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

被引:54
作者
Hannel, Mark D. [1 ,2 ]
Abdulali, Aidan [3 ]
O'brien, Michael [1 ,2 ]
Grier, David G. [1 ,2 ]
机构
[1] NYU, Dept Phys, 4 Washington Pl, New York, NY 10003 USA
[2] NYU, Ctr Soft Matter Res, 550 1St Ave, New York, NY 10003 USA
[3] Packer Collegiate Inst, Brooklyn, NY 11201 USA
来源
OPTICS EXPRESS | 2018年 / 26卷 / 12期
基金
美国国家科学基金会;
关键词
VIDEO MICROSCOPY; TRACKING; DYNAMICS;
D O I
10.1364/OE.26.015221
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
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. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
引用
收藏
页码:15221 / 15231
页数:11
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