1. Geometric clustering for line drawing simplification
Pascal Barla – Joëlle Thollot – François Sillion
ARTIS, GRAVIR/IMAG-INRIA

2. Introduction Line drawings are useful Complexity issues
Convey shape, tone, style
Used in illustration, art
Created in many different ways
Complexity issues
Artists know how to tune
complexity
Computers don’t
Often too many lines…
Geometric clustering for line drawing simplification

3. Problem statement Lines from various sources Simplification
Scanned drawing
Digital drawing
Image processing
Non-photorealistic rendering
Simplification
Smaller set of lines
Keep drawing’s overall structure
Scanned drawing of a hand
Just an overview…
Non-Photorealistic Rendering [Grabli]
Geometric clustering for line drawing simplification

4. Outline Related Work Methodology Clustering algorithm
Geometric strategies
Results
Conclusions
Geometric clustering for line drawing simplification

5. Related work Density reduction Indication Oversketching
trees in object-space [Deussen]
in image-space [Wilson][Grabli]
Indication
for complex textures
[Winkenbach]
Oversketching
smoothed [Baudel]
constrained [Igarashi]
Density reduction [Grabli]
Texture indication [Winkenbach]
Deussen use specific models
Oversketching tool [Baudel]
Geometric clustering for line drawing simplification

6. Related work levels of detail for NPR Overall limitations
In texture-space
Tonal Art Maps [Praun]
In object-space
WYSIWYG NPR [Kalnins]
Overall limitations
Specific solutions
Simplify = remove
No perceptual consideration
NPR with hatching patterns
Exhibiting LOD behaviors [Kalnins]
Geometric clustering for line drawing simplification

7. A schematic sun figure and the two largest parallel groupings [Rosin]
Related work
Perceptual organization [Boyer&Sarkar]
Only group lines
Based on human perception
Study criteria independently (e.g., parallelism)
Do not address simplification
BUT we want to take inspiration
A schematic sun figure and the two largest parallel groupings [Rosin]
Geometric clustering for line drawing simplification

8. Contributions / limitations
Identify common behavior
Oversketching, Density reduction and Levels of detail
Perceptually motivated
Various simplification strategies
Not only deletion
Limitations
Low-level
Static 2d drawings
Geometric clustering for line drawing simplification

9. Outline Methodology Related Work Clustering algorithm
Geometric strategies
Results
Conclusions
Geometric clustering for line drawing simplification

10. Methodology Single control param e 2 steps: simplification scale
Automatic Clustering
Common to envisioned
applications
Line creation
Geometric strategies
Application dependent
Clustering
Line creation
Geometric clustering for line drawing simplification

11. Methodology Input Clustering output Final output 2d Vectorized lines
Attributes: e.g., color
Static drawings
Clustering output
Line clusters
Final output
Vectorized lines
+ attributes
Clustering
Line creation
Geometric clustering for line drawing simplification

12. Methodology Proximity is not enough Forks Hatching groups
Unnatural fork behavior
Two simplifying lines keeping underlying
fork structure
Just to give an intuition...
Unnatural hatching group behavior
Three simplifying lines keeping underlying
stack structure and orientation
Geometric clustering for line drawing simplification

13. Methodology Perceptual grouping [Palmer]
Criteria: proximity, parallelism, continuation,
and color.
Integrated in clustering constraints
Definition of an e-group (see paper)
Palmer is a good survey.
We take inspiration from those criterions to def a eps-group
Clustering goal: set of eps-groups (the clusters)
Explain pink line
Geometric clustering for line drawing simplification

14. Outline Clustering algorithm Related Work Methodology
Geometric Strategies
Results
Conclusion
Geometric clustering for line drawing simplification

15. Clustering algorithm e Clustering = partition Greedy algorithm
Geometric clustering for line drawing simplification

16. Clustering algorithm e Clustering = partition Greedy algorithm
Clustering 2 lines/groups
Do they form an e-group ?
Error measure
Using attributes e
Geometric clustering for line drawing simplification

17. Clustering algorithm e Clustering a pair of lines
Example of an invalid pair
(pb. with parallelism)
Red = overlapping zone
Cross = end of zone
Geometric clustering for line drawing simplification

18. Clustering algorithm e Clustering a pair of lines
Example of an invalid pair
(pb. with parallelism)
Five valid configurations (see paper)
Correspond to e-groups on a pair of lines
Favor parallelism, continuation and proximity
Disc = line extremity
Valid configs based on number of extremities in a zone
Need to remember: valid pair iff one of those configs.
Continuation & closure details
Geometric clustering for line drawing simplification

19. Clustering algorithm Error measure Based on proximity
Normalized between 0 and 1
No anim 
Geometric clustering for line drawing simplification

20. Clustering algorithm Error measure Based on proximity
Normalized between 0 and 1
Can take attributes into
account (e.g. color)
Combined in a multiplicative
way
Geometric clustering for line drawing simplification

21. Clustering algorithm Implementation e Clustering graph
Graph node = line
Graph edge = valid pair
Error stored on edges e
Show eps
Geometric clustering for line drawing simplification

22. Clustering algorithm Implementation e Clustering graph
Graph node = line
Graph edge = valid pair
Error stored on edges
Greedy algo = edge collapse
Collapse min error edge
Delete degenerated edges
Update graph locally e
DARK GREEN !!!
Geometric clustering for line drawing simplification

23. Outline Geometric Strategies Related Work Methodology
Clustering algorithm
Geometric Strategies
Results
Conclusion
Add Geom section
Geometric clustering for line drawing simplification

24. Geometric strategies Geometric strategies Work on clustering output
May use clustering history
Many possibilities
Application dependent
Clusters
2 simple strategies
Show how to use in results
Geometric clustering for line drawing simplification

25. Geometric strategies Geometric strategies 2 basic strategies
Work on clustering output
May use clustering history
Many possibilities
Application dependent
2 basic strategies
Average line
Most significant line
Clusters
Average line strategy
Longest line strategy
Geometric clustering for line drawing simplification

26. Outline Results Related Work Methodology Clustering algorithm
Geometric Strategies
Results
Conclusion
Geometric clustering for line drawing simplification

27. Results Density reduction (scanned drawing) A single strategy
Average line
Scanned drawing
357 input lines
87 output clusters
Geometric clustering for line drawing simplification

28. Results Density reduction (3D model) Two different strategies
Average line for the
leaves
Longest line for the
inner part
531 input lines
256 clusters
Nb under figs
294 clusters
Geometric clustering for line drawing simplification

29. Results Density reduction (scanned drawing) Taking attributes
into account
Lab color threshold
Not too many details
Geometric clustering for line drawing simplification

30. Results Oversketching Apply simplification iteratively
Drawing sensitivity = e
Each new a sketch has its own sensitivity
Specific average line strategy
Give higher priority to last drawn line
See video…
Switch w/ LODs
Geometric clustering for line drawing simplification

31. Results Levels of detail
Geometric clustering for line drawing simplification

32. Results Levels of detail Increasing e Two different strategies
Simplify output of finer level
Two different strategies
Average line for contour
Longest line for hatching
See video…
Geometric clustering for line drawing simplification

33. Conclusions conclusionSSSSS
Geometric clustering for line drawing simplification

34. Conclusions 2-step approach is valuable Analysis of common behavior
Adaptation to application goals
3 application examples
Geometric clustering for line drawing simplification

35. Conclusions 2-step approach is valuable Perceptual grouping
Analysis of common behavior
Adaptation to application goals
3 application examples
Perceptual grouping
Incorporate a human vision model in NPR
Perception of a drawing
Geometric clustering for line drawing simplification

36. Conclusions Future work
Geometric clustering for line drawing simplification

37. Conclusions Future work Improve clustering
More perceptual criteria (e.g closeness)
Individual control for each criterion
Medium- and high-level processing (i.e drawing structure)
Geometric clustering for line drawing simplification

38. Conclusions Future works Create new applications
Automatic creation of Tonal Art Maps
Morph transitions for LODs
Clustering of 2d lines for animation
Simplification of lines lying on surfaces
More on temporal coherence -> common fate
Geometric clustering for line drawing simplification

39. Acknowledgements Gilles Debunne for the video
ARTIS team’s many reviewers
Lee Markosian and Chuck Hansen for “english cleanup”.
Video available on the web: give address
Geometric clustering for line drawing simplification