Real-time Image Vectorization on GPU

Xiaoliang Xiong, Jie Feng, Bingfeng Zhou

2016

Abstract

In this paper, we present a novel algorithm to convert a raster image into its vector form. Different from the state-of-art methods, we explore the potential parallelism that exists in the problem and propose an algorithm suitable to be accelerated by the graphics hardware. In our algorithm, the vectorization task is decomposed into four steps: detecting the boundary pixels, pre-computing the connectivity relationship of detected pixels, organizing detected pixels into boundary loops and vectorizing each loop into line segments. The boundary detection and connectivity pre-computing are parallelized owing to the independence between scanlines. After a sequential boundary pixels organizing, all loops are vectorized concurrently. With a GPU implementation, the vectorization can be accomplished in real-time. Then, the image can be represented by the vectorized contour. This real-time vectorization algorithm can be used on images with multiple silhouettes and multi-view videos. We demonstrate the efficiency of our algorithm with several applications including cartoon and document vectorization.

Paper Citation

in Harvard Style

Xiong X., Feng J. and Zhou B. (2016). Real-time Image Vectorization on GPU . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 143-150. DOI: 10.5220/0005668901410148

in Bibtex Style

@conference{grapp16,
author={Xiaoliang Xiong and Jie Feng and Bingfeng Zhou},
title={Real-time Image Vectorization on GPU},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2016)},
year={2016},
pages={143-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005668901410148},
isbn={978-989-758-175-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2016)
TI - Real-time Image Vectorization on GPU
SN - 978-989-758-175-5
AU - Xiong X.
AU - Feng J.
AU - Zhou B.
PY - 2016
SP - 143
EP - 150
DO - 10.5220/0005668901410148