides depth information, which can be used in concentration predictions. Our model shows promising results on real data with ground truth depth, shape and size information. The outcome of our approach is a reliable particle analysis obtained from shorter sampling time.

KW - Particle Analysis, Deconvolution, Depth Estimation, Microscopic Imaging

M3 - Article in proceedings

VL - 5

BT - Proceedings of VISAPP 2010

ER -