Visualization of Two-Dimensional Symmetric Tensor Fields Using the Heat Kernel Signature
2014 (English)In: Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications / [ed] Peer-Timo Bremer, Ingrid Hotz, Valerio Pascucci, Ronald Peikert, Springer, 2014, 249-262 p.Chapter in book (Refereed)
We propose a method for visualizing two-dimensional symmetric positive definite tensor fields using the Heat Kernel Signature (HKS). The HKS is derived from the heat kernel and was originally introduced as an isometry invariant shape signature. Each positive definite tensor field defines a Riemannian manifold by considering the tensor field as a Riemannian metric. On this Riemmanian manifold we can apply the definition of the HKS. The resulting scalar quantity is used for the visualization of tensor fields. The HKS is closely related to the Gaussian curvature of the Riemannian manifold and the time parameter of the heat kernel allows a multiscale analysis in a natural way. In this way, the HKS represents field related scale space properties, enabling a level of detail analysis of tensor fields. This makes the HKS an interesting new scalar quantity for tensor fields, which differs significantly from usual tensor invariants like the trace or the determinant. A method for visualization and a numerical realization of the HKS for tensor fields is proposed in this chapter. To validate the approach we apply it to some illustrating simple examples as isolated critical points and to a medical diffusion tensor data set.
Place, publisher, year, edition, pages
Springer, 2014. 249-262 p.
, Mathematics and Visualizaiton, ISSN 1612-3786
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-127660DOI: 10.1007/978-3-319-04099-8ISBN: 978-3-319-04098-1ISBN: 978-3-319-04099-8OAI: oai:DiVA.org:liu-127660DiVA: diva2:926349