Top scientific visualization research problems

Johnson, Christopher R.

doi:10.1109/mcg.2004.20

Cited by 248 publications

(156 citation statements)

References 10 publications

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“…The extension to n ≥ 4 dimensions is motivated by the availability of high-resolution time-series of 3-dimensional images (eg. Stock [15] observing the breaking of bone structure under pressure) and by the general quest to analyze multi-variate scientific data [8,9]. Our particular interest is in fast algorithms for computing Outline.…”

Section: Introductionmentioning

confidence: 99%

Dual Complexes of Cubical Subdivisions of ℝ n

Edelsbrunner

Kerber

2011

Discrete Comput Geom

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We use a distortion to define the dual complex of a cubical subdivision of R n as an n-dimensional subcomplex of the nerve of the set of n-cubes. Motivated by the topological analysis of high-dimensional digital image data, we consider such subdivisions defined by generalizations of quad-and oct-trees to n dimensions. Assuming the subdivision is balanced, we show that mapping each vertex to the center of the corresponding n-cube gives a geometric realization of the dual complex in R n .

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Section: Introductionmentioning

confidence: 99%

Dual Complexes of Cubical Subdivisions of ℝ n

Edelsbrunner

Kerber

2011

Discrete Comput Geom

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“…However, classification uncertainty is a major issue for the uptake of such technologies. Likewise the visualization community identifies uncertainty as a major challenge [8,9,16]. Uncertainty needs to be considered along with the data transformation steps, and the machine learning components are typically concerned.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty-aware estimation of population abundance using machine learning

et al. 2015

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“…For this reason, biomedical computing is ripe for research in what we will term "multi-field" visualization [115], in which combinations of the above fields are visualized together to elucidate their interactions. The challenges for such multi-field visualizations are many and include large-scale data, complicated geometries, and heterogeneous and anisotropic material properties.…”

Section: Multi-field Visualizationmentioning

confidence: 99%