Paper Info
Title
A probabilistic framework for component-based vector graphics.
Abstract
We propose a framework for data-driven manipulation and synthesis of component-based vector graphics. Using labelled vector graphical images of a given type of object as input, our processing pipeline produces training data, learns a probabilistic Bayesian network from that training data, and offer various data-driven vector-related tools using synthesis functions. The tools ranges from data-driven vector design to automatic synthesis of vector graphics. Our tools were well received by designers, our model provides good generalisation performance, also from small data sets, and our method for synthesis produces vector graphics deemed significantly more plausible compared with alternative methods.
Year
DOI
Venue
2017
10.1111/cgf.13285
COMPUTER GRAPHICS FORUM
Field
DocType
Volume
Computer vision,Vector graphics,3D computer graphics,2D computer graphics,Computer science,Theoretical computer science,Image tracing,Artificial intelligence,Graphics software,Computer graphics,Turtle graphics,Computer Graphics Metafile
Journal
36.0
Issue
ISSN
Citations 
7.0
0167-7055
0
PageRank 
References 
Authors
0.34
21
1
Name
Order
Citations
PageRank
Henrik Lieng1143.04