TY - GEN
T1 - Texture-aware ASCII art synthesis with proportional fonts
AU - Xu, Xuemiao
AU - Zhong, Linyuan
AU - Xie, Minshan
AU - Qin, Jing
AU - Chen, Yilan
AU - Jin, Qiang
AU - Wong, Tien Tsin
AU - Han, Guoqiang
N1 - Funding Information:
This work was supported by grants of NSFC and NSFC-Guangdong (Grant No. 61103120, 61472145, 61272293, 61233012 and S2013010014973), Doctoral Program of Higher Education of China (Grant No. 20110172120026), Shenzhen Nanshan IIEF(Grant No. KC2013ZDZJ0007A), Shenzhen Basic Research Project (Grant No. JCYJ20120619152326448), RGC of Hong Kong (Grant No. 417913), Guangzhou Novo Program of Science and Technology (Grant No. 0501-330) and Guangzhou Key Lab of cloud computing technology and safety evaluation.
Publisher Copyright:
© The Eurographics Association 2015.
PY - 2015/6/20
Y1 - 2015/6/20
N2 - We present a fast structure-based ASCII art generation method that accepts arbitrary images (real photograph or hand-drawing) as input. Our method supports not only fixed width fonts, but also the visually more pleasant and computationally more challenging proportional fonts, which allows us to represent challenging images with a variety of structures by characters. We take human perception into account and develop a novel feature extraction scheme based on a multi-orientation phase congruency model. Different from most existing contour detection methods, our scheme does not attempt to remove textures as much as possible. Instead, it aims at faithfully capturing visually sensitive features, including both main contours and textural structures, while suppressing visually insensitive features, such as minor texture elements and noise. Together with a deformation-tolerant image similarity metric, we can generate lively and meaningful ASCII art, even when the choices of character shapes and placement are very limited. A dynamic programming based optimization is proposed to simultaneously determine the optimal proportional-font characters for matching and their optimal placement. Experimental results show that our results outperform state-of-the-art methods in term of visual quality.
AB - We present a fast structure-based ASCII art generation method that accepts arbitrary images (real photograph or hand-drawing) as input. Our method supports not only fixed width fonts, but also the visually more pleasant and computationally more challenging proportional fonts, which allows us to represent challenging images with a variety of structures by characters. We take human perception into account and develop a novel feature extraction scheme based on a multi-orientation phase congruency model. Different from most existing contour detection methods, our scheme does not attempt to remove textures as much as possible. Instead, it aims at faithfully capturing visually sensitive features, including both main contours and textural structures, while suppressing visually insensitive features, such as minor texture elements and noise. Together with a deformation-tolerant image similarity metric, we can generate lively and meaningful ASCII art, even when the choices of character shapes and placement are very limited. A dynamic programming based optimization is proposed to simultaneously determine the optimal proportional-font characters for matching and their optimal placement. Experimental results show that our results outperform state-of-the-art methods in term of visual quality.
UR - http://www.scopus.com/inward/record.url?scp=84985006571&partnerID=8YFLogxK
U2 - 10.2312/exp.20151191
DO - 10.2312/exp.20151191
M3 - Conference article published in proceeding or book
AN - SCOPUS:84985006571
T3 - Expressive 2015: Joint Symposium of Computational Aesthetics, CAe, Non-Photorealistic Animation and Rendering, NPAR, Sketch-Based Interfaces and Modeling, SBIM
SP - 183
EP - 193
BT - Expressive 2015
A2 - Fellner, Dieter W.
PB - Association for Computing Machinery, Inc
T2 - Workshop on Non-Photorealistic Animation and Rendering, NPAR 2015
Y2 - 20 June 2015 through 22 June 2015
ER -