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SIGGRAPH 2010

SIGGRAPH 2010. Structure-based ASCII Art Xuemiao Xu, Linling Zhang, Tien-Tsin Wong The Chinese University of Hong Kong. Since the 1860s, text art emerged…. Since the 1860s, text art emerged…. From the 1970s, ASCII art has been widely used…. From the 1970s, ASCII art has been widely used….

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SIGGRAPH 2010

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

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

  3. From the 1970s, ASCII art has been widely used… From the 1970s, ASCII art has been widely used…

  4. Today, ASCII art remains popular…

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

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

  7. Main Challenge • Arbitrary image content

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

  9. Matching Strategies _ _) • Character matching • Misalignment tolerance • Transformation awareness

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

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

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

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

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

  15. Framework (_) Current best matched characters Matching error map Combined cost map Deformation cost map

  16. Framework Current best matched characters Deformed image Combined cost map Optimal ASCII art

  17. Objective Function • Shape dissimilaritybetween ASCII and deformed images • Deformationcost of the vectorized images E = DAISS Ddeform .

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

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

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

  21. Design of AISS • Transformation awareness New sampling layout h

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

  23. Metrics Comparison (2) • Alignment-sensitive metrics RMSEafter blurring Our metric SSIM Query Over-emphasize overlapping

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

  25. Constrained Deformation • Local deformation constraint • Accessibility constraint

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

  27. Accessibility Constraint

  28. Optimization Vectorized image Input Corresponding ASCII art

  29. Comparison Input O’Grady & Rickard Our method Resolution=30X20 Resolution=20X15

  30. User Study Test set 3: Test set 2: Test set 1: Input By Artist Our Method O’Grady & Rickard Input By Artist Our Method O’Grady & Rickard Input By Artist Our Method O’Grady & Rickard

  31. More Results

  32. Other Results

  33. Other Results

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

  35. A A Limitation • 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

  36. Q&A

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