“Structure-based ASCII art” by Xu, Zhang and Wong

The wide availability and popularity of text-based communication channels encourage the usage of ASCII art in representing images. Existing tone-based ASCII art generation methods lead to halftone-like results and require high text resolution for display, as higher text resolution offers more tone variety. This paper presents a novel method to generate structure-based ASCII art that is currently mostly created by hand. It approximates the major line structure of the reference image content with the shape of characters. Representing the unlimited image content with the extremely limited shapes and restrictive placement of characters makes this problem challenging. Most existing shape similarity metrics either fail to address the misalignment in real-world scenarios, or are unable to account for the differences in position, orientation and scaling. Our key contribution is a novel alignment-insensitive shape similarity (AISS) metric that tolerates misalignment of shapes while accounting for the differences in position, orientation and scaling. Together with the constrained deformation approach, we formulate the ASCII art generation as an optimization that minimizes shape dissimilarity and deformation. Convincing results and user study are shown to demonstrate its effectiveness.

1. Arkin, E. M., Chew, L. P., Huttenlocher, D. P., Kedem, K., and Mitchell, J. S. B. 1991. An efficiently computable metric for comparing polygonal shapes. IEEE Trans. Pattern Anal. Mach. Intell. 13, 3, 209–216. Google ScholarDigital Library
2. Au, D., 1995. Make a start in ASCII art. http://www.ludd.luth.se/~vk/pics/ascii/junkyard/techstuff/tutori als/Daniel_Au.html.Google Scholar
3. Bayer, B. 1973. An optimum method for two-level rendition of continuous-tone pictures. In IEEE International Conference on Communications, IEEE, (26-11)–(26-15).Google Scholar
4. Belongie, S., Malik, J., and Puzicha, J. 2002. Shape matching and object recognition using shape contexts. IEEE Tran. Pattern Analysis and Machine Intelligence 24, 4, 509–522. Google ScholarDigital Library
5. CJRandall, 2003. alt.ascii-art: Frequently asked questions. http://www.ascii-art.de/ascii/faq.html.Google Scholar
6. Cohen, I., Ayache, N., and Sulger, P. 1992. Tracking points on deformable objects using curvature information. In ECCV ’92, Springer-Verlag, London, UK, 458–466. Google ScholarDigital Library
7. Crawford, R., 1994. ASCII graphics techniques v1.0. http://www.ludd.luth.se/~vk/pics/ascii/junkyard/techstuff/tutori als/Rowan_Crawford.html.Google Scholar
8. Davis, I. E., 1986. theDraw. TheSoft Programming Services.Google Scholar
9. DeFusco, R., 2007. MosASCII. freeware.Google Scholar
10. Floyd, R. W., and Steinberg, L. 1974. An adaptive algorithm for spatial grey scale. In SID Int. Sym. Digest Tech. Papers, 36–37.Google Scholar
11. Gal, R., Sorkine, O., Popa, T., Sheffer, A., and Cohen-Or, D. 2007. 3d collage: Expressive non-realistic modeling. In In Proc. of 5th NPAR. Google ScholarDigital Library
12. Gebhard, M., 2009. JavE. freeware.Google Scholar
13. Goh, W.-B. 2008. Strategies for shape matching using skeletons. Comput. Vis. Image Underst. 110, 3, 326–345. Google ScholarDigital Library
14. Hsu, S.-C., and Wong, T.-T. 1995. Simulating dust accumulation. IEEE Comput. Graph. Appl. 15, 1, 18–22. Google ScholarDigital Library
15. Kang, H., Lee, S., and Chui, C. K. 2007. Coherent line drawing. In ACM Symposium on Non-Photorealistic Animation and Rendering (NPAR), 43–50. Google ScholarDigital Library
16. Klose, L. A., and McIntosh, F., 2000. Pictexter. AxiomX.Google Scholar
17. Milios, E. E. 1989. Shape matching using curvature processes. Comput. Vision Graph. Image Process. 47, 2, 203–226. Google ScholarDigital Library
18. Miller, G. 1994. Efficient algorithms for local and global accessibility shading. In Proceedings of SIGGRAPH 94, 319–326. Google ScholarDigital Library
19. Mori, G., Belongie, S., and Malik, J. 2005. Efficient shape matching using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 11, 1832–1837. Google ScholarDigital Library
20. O’Grady, P. D., and Rickard, S. T. 2008. Automatic ASCII art conversion of binary images using non-negative constraints. In Proceedings of Signals and Systems Conference 2008 (ISSC 2008), 186–191.Google Scholar
21. Sundar, H., Silver, D., Gagvani, N., and Dickinson, S. 2003. Skeleton based shape matching and retrieval. SMI ’03, 130. Google ScholarDigital Library
22. Torsello, A., and Hancock, E. R. 2004. A skeletal measure of 2d shape similarity. Computer Vision and Image Understanding 95, 1, 1–29. Google ScholarDigital Library
23. Ulichney, R. A. MIT Press.Google Scholar
24. Wakenshaw, H., 2000. Hayley Wakenshaw’s ASCII art tutorial. http://www.ludd.luth.se/~vk/pics/ascii/junkyard/techstuff/tutori als/Hayley_Wakenshaw.html.Google Scholar
25. Wang, Z., Bovik, A. C., Sheikh, H. R., Member, S., Simoncelli, E. P., and Member, S. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 600–612. Google ScholarDigital Library
26. Wikipedia, 2009. ASCII art. http://en.wikipedia.org/wiki/Ascii_art.Google Scholar
27. Zahn, C. T., and Roskies, R. Z. 1972. Fourier descriptors for plane closed curves. IEEE Tran. Computers 21, 3, 269–281. Google ScholarDigital Library