Visualizing and modeling scattered multivariate data

Nielson, Gregory M.; Foley, Thomas A.; Hamann, Bernd; Lane, David A.

doi:10.1109/38.79453

Cited by 60 publications

(20 citation statements)

References 11 publications

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“…For example, when 3D methods are used, data are accurately mapped to points in space, but users have hard times seeing them and navigate in 3D spaces. Several previous works [3,9,18] have explored different methods for rendering multivariate data sets. Our tool, Tradeoff Map, is novel in terms of its unique metaphor to a balance during a tradeoff process.…”

Section: Visualization In Multivariate Spacesmentioning

confidence: 99%

Design visual thinking tools for mixed initiative systems

Pu¹,

Lalanne

2002

Proceedings of the 7th International Conference on Intelligent User Interfaces

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Visual thinking tools are visualization-enabled mixed initiative systems that empower people in solving complex problems by engaging them in the entire resolution process, suggesting appropriate actions with visual cues, and reducing their cognitive load with visual representations of their tasks. At the same time, the visual interaction style provides an alternative to the dialog-based model employed in most mixed-initiative (MI) systems. Visual thinking tools avoid complex analyses of turn taking, and put users in control all the time. We are especially interested in implementing visual "affordances" in such systems and present three examples used in COMIND, a visual MI system that we have developed. We show how humans can more effectively concentrate on synthesizing problems, selecting resolution paths that were unseen by the machine, and reformulating problems if solutions cannot be found or are unsatisfactory. We further discuss our evaluation of the techniques at the end of the paper.

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Section: Visualization In Multivariate Spacesmentioning

confidence: 99%

Design visual thinking tools for mixed initiative systems

Pu¹,

Lalanne

2002

Proceedings of the 7th International Conference on Intelligent User Interfaces

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“…The domain surfaces are usually assumed to be spherical, convex or genus zero. The surface-on-surface are not always polynomial [15], [70] or rather higher order polynomial [85] or a large number of pieces [1] HolTmann and Hopcroft [48,50,49] use similar affine and projective potential methods to blend two or three algebraic surfaces with quadrics. The blending surfaces can be derived by substitution from a parametric base curve.…”

Section: Implicit Scattered Datamentioning

confidence: 99%

Splines and geometric modeling

Bajaj¹,

Evans

2004

Handbook of Discrete and Computational Geometry, Second Edition

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“…[7]). Some recent work under the title surfaces on surfaces addresses the case when M is a general curved surface such as the skin of an airplane [16].…”

Section: Surface Reconstruction Vs Function Reconstructionmentioning

confidence: 99%

Surface reconstruction from unorganized points

Hoppe

DeRose

Duchamp

et al. 1992

Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques

1,569

263

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We describe and demonstrate an algorithm that takes as input an unorganized set of points fx1; : : : ; xng IR 3 on or near an unknown manifold M, and produces as output a simplicial surface that approximates M. Neither the topology, the presence of boundaries, nor the geometry of M are assumed to be known in advance -all are inferred automatically from the data. This problem naturally arises in a variety of practical situations such as range scanning an object from multiple view points, recovery of biological shapes from two-dimensional slices, and interactive surface sketching.

show abstract

Visualizing and modeling scattered multivariate data

Cited by 60 publications

References 11 publications

Design visual thinking tools for mixed initiative systems

Design visual thinking tools for mixed initiative systems

Splines and geometric modeling

Surface reconstruction from unorganized points

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