Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session B55: Big Data in Polymer and Soft Matter Physics
11:15 AM–2:15 PM,
Monday, March 4, 2019
BCEC
Room: 254B
Sponsoring
Units:
DPOLY GSOFT DCOMP
Chair: Jonathan Whitmer, University of Notre Dame
Abstract: B55.00011 : End-to-End Characterization of Colloidal Particles through Holographic Microscopy and Deep Convolutional Neural Networks
1:39 PM–1:51 PM
Presenter:
Lauren Altman
(Center for Soft Matter Research, New York University)
Authors:
Lauren Altman
(Center for Soft Matter Research, New York University)
David Grier
(Center for Soft Matter Research, New York University)
Mark D Hannel II
(Center for Soft Matter Research, New York University)
theory yields the particles' sizes, refractive indexes and three-dimensional
positions, all with exquisite precision and accuracy. No other technique
provides such a wealth of particle-resolved and time-resolved characterization
data. The underlying fits to Lorenz-Mie theory, however, require estimates
for the particles' positions and properties that are good enough to ensure
convergence to the optimal solution. Here, we demonstrate that this estimation
problem can be solved with a single, specially structured deep convolutional
neural network. The machine-learning approach to holographic particle
characterization is orders of magnitude faster than conventional image-analysis
techniques, substantially more robust against image defects, and yields answers
that already are sufficiently precise for many applications. We demonstrate
the method's efficacy through experimental measurements of the properties and dynamics
of model colloidal systems.
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