2. SIGGRAPH 2010 Structure-based ASCII Art Xuemiao Xu, Linling Zhang, Tien-Tsin Wong The Chinese University of Hong Kong

3. Since the 1860s, text art emerged… Since the 1860s, text art emerged…

4. From the 1970s, ASCII art has been widely u sed… From the 1970s, ASCII art has been widely used…

5. Today, ASCII art remains popular…

6. ASCII Art Classification Structure-based Tone-based –Halftone approaches Regarded as dithering –O’Grady and Rickard [2008] Dithering essentially

7. Structure-based –Manual Tedious ASCII Art Classification Automatic generation of structure-based ASCII art Tone-based –Halftone approaches Regarded as dithering –O’Grady and Rickard [2008] Dithering essentially

8. Arbitrary image content Main Challenge

9. Arbitrary image content Extremely limited character shapes Restrictive placement of characters Main Challenge

10. _ _) Matching Strategies Character matching –Misalignment tolerance –Transformation awareness

11. Matching Strategies Character matching –Misalignment tolerance –Transformation awareness Image deformation –Increase the chance of matching –Avoid over-deformation (_ _ _)

12. Character matching –Misalignment tolerance –Transformation awareness Image deformation –increase the chance of matching –Avoid over-deformation Alignment-insensitive shape similarity metric Constrained deformation Matching Strategies

13. Vectorized polylines Framework Input Rasterized image Current best matched characters Matching error map

14. ^ ) Current best matched characters Framework Matching error map Good matching Poor matching _ ; r ;

15. Current best matched characters Framework Matching error map (') (_)

16. Current best matched characters Framework Matching error map Deformation cost map Combined cost map

17. Current best matched characters Framework Deformed image Optimal ASCII art

18. Deformation cost of the vectorized images Objective Function E = D AISS. D deform Shape dissimilarity between ASCII and deformed images

19. Main Contribution Shape Matching Alignment-Insensitive Shape Similarity (AISS) Metric Constrained Deformation Deformation Metric

20. Matching requirements Misalignment tolerance Transformation awareness Scope Pattern recognition and image analysis, e.g. OCR AISS OCR O O 6 9

21. Misalignment tolerance Log-polar diagram (5x12) Design of AISS log-polar diagram Log-polar histogram

22. Transformation awareness Design of AISS h New sampling layout

23. Query Shape Context Our metric Translation and scale invariant Metrics Comparison (1) Transformation-invariant metrics

24. Over-emphasize overlapping Metrics Comparison (2) SSIM Query Our metric RMSE after blurring Alignment-sensitive metrics

25. Main Contribution Shape Matching Alignment-Insensitive Shape Similarity (AISS) Metric Constrained Deformation Deformation Metric

26. Constrained Deformation Local deformation constraint Accessibility constraint

27. A B r’ r Local Deformation Constraint B’ A’

28. Accessibility Constraint

29. Optimization Corresponding ASCII art InputVectorized image

30. Resolution=30X20 Resolution=20X15 Comparison InputO’Grady & RickardOur method

31. Test set 3:Test set 2: Input By Artist Our Method O’Grady & Rickard Test set 1: Clarity Artists7.18 Our method7.09 O’Grady & Rickard4.15 User Study Similarity Artists6.86 Our method7.36 O’Grady & Rickard4.42 Input By Artist Our Method O’Grady & Rickard

32. More Results

33. Other Results

36. Conclusion Mimic ASCII artists’ work by an optimization process Propose a novel alignment-insensitive shape similarity metric - also benefits pattern recognition Propose a new deformation metric to control over-deformation

37. Do not consider the stylish variation of line thickness within a font Do not handle proportional placement of characters Affected by the quality of the vectorization Limitation AA

38. Q&A