Data-Driven Iconification
| dc.contributor.author | Liu, Yiming | en_US |
| dc.contributor.author | Agarwala, Aseem | en_US |
| dc.contributor.author | Lu, Jingwan | en_US |
| dc.contributor.author | Rusinkiewicz, Szymon | en_US |
| dc.contributor.editor | Pierre Bénard and Holger Winnemöller | en_US |
| dc.date.accessioned | 2016-05-04T16:05:50Z | |
| dc.date.available | 2016-05-04T16:05:50Z | |
| dc.date.issued | 2016 | en_US |
| dc.description.abstract | Pictograms (icons) are ubiquitous in visual communication, but creating the best icon is not easy: users may wish to see a variety of possibilities before settling on a final form, and they might lack the ability to draw attractive and effective pictograms by themselves. We describe a system that synthesizes novel pictograms by remixing portions of icons retrieved from a large online repository. Depending on the user's needs, the synthesis can be controlled by a number of interfaces ranging from sketch-based modeling and editing to fully-automatic hybrid generation and scribble-guided montage. Our system combines icon-specific algorithms for salient-region detection, shape matching, and multi-label graph-cut stitching to produce results in styles ranging from line drawings to solid shapes with interior structure. | en_US |
| dc.description.sectionheaders | Synthesis | en_US |
| dc.description.seriesinformation | Non-Photorealistic Animation and Rendering | en_US |
| dc.identifier.doi | 10.2312/exp.20161070 | en_US |
| dc.identifier.isbn | 978-3-03868-002-4 | en_US |
| dc.identifier.issn | - | en_US |
| dc.identifier.pages | 113-124 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/exp.20161070 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | I.3.3 [Computer Graphics] | en_US |
| dc.subject | Picture/Image Generation | en_US |
| dc.title | Data-Driven Iconification | en_US |