External Labeling Techniques: A Taxonomy and Survey

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dc.contributor.authorBekos, Michael A.en_US
dc.contributor.authorNiedermann, Benjaminen_US
dc.contributor.authorNöllenburg, Martinen_US
dc.contributor.editorLaramee, Robert S. and Oeltze, Steffen and Sedlmair, Michaelen_US
dc.date.accessioned2019-06-02T18:23:02Z
dc.date.available2019-06-02T18:23:02Z
dc.date.issued2019
dc.description.abstractExternal labeling is frequently used for annotating features in graphical displays and visualizations, such as technical illustrations, anatomical drawings, or maps, with textual information. Such a labeling connects features within an illustration by thin leader lines with their labels, which are placed in the empty space surrounding the image. Over the last twenty years, a large body of literature in diverse areas of computer science has been published that investigates many different aspects, models, and algorithms for automatically placing external labels for a given set of features. This state-of-the-art report introduces a first unified taxonomy for categorizing the different results in the literature and then presents a comprehensive survey of the state of the art, a sketch of the most relevant algorithmic techniques for external labeling algorithms, as well as a list of open research challenges in this multidisciplinary research field.en_US
dc.description.documenttypestar
dc.description.number3
dc.description.sectionheadersGraphs and Labels
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume38
dc.identifier.doi10.1111/cgf.13729
dc.identifier.issn1467-8659
dc.identifier.pages833-860
dc.identifier.urihttps://doi.org/10.1111/cgf.13729
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13729
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisualization techniques
dc.subjectInformation visualization
dc.subjectTheory of computation
dc.subjectComputational geometry
dc.titleExternal Labeling Techniques: A Taxonomy and Surveyen_US
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