Shape and Size from the Mist: A Deformable Model for Particle Characterization |
| | Abstract | 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. | | Keywords | Particle Analysis, Deconvolution, Depth Estimation, Microscopic Imaging | | Type | Conference paper [With referee] | | Conference | Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) | | Year | 2010 Month May | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Image Analysis & Computer Graphics |
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