siggraph 2010 structure-based ascii art xuemiao xu, linling zhang, tien-tsin wong the chinese...
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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…
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
InputRasterized imageCurrent 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 mapDeformation cost map Combined cost map
Combined cost mapCurrent best matched characters
Framework
Deformed image Optimal ASCII art
• Deformation cost of the vectorized images
Objective Function
E = DAISS . Ddeform
• Shape dissimilarity between ASCII and deformed images
Main Contribution
• Shape MatchingAlignment-Insensitive Shape Similarity (AISS) Metric
• Constrained Deformation Deformation Metric
• Matching requirements • Misalignment tolerance• Transformation awareness
• Scope • Pattern recognition and image analysis, e.g. OCR
AISSOCR
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)
SSIMQuery Our metric RMSEafter blurring
• Alignment-sensitive metrics
Main Contribution
• Shape MatchingAlignment-Insensitive Shape Similarity (AISS) Metric
• Constrained Deformation Deformation Metric
Constrained Deformation
• Local deformation constraint
• Accessibility constraint
AA
BB
r’r’
rr
Local Deformation Constraint
B’B’
A’A’
Accessibility Constraint
Optimization
Corresponding ASCII art Input Vectorized image
Resolution=30X20 Resolution=20X15
Comparison
Input O’Grady & Rickard Our method
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
More Results
Other 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
A A
Q&A
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