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SIGGRAPH 2010
Structure-based ASCII Art
Xuemiao Xu, Linling Zhang, Tien-Tsin Wong
The Chinese University of Hong Kong
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Since the 1860s, text art emerged…
Since the 1860s, text art emerged…
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From the 1970s, ASCII art has been widely used…
From the 1970s, ASCII art has been widely used…
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Today, ASCII art remains popular…
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ASCII Art Classification
• Structure-based• Tone-based– Halftone approaches
Regarded as dithering
– O’Grady and Rickard [2008]
Dithering essentially
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• 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
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• Arbitrary image content
Main Challenge
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• Arbitrary image content
• Extremely limited character shapes• Restrictive placement of characters
Main Challenge
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__)
Matching Strategies
• Character matching – Misalignment tolerance– Transformation awareness
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Matching Strategies
• Character matching – Misalignment tolerance– Transformation awareness
• Image deformation – Increase the chance of matching – Avoid over-deformation
__)
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• 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
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Vectorized polylines
Framework
InputRasterized imageCurrent best matched characters
Matching error map
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^)
Current best matched characters
Framework
Matching error map
Good matching
Poor matching
_
;r ;
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Current best matched characters
Framework
Matching error map
(')(_)
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(_)
Current best matched characters
Framework
Matching error mapDeformation cost map Combined cost map
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Combined cost mapCurrent best matched characters
Framework
Deformed image Optimal ASCII art
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• Deformation cost of the vectorized images
Objective Function
E = DAISS . Ddeform
• Shape dissimilarity between ASCII and deformed images
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Main Contribution
• Shape MatchingAlignment-Insensitive Shape Similarity (AISS) Metric
• Constrained Deformation Deformation Metric
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• Matching requirements • Misalignment tolerance• Transformation awareness
• Scope • Pattern recognition and image analysis, e.g. OCR
AISSOCR
O O 6 9
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• Misalignment tolerance
Log-polar diagram (5x12)
Design of AISS
log-polar diagram
Log-polar histogram
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• Transformation awareness
Design of AISS
h
New sampling layout
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Query Shape Context Our metric
Translation and scale invariant
Metrics Comparison (1)
• Transformation-invariant metrics
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Over-emphasize overlapping
Metrics Comparison (2)
SSIMQuery Our metric RMSEafter blurring
• Alignment-sensitive metrics
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Main Contribution
• Shape MatchingAlignment-Insensitive Shape Similarity (AISS) Metric
• Constrained Deformation Deformation Metric
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Constrained Deformation
• Local deformation constraint
• Accessibility constraint
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AA
BB
r’r’
rr
Local Deformation Constraint
B’B’
A’A’
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Accessibility Constraint
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Optimization
Corresponding ASCII art Input Vectorized image
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Resolution=30X20 Resolution=20X15
Comparison
Input O’Grady & Rickard Our method
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Test set 3:Test set 2:
Input By Artist Our Method O’Grady & Rickard Input By Artist Our Method O’Grady & Rickard
Test set 1:
Clarity
Artists 7.18
Our method 7.09
O’Grady & Rickard 4.15
User Study
Similarity
Artists 6.86
Our method 7.36
O’Grady & Rickard 4.42
Input By Artist Our Method O’Grady &
Rickard
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More Results
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Other Results
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Other Results
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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
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• 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
A A
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Q&A