Evaluating Unsupervised Methods to Size and Classify Suspended Particles Using Digital In-Line Holography
Evaluating Unsupervised Methods to Size and Classify Suspended Particles Using Digital In-Line Holography
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DOI：
10.1175/jtech-d-14-00157.1
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发表时间：
2015-06-01
影响因子：
2.2
通讯作者：
Nimmo-Smith, W. Alex M.
中科院分区：
文献类型：
--
作者：
Davies, Emlyn J.;Buscombe, Daniel;Nimmo-Smith, W. Alex M.
可以从海洋颗粒的数字同轴全息术中获得大量信息，从而消除与基于标准透镜的成像方法相关的景深和聚焦误差。然而，为了充分发挥该技术在海洋研究中的潜力，需要使用完全无监督(自动化)的方法对颗粒进行聚焦、分割、大小调整和分类。这些计算挑战是这篇论文的主题，在这篇论文中，作者利用了英国普利茅斯大学开发的各种全息系统收集的数据，这些全息系统来自大量的粒子类型、大小和形状。一种新的重建平面降噪方法被成功地用于辅助粒子分割和大小调整。根据受过训练的操作员在屏幕上测量颗粒轴线所获得的等效尺寸度量来评估用于得出颗粒特征(以及随后的尺寸分布)的自动化例程的性能。无监督方法被发现是可靠的，尽管由于对粒子的过度分割而产生了一些误差。开发了一种简单的无监督粒子分类系统，能够成功地区分冲浪区内的沙粒、气泡和硅藻。避免将气泡和生物颗粒错误地计算为沙粒，可以更准确地估计沙子浓度，这在部署加气水中的颗粒监测仪器时尤其重要。也许在粒子全息术的计算方面进一步发展的最大潜力是在无监督粒子分类领域。这里提出的简单方法提供了一个基础，在此基础上，进一步的发展可以导致可靠地识别更复杂的颗粒种群，例如那些含有浮游植物、浮游动物、絮凝的粘性沉积物和油滴的颗粒种群。
Substantial information can be gained from digital in-line holography of marine particles, eliminating depth-of-field and focusing errors associated with standard lens-based imaging methods. However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing, and classification of particles. These computational challenges are the subject of this paper, in which the authors draw upon data collected using a variety of holographic systems developed at Plymouth University, United Kingdom, from a significant range of particle types, sizes, and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from oversegmentation of particles. A simple unsupervised particle classification system is developed and is capable of successfully differentiating sand grains, bubbles, and diatoms from within the surfzone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classification. The simple method proposed here provides a foundation upon which further development could lead to reliable identification of more complex particle populations, such as those containing phytoplankton, zooplankton, flocculated cohesive sediments, and oil droplets.