1 / 39

Geometric clustering for line drawing simplification

Geometric clustering for line drawing simplification. Pascal Barla – Joëlle Thollot – François Sillion ARTIS, GRAVIR/IMAG-INRIA. Introduction. Line drawings are useful Convey shape, tone, style Used in illustration, art Created in many different ways Complexity issues

apaula
Download Presentation

Geometric clustering for line drawing simplification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. Introduction • Line drawings are useful • 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 • Scanned drawing • Digital drawing • Image processing • Non-photorealistic rendering • Simplification • Smaller set of lines • Keep drawing’s overall structure Scanned drawing of a hand 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 • 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] Oversketching tool [Baudel] Geometric clustering for line drawing simplification

  6. Related work • levels of detail for NPR • 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. Related work • Perceptual organization [Boyer&Sarkar] • Only group lines • Based on human perception • Study criteria independently (e.g., parallelism) A schematic sun figure and the two largest parallel groupings [Rosin] Geometric clustering for line drawing simplification

  8. Contributions / limitations • Contributions • 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 • Related Work • Methodology • Clustering algorithm • Geometric strategies • Results • Conclusions Geometric clustering for line drawing simplification

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

  11. e Methodology • Input • 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 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) Geometric clustering for line drawing simplification

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

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

  16. Clustering algorithm • 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) 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 Geometric clustering for line drawing simplification

  19. Clustering algorithm • Error measure • Based on proximity • Normalized between 0 and 1 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) • Normalized between 0 and 1 • Combined in a multiplicative way Geometric clustering for line drawing simplification

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

  22. Clustering algorithm • Implementation • 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 Geometric clustering for line drawing simplification

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

  24. Geometric strategies • Geometric strategies • Work on clustering output • May use clustering history • Many possibilities • Application dependent Clusters Geometric clustering for line drawing simplification

  25. Geometric strategies • Geometric 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 • 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 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 294 clusters Geometric clustering for line drawing simplification

  29. Results • Density reduction (scanned drawing) • Taking attributes into account • Lab color threshold 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… Geometric clustering for line drawing simplification

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

  32. Results • Levels of detail • Increasing e • 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 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 • 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 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”. Geometric clustering for line drawing simplification

More Related