Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles
Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles
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DOI：
10.1364/oe.26.015221
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发表时间：
2018-06-11
期刊：
影响因子：
3.8
通讯作者：
Grier, David G.
中科院分区：
文献类型：
--
作者：
Hannel, Mark D.;Abdulali, Aidan;Grier, David G.
可以利用光散射的洛伦兹-米氏理论来分析胶体颗粒的全息图，以纳米精度测量单个颗粒的三维位置，同时估计它们的尺寸和折射率。提取如此丰富的信息首先是检测和定位各个全息图中感兴趣的特征。传统上采用启发式算法来解决该图像分析问题，但可以通过机器学习技术更快、更普遍地解决。我们证明了两种流行的机器学习算法，级联分类器和深度卷积神经网络（CNN），可以比当前最先进的技术更快地解决特征定位问题。我们的 CNN 实现足够精确地定位全息特征，以引导基于光散射的 Lorenz-Mie 理论进行更详细的分析。基于小波的 Haar 级联被证明精度较低，但计算效率很高，为强调速度和低成本的应用程序创造了新的机会。我们展示了它作为全息光捕获实时瞄准系统的用途。 (C) 2018 年美国光学协会根据 OSA 开放获取出版协议条款
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