Feature Level-Sets: Generalizing Iso-Surfaces to Multi-Variate Data

Jankowai, Jochen; Hotz, Ingrid

doi:10.1109/tvcg.2018.2867488

Cited by 23 publications

(17 citation statements)

References 29 publications

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“…Inviwo has successfully been used in numerous scientific publications within different application areas, commercial products, and university-level courses. The scientific contributions include work on advanced volumetric illumination [35]- [37], medical and molecular visualization [38]- [40], transfer function design for volume rendering [21], [41], crowd-sourcing-based user studies [42]- [44], topological analysis [45]- [47], as well as multi-variate data and flow visualization [48]- [50]. In addition, Inviwo is to the best of our knowledge currently used by four different universities and two commercial companies.…”

Section: Application Examplesmentioning

confidence: 99%

Inviwo — A Visualization System with Usage Abstraction Levels

Jonsson

Steneteg

Sundén

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities.

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Section: Application Examplesmentioning

confidence: 99%

Inviwo — A Visualization System with Usage Abstraction Levels

Jonsson

Steneteg

Sundén

et al. 2020

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…Traits are defined as geometric objects in the attribute space and features as the preimage of a trait in the spatial domain. In contrast to the interactive definition of feature‐level‐sets of Jankowai and Hotz [JH20] through star plots or parallel coordinates, we define our fiber surfaces using multiple three‐dimensional convex solids in different three‐dimensional subspaces of the whole multifield data set. Our approach is also different in the way that we do not only extract the fiber surface, but we also reduce and refine the tetrahedral input grid.…”

Section: Related Workmentioning

confidence: 99%

“…Another approach to extend the concept of isolines and isosurfaces was published by Jankowai and Hotz [JH20]. They introduced feature‐level sets and traits as general approach for the visualization of multivariate data.…”

Section: Related Workmentioning

confidence: 99%

Fiber Surfaces for many Variables

Blecha

Raith

Prager

et al. 2020

Computer Graphics Forum

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Scientific visualization deals with increasingly complex data consisting of multiple fields. Typical disciplines generating multivariate data are fluid dynamics, structural mechanics, geology, bioengineering, and climate research. Quite often, scientists are interested in the relation between some of these variables. A popular visualization technique for a single scalar field is the extraction and rendering of isosurfaces. With this technique, the domain can be split into two parts, i.e. a volume with higher values and one with lower values than the selected isovalue. Fiber surfaces generalize this concept to two or three scalar variables up to now. This article extends the notion further to potentially any finite number of scalar fields. We generalize the fiber surface extraction algorithm of Raith et al. [RBN∗19] from 3 to d dimensions and demonstrate the technique using two examples from geology and climate research. The first application concerns a generic model of a nuclear waste repository and the second one an atmospheric simulation over central Europe. Both require complex simulations which involve multiple physical processes. In both cases, the new extended fiber surfaces helps us finding regions of interest like the nuclear waste repository or the power supply of a storm due to their characteristic properties.

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“…FEATURE LEVEL-SETS -We utilise the concept of feature level sets [13] for a closer inspection of critical regions in the data set.…”

Section: Tensor Transfer Function Designmentioning

confidence: 99%

“…The domain expert can navigate the attribute space in their familiar context; interacting with a few glyphs in a two-dimensional interaction panel to assign colour and opacity values. Based on these assignment the TTF is assembled either through classification, linear blending or feature level-set generation [13]. Our approach adheres to three design principles; (i) Use as much domain specific knowledge as possible for the interaction with the data while having one underlying concept.…”

Section: Introductionmentioning

confidence: 99%

Tensor volume exploration using attribute space representatives

Jankowai¹,

Hotz²

2020

Preprint

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While volume rendering for scalar fields has been advanced into a powerful visualisation method, similar volumetric representations for tensor fields are still rare. The complexity of the data challenges not only the rendering but also the design of the transfer function. In this paper we propose an interface using glyph widgets to design a transfer function for the rendering of tensor data sets. Thereby the transfer function (TF) controls a volume rendering which represents sought after tensor-features and a texture that conveys directional information. The basis of the design interface is a two-dimensional projection of the attribute space. Characteristicrepresentatives in the form of glyphs support an intuitive navigation through the attribute space. We provide three different options to select the representatives: automatic selection based on attribute space clustering, uniform sampling of the attribute space, or manually selected representatives. In contrast to glyphs placed into the 3D volume, we use glyphs with complex geometry as widgets to control the shape and extent of the representatives. In the final rendering the glyphs with their assigned colors play a similar role as a legend in an atlas like representation. The method provides an overview of the tensor field in the 3D volume at the same time as it allows the user to explore the tensor field in an attribute space. We demonstrate the flexibility of our approach on tensor fields for selected data sets with very different characteristics.

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Feature Level-Sets: Generalizing Iso-Surfaces to Multi-Variate Data

Cited by 23 publications

References 29 publications

Inviwo — A Visualization System with Usage Abstraction Levels

Inviwo — A Visualization System with Usage Abstraction Levels

Fiber Surfaces for many Variables

Tensor volume exploration using attribute space representatives

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