Temporal Merge Tree Maps:A Topology-Based Static Visualization for Temporal Scalar Data

Köpp, Wiebke; Weinkauf, Tino

doi:10.1109/tvcg.2022.3209387

Cited by 2 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A method by Yan et al [66] combines geometric and topological measures. Furthermore, topological methods can also be used for more advanced analysis of time series such as temporal merge tree maps [26] or geodesics [43,57].…”

Section: Related Workmentioning

confidence: 99%

Merge Tree Geodesics and Barycenters with Path Mappings

Wetzels,

Pont,

Tierny

et al. 2023

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Fig. 1: Two merge tree geodesics with their corresponding mappings. The top row shows the mappings of critical points (restricted to maxima) embedded into the domain. In the bottom row the mapping between the two corresponding merge trees and their interpolated geodesic is shown. The most persistent mapped features are highlighted and colored to show the correspondence between embedding and planar layout. The Wasserstein geodesic can be seen on the left, the path mapping geodesic on the right. Clearly, the middle tree on the right is a more meaningful interpolation of the two input trees than the middle tree on the left; and yields a more intuitive correspondence between the nodes. Note that the used layout differs between the two figures. However, the two trees in front are indeed identical in both figures, as are the trees in the back.

show abstract

Section: Related Workmentioning

confidence: 99%

Merge Tree Geodesics and Barycenters with Path Mappings

Wetzels,

Pont,

Tierny

et al. 2023

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice

Ternström

Pabon

2022

Applied Sciences

View full text Add to dashboard Cite

Individual acoustic and other physical metrics of vocal status have long struggled to prove their worth as clinical evidence. While combinations of metrics or “features” are now being intensely explored using data analytics methods, there is a risk that explainability and insight will suffer. The voice mapping paradigm discards the temporal dimension of vocal productions and uses fundamental frequency (fo) and sound pressure level (SPL) as independent control variables to implement a dense grid of measurement points over a relevant voice range. Such mapping visualizes how most physical voice metrics are greatly affected by fo and SPL, and more so individually than has been generally recognized. It is demonstrated that if fo and SPL are not controlled for during task elicitation, repeated measurements will generate “elicitation noise”, which can easily be large enough to obscure the effect of an intervention. It is observed that, although a given metric’s dependencies on fo and SPL often are complex and/or non-linear, they tend to be systematic and reproducible in any given individual. Once such personal trends are accounted for, ordinary voice metrics can be used to assess vocal status. The momentary value of any given metric needs to be interpreted in the context of the individual’s voice range, and voice mapping makes this possible. Examples are given of how voice mapping can be used to quantify voice variability, to eliminate elicitation noise, to improve the reproducibility and representativeness of already established metrics of the voice, and to assess reliably even subtle effects of interventions. Understanding variability at this level of detail will shed more light on the interdependent mechanisms of voice production, and facilitate progress toward more reliable objective assessments of voices across therapy or training.

show abstract

Temporal Merge Tree Maps:A Topology-Based Static Visualization for Temporal Scalar Data

Cited by 2 publications

References 41 publications

Merge Tree Geodesics and Barycenters with Path Mappings

Merge Tree Geodesics and Barycenters with Path Mappings

Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice

Contact Info

Product

Resources

About