2. Multi-Class Blue Noise Sampling Li-Yi Wei Microsoft Research

3. smart↑ → research↑ Q: not so smart?

4. Blue noise random & uniform sample spacing r

5. r = 0.02 r = 0.02 0.01 0.01 0.02 single classmulti class

6. Multi-class blue noise class 0class 1total set

7. Object placement

8. Sensor layout cone/rod cellsRGB sensors

9. Color stippling RGBCMYB dots

10. A: smart↓ → popular↑ simpler algorithms less intimidating 5456 too smart # sig10 paper

11. 50 seconds old version

12. smart↑ → research↑ Q: not so smart?

13. Blue noise random & uniform sample spacing r

14. r = 0.02 r = 0.02 0.01 0.01 0.02 single classmulti class

15. Multi-class blue noise class 0class 1total set

16. Object placement

17. Sensor layout cone/rod cellsRGB sensors

18. Color stippling RGBCMYB dots

19. A: smart↓ → popular↑ simpler algorithms less intimidating 5456 too smart # sig10 paper

20. Old Version continue joke from FF 2008

21. Blue noise distribution random & uniform dart throwing Lloyd relaxation

22. X not novel [fast-forward SIG 2008] X not very useful texture synthesis inverse texture synthesis flip

23. overlay class 0class 1 X not uniform total set

24. Multi-class blue noise class 0class 1total set

25. Object placement

26. Sensor layout cone/rod cellsRGB sensors

27. Color stippling RGBCMYB dots

28. Backup

29. overlay X not uniform

30. Sensor layout continuous domaindiscrete domain retina

31. texture synthesis

32. inverse texture synthesis

33. Old slide from SIGGRAPH 2000 input (small) output (large) texture synthesis

34. New SIGGRAPH 2008 paper (just by flipping the old slide) input (large) output (small) inverse texture synthesis

35. Object placement Uniform distribution Red flower Yellow flower Entire set

36. Uniform per class class 0 Poisson disk class 1 Poisson disk total set OOX

37. Uniform total set total set Poisson disk class 0class 1 XXO

38. Our method class 0 Poisson disk class 1 Poisson disk total set Poisson disk OOO