English

Topology-Based Feature Design and Tracking for Multi-Center Cyclones

Machine Learning 2020-11-18 v1 Computational Geometry Computer Vision and Pattern Recognition Graphics

Abstract

In this paper, we propose a concept to design, track, and compare application-specific feature definitions expressed as sets of critical points. Our work has been inspired by the observation that in many applications a large variety of different feature definitions for the same concept are used. Often, these definitions compete with each other and it is unclear which definition should be used in which context. A prominent example is the definition of cyclones in climate research. Despite the differences, frequently these feature definitions can be related to topological concepts. In our approach, we provide a cyclone tracking framework that supports interactive feature definition and comparison based on a precomputed tracking graph that stores all extremal points as well as their temporal correspondents. The framework combines a set of independent building blocks: critical point extraction, critical point tracking, feature definition, and track exploration. One of the major advantages of such an approach is the flexibility it provides, that is, each block is exchangeable. Moreover, it also enables us to perform the most expensive analysis, the construction of a full tracking graph, as a prepossessing step, while keeping the feature definition interactive. Different feature definitions can be explored and compared interactively based on this tracking graph. Features are specified by rules for grouping critical points, while feature tracking corresponds to filtering and querying the full tracking graph by specific requests. We demonstrate this method for cyclone identification and tracking in the context of climate research.

Keywords

Cite

@article{arxiv.2011.08676,
  title  = {Topology-Based Feature Design and Tracking for Multi-Center Cyclones},
  author = {Wito Engelke and Talha Bin Masood and Jakob Beran and Rodrigo Caballero and Ingrid Hotz},
  journal= {arXiv preprint arXiv:2011.08676},
  year   = {2020}
}

Comments

13 pages, 9 figures, 8th workshop on Topological Methods in Data Analysis and Visualization (TopoInVis 2019)

R2 v1 2026-06-23T20:19:00.415Z