SIGGRAPH 2011 Paper Reading

1. SIGGRAPH 2011 Paper Reading Shen Xiaoyong 2011/07/05

2. Papers • Colorful • Color Compatibility from Large Datasets (Siggraph 2011) • Edge-Aware Color Appearance (TOG Paper) • Data-Driven Image Color Theme Enhancement (Siggraph Asia 2010) • Example-based Image Color and Tone Style Enhancement (Siggraph 2011) • Image Synthesis • Discrete Element Textures (Siggraph 2011) • Image Guided Weathering: A New Approach Applied to Flow Phenomena (TOG Paper) • Image Application • Automatic Generation of Maps (Siggraph Asia 2010) • Switchable Primaries Using Shiftable Layers of Color Filter Arrays

3. PhD Student Senior Research Scientist Associate Professor

4. Abstract • Studies color compatibility theories using large datasets • Develops new tools for choosing colors

5. Three Datasets • Adobe Kuler • http://kuler.adobe.com/ • About 104426 five color themes • COLOURLovers • http://www.colourlovers.com/ • Over one million 2-5 color themes • Amazon Mechanical Turk

6. Color Themes

7. Data Analysis • Distribution of Themes, form a cluster

8. Preferred Colors • Prefer warm hues • Red, orange, yellow…

9. Color pairs • Warm hues around yellow and red have strong adjacency

10. Hue Entropy • 2~3 hues theme seems best

11. Learning Color Compatibility • Input data comprises pairs • Theme • rating • Regression

12. Applications • Theme optimization • Theme extraction • Color suggestion

13. TOBIAS RITSCHEL JAN KAUTZ

14. Abstract • Studied the appearance of color under different degrees of edge smoothness

15. Main Contributions • Appearance measurement data of color with edge variation • A spatial model taking into account edge variations

16. Psychophysical Experiment • Magnitude Estimation • Lightness • Colorfulness • Hue • Experiment

17. Psychophysical Experiment • Hue cancellation • The effect of the background color • Experiment

18. Experiment results (1) • Lightness • Affected by the smoothness of the edge • More softer more strongly • Induced more towards the background lightness

19. Experiment results (2) • Colorfulness • Shows a subtle change according to edge smoothness

20. Experiment results (3) • Hue • Seems unaffected

21. Modeling • The change lightness • Linear regression

22. Some applications

24. Overview

25. Data-driven knowledge extraction • Color theme dataset • From Adobe Kuler • Image dataset • From Flickr • Manually get the relationship between image color and theme

26. Texture dataset • Texture-Color Relationship

27. Continuous probability Density Estimation • GMM model

28. Color Optimization • Energy • First term • Color constraints • Second term • Texture constraints for naturalness and realism • Third term • Theme constraints

29. results

32. Abstract • Develop a learning based method to achieve color and tone enhancement • Related work • Gradient domain image editing • Color transfer • ……

33. Overview • Training Stage • Color • Luminance gradient

34. Binary Hierarchical Clustering

35. Two kinds mapping • Local color mapping • Learning mapping for luminance gradients • Using the gamma correction

36. Color and Tone style Enhancement • Image enhancement pipeline

37. Applications • Photo enhancement by style transfer • Learning color and tone style from photographers

38. results

40. Abstract • Present discrete element texture • Data-driven method for synthesizing • Small input exemplar and large output domain

41. Related work • Example-based texturing • Pixels [Wei and Levoy 2009] • Vertices [Turk 2001] • Voxels [Kopf et al. 2007] • …… • Geometry synthesis • Surface meshes [Zhou al. 2006] • volumetric models [Bhat et al. 2004] • ……

42. Problems

44. Texture Representation • Element Samples • Each sample • Position • Attributes

45. Neighborhood Metric • the spatial neighborhood around • Measure the distance

46. Basic Synthesis • Energy function Optional application terms (boundary conditions) Neighborhood distance

47. Results

48. Watch the demo……

50. Main Ideas • Present a new approach to weathering, which uses photographs to drive a simulation