Multitype Multifield Visualization

多领域数据的可视化分析是科学可视化领域的一大研究挑战。近年来，已经提出了许多方法，这些方法要么对字段进行并排可视化，要么应用半自动方法来计算和可视化字段之间的关系。然而，大多数现有技术都集中在同一类型的多字段上，例如多个纯量字段的集合。最近的多字段倾向于由不同类型的字段组成，即，标量场、矢量场和张量场都是在同一个域上获得的。该项目提出了不同类型的多领域的可视化方法。特别是，我们提出了多类型字段之间的相似性度量，我们定义的功能描述多类型字段之间的相关性，我们适应离散方法从信息可视化多类型字段。的方法进行了测试和评估的测试数据集从不同的应用领域。

The visual analysis of multifield data is one of the big research challenges in the field of Scientific Visualization. In recent years, many approaches for this have been proposed which either do a side-by-side visualization if the fields or apply semi-automatic methods to compute and visualize the relations between the fields. However, most existing techniques focus on multifields of the same type, for instance a collection of multiple scalar fields. Recent multifields tend to consist of fields of different types, i.e., scalar, vector andtensor fields are acquired over the same domain. This project proposes approaches for the visualization of multifields of different types. In particular, we propose similarity measures between multitype fields, we define features describing the correlations between multitype fields, and we adapt discrete methods from Information Visualization for multitype fields. The approaches are tested and evaluated on a number of test data sets from different areas of application.