Data-Driven Iconification

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Date
2016
Authors
Liu, Yiming
Agarwala, Aseem
Lu, Jingwan
Rusinkiewicz, Szymon
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description

        
@inproceedings{
10.2312:exp.20161070
,
booktitle = {
Non-Photorealistic Animation and Rendering
},
editor = {
Pierre Bénard and Holger Winnemöller
},
title = {{
Data-Driven Iconification
}},
author = {
Liu, Yiming
 and 
Agarwala, Aseem
 and 
Lu, Jingwan
 and 
Rusinkiewicz, Szymon
},
year = {
2016
},
publisher = {
The Eurographics Association
},
ISSN = {
-
},
ISBN = {
978-3-03868-002-4
},
DOI = {
10.2312/exp.20161070
}
}
Citation
URI
https://doi.org/10.2312/exp.20161070
Collections
NPAR16