  Holograms of colloidal particles can be analyzed with the Lorenz-Mie theory
of light scattering to measure individual particles' three-dimensional
positions with nanometer precision while simultaneously estimating their sizes
and refractive indexes. Extracting this wealth of information begins by
detecting and localizing features of interest within individual holograms.
Conventionally approached with heuristic algorithms, this image analysis
problem can be solved faster and more generally with machine-learning
techniques. We demonstrate that two popular machine-learning algorithms,
cascade classifiers and deep convolutional neural networks (CNN), can solve the
feature-localization problem orders of magnitude faster than current
state-of-the-art techniques. Our CNN implementation localizes holographic
features precisely enough to bootstrap more detailed analyses based on the
Lorenz-Mie theory of light scattering. The wavelet-based Haar cascade proves to
be less precise, but is so computationally efficient that it creates new
opportunities for applications that emphasize speed and low cost. We
demonstrate its use as a real-time targeting system for holographic optical
trapping.
