Data-Driven Iconification
Inhaltsverzeichnis
Reference
Yiming Liu, Aseem Agarwala, Jingwan Lu, and Szymon Rusinkiewicz: Data-Driven Iconification. In: Computational Aesthetics 2016 CAE'16, 113-124.
DOI
http://dx.doi.org/10.2312/exp.20161070
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.
Extended Abstract
Bibtex
@inproceedings{Liu:2016:DI:2981324.2981341,
author = {Liu, Yiming and Agarwala, Aseem and Lu, Jingwan and Rusinkiewicz, Szymon},
title = {Data-driven Iconification},
booktitle = {Proceedings of the Joint Symposium on Computational Aesthetics and Sketch Based Interfaces and Modeling and Non-Photorealistic Animation and Rendering},
series = {Expresive '16},
year = {2016},
location = {Lisbon, Portugal},
pages = {113--124},
numpages = {12},
url = {http://dx.doi.org/10.2312/exp.20161070 http://dl.acm.org/citation.cfm?id=2981324.2981341 http://de.evo-art.org/index.php?title=Data-Driven_Iconification},
acmid = {2981341},
publisher = {Eurographics Association},
address = {Aire-la-Ville, Switzerland, Switzerland},
}
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http://dl.acm.org/citation.cfm?id=2981341&CFID=558819604&CFTOKEN=68186175
