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 u sed… From the 1970s, ASCII art has been widely used…
Today, ASCII art remains popular…
ASCII Art Classification Structure-based Tone-based –Halftone approaches Regarded as dithering –O’Grady and Rickard [2008] Dithering essentially
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
Arbitrary image content Main Challenge
Arbitrary image content Extremely limited character shapes Restrictive placement of characters Main Challenge
_ _) Matching Strategies Character matching –Misalignment tolerance –Transformation awareness
Matching Strategies Character matching –Misalignment tolerance –Transformation awareness Image deformation –Increase the chance of matching –Avoid over-deformation (_ _ _)
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
Vectorized polylines Framework Input Rasterized image Current best matched characters Matching error map
^ ) Current best matched characters Framework Matching error map Good matching Poor matching _ ; r ;
Current best matched characters Framework Matching error map (') (_)
Current best matched characters Framework Matching error map Deformation cost map Combined cost map
Current best matched characters Framework Deformed image Optimal ASCII art
Deformation cost of the vectorized images Objective Function E = D AISS. D deform Shape dissimilarity between ASCII and deformed images
Main Contribution Shape Matching Alignment-Insensitive Shape Similarity (AISS) Metric Constrained Deformation Deformation Metric
Matching requirements Misalignment tolerance Transformation awareness Scope Pattern recognition and image analysis, e.g. OCR AISS OCR O O 6 9
Misalignment tolerance Log-polar diagram (5x12) Design of AISS log-polar diagram Log-polar histogram
Transformation awareness Design of AISS h New sampling layout
Query Shape Context Our metric Translation and scale invariant Metrics Comparison (1) Transformation-invariant metrics
Over-emphasize overlapping Metrics Comparison (2) SSIM Query Our metric RMSE after blurring Alignment-sensitive metrics
Main Contribution Shape Matching Alignment-Insensitive Shape Similarity (AISS) Metric Constrained Deformation Deformation Metric
Constrained Deformation Local deformation constraint Accessibility constraint
A B r’ r Local Deformation Constraint B’ A’
Accessibility Constraint
Optimization Corresponding ASCII art InputVectorized image
Resolution=30X20 Resolution=20X15 Comparison InputO’Grady & RickardOur method
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
More Results
Other Results
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
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
Q&A