Sliceplorer: 1D Slices for Multi-dimensional Continuous Functions

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dc.contributor.authorTorsney-Weir, Thomasen_US
dc.contributor.authorSedlmair, Michaelen_US
dc.contributor.authorMöller, Torstenen_US
dc.contributor.editorHeer, Jeffrey and Ropinski, Timo and van Wijk, Jarkeen_US
dc.date.accessioned2017-06-12T05:22:28Z
dc.date.available2017-06-12T05:22:28Z
dc.date.issued2017
dc.description.abstractMulti-dimensional continuous functions are commonly visualized with 2D slices or topological views. Here, we explore 1D slices as an alternative approach to show such functions. Our goal with 1D slices is to combine the benefits of topological views, that is, screen space efficiency, with those of slices, that is a close resemblance of the underlying function. We compare 1D slices to 2D slices and topological views, first, by looking at their performance with respect to common function analysis tasks. We also demonstrate 3 usage scenarios: the 2D sinc function, neural network regression, and optimization traces. Based on this evaluation, we characterize the advantages and drawbacks of each of these approaches, and show how interaction can be used to overcome some of the shortcomings.en_US
dc.description.number3
dc.description.sectionheadersPlots, Plots, Plots
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.13177
dc.identifier.issn1467-8659
dc.identifier.pages167-177
dc.identifier.urihttps://doi.org/10.1111/cgf.13177
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13177
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.3 [Computer Graphics]
dc.subjectPicture/Image Generation
dc.subjectLine and curve generation
dc.titleSliceplorer: 1D Slices for Multi-dimensional Continuous Functionsen_US
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