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SIGGRAPH 2011 Paper Reading

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  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

  23. Overview

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

  25. Texture dataset • Texture-Color Relationship

  26. Continuous probability Density Estimation • GMM model

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

  28. results

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

  30. Overview • Training Stage • Color • Luminance gradient

  31. Binary Hierarchical Clustering

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

  33. Color and Tone style Enhancement • Image enhancement pipeline

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

  35. results

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

  37. 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] • ……

  38. Problems

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

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

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

  42. Results

  43. Watch the demo……

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