Abstract
Recent advancements in neutron and X-ray sources, instrumentation and data collection modes have significantly increased the experimental data size (which could easily contain 10^8-10^10 data points), so that conventional volumetric visualization approaches become inefficient for both still imaging and interactive OpenGL rendition in a 3D setting. We introduce a new approach based on the unsupervised machine learning algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to efficiently analyze and visualize large volumetric datasets. Here we present two examples of analyzing and visualizing datasets from the diffuse scattering experiment of a single crystal sample and the tomographic reconstruction of a neutron scanning of a turbine blade. We found that by using the intensity as the weighting factor in the clustering process, DBSCAN becomes very effective in de-noising and feature/boundary detection, and thus enables better visualization of the hierarchical internal structures of the neutron scattering data.
Abstract (translated)
中子和X射线源，仪器和数据采集模式的最新进展显着增加了实验数据的大小（可以容易地包含10 ^ 8-10 ^ 10个数据点），因此传统的体积可视化方法对于静态成像都变得低效。和3D设置中的交互式OpenGL再现。我们引入了一种基于无监督机器学习算法，基于密度的噪声应用空间聚类（DBSCAN）的新方法，以有效地分析和可视化大体积数据集。在这里，我们提出了两个分析和可视化数据集的例子，这些数据集来自单晶样品的漫散射实验和涡轮叶片中子扫描的层析成像重建。我们发现，通过在聚类过程中使用强度作为加权因子，DBSCAN在去噪和特征/边界检测方面变得非常有效，因此能够更好地可视化中子散射数据的分层内部结构。
URL
https://arxiv.org/abs/1710.05994
