Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles

TL;DR

This paper compares machine-learning methods, specifically cascade classifiers and CNNs, for rapid and precise feature localization in holograms of colloidal particles, enabling faster analysis and real-time applications.

Contribution

It demonstrates that machine-learning algorithms can significantly outperform traditional methods in speed and accuracy for hologram feature localization, with practical applications in optical trapping.

Findings

CNN achieves high localization precision suitable for Lorenz-Mie analysis.

Cascade classifiers are extremely fast but less precise.

Both methods enable real-time hologram analysis and applications.

Abstract

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.