1. Matting, Transparency, and Illumination
cs129: Computational Photography
James Hays, Brown, Spring 2011
Slides from Alexei Efros

2. How does Superman fly? Super-human powers? OR
Image Blending (project 2)
Image Matting?
Matting vs Blending

3. Physics of Alpha Matting
Semi-transparent objects
Or, why segmentation isn’t good enough.
Pixels too large

4. alpha channel Add one more channel:
Image(R,G,B,alpha) Sprite!
Encodes transparency (or pixel coverage):
Alpha = 1: opaque object (complete coverage)
Alpha = 0: transparent object (no coverage)
0<Alpha<1: semi-transparent (partial coverage)
Example: alpha = 0.7
Partial coverage or semi-transparency

5. “Pulling a Matte” Problem Definition: Hard problem
The separation of an image C into
A foreground object image Co,
a background image Cb,
and an alpha matte a
Co and a can then be used to composite the foreground object into a different image
Hard problem
Even if alpha is binary, this is hard to do automatically (background subtraction problem)
For movies/TV, manual segmentation of each frame is infeasible
Need to make a simplifying assumption…

6. Average/Median Image
What can we do with this?

7. Background Subtraction- =

8. Background Subtraction
A largely unsolved problem…
One video
frame
Estimated
background
Difference
Image
Thresholded
Foreground
on blue

9. Blue Screen

10. Blue Screen matting Most common form of matting in TV studios & movies
Petros Vlahos invented blue screen matting in the 50s. His Ultimatte® is still the most popular equipment. He won an Oscar for lifetime achievement.
A form of background subtraction:
Need a known background
Compute alpha as SSD(C,Cb) > threshold
Or use Vlahos’ formula: a = 1-p1(B-p2G)
Hope that foreground object doesn’t look like background
no blue ties!
Why blue?
Why uniform?

11. The Ultimatte
p1 and p2

12. Semi-transparent mattes
What we really want is to obtain a true alpha matte, which involves semi-transparency
Alpha between 0 and 1

13. Matting Problem: Mathematical Definition

14. Why is general matting hard?

15. Solution #1: No Blue!

16. Solution #2: Gray or Flesh

17. Triangulation Matting (Smith & Blinn)
How many equations?
How many unknowns?
Does the background need to be constant color?

18. The Algorithm

19. Triangulation Matting Examples

20. More Examples

21. More examples

22. Problems with Matting Solution: Modify the Matting Equation
Images do not look realistic
Lack of Refracted Light
Lack of Reflected Light
Solution:
Modify the Matting Equation

23. Environment Matting and Compositing
slides by Jay Hetler
Douglas E. Zongker ~ Dawn M. Werner ~ Brian Curless ~ David H. Salsin
SIGGRAPH 99

24. Environment Matting Equation
C = F + (1- a)B + F
C ~ Color
F ~ Foreground color
B ~ Background color
a ~ Amount of light that passes through the foreground
F ~ Contribution of light from Environment that travels through the object
Alpha holds values from zero to one…

25. Explanation of F
R – reflectance image
T – Texture image

26. Series of structured backgrounds
Environment Mattes

27. How much better is Environment Matting?
Alpha Matte Environment Matte Photograph

28. How much better is Environment Matting?
Alpha Matte Environment Matte Photograph

29. Shree K. Nayar Gurunandan G. Krishnan Columbia University
Fast Separation of Direct and Global Images Using High Frequency Illumination
Shree K. Nayar
Gurunandan G. Krishnan
Columbia University
Michael D. Grossberg
City College of New York
Ramesh Raskar
MERL
SIGGRAPH Conference
Boston, July 2006
Support: ONR, NSF, MERL

30. Direct and Global Illumination
participating
medium
D : Volumetric
translucent
surface E
E : Diffusion C
C : Subsurface A
A : Direct
surface
source B
B : Interrelection P
camera

31. Direct and Global Components: Interreflections
surface
source j
BRDF and geometry i
camera
direct
global
radiance

32. High Frequency Illumination Pattern
surface
source i
camera
fraction of activated source elements +

33. - High Frequency Illumination Pattern + i surface source camera
fraction of activated source elements

34. Separation from Two Images
direct
global

35. Diffuse
Interreflections
Specular
Volumetric
Scattering
Subsurface
Diffusion

36. Scene

37. Scene
Direct
Global

38. More Real World Examples:

39. Eggs: Diffuse Interreflections
Direct
Global

40. Wooden Blocks: Specular Interreflections
Direct
Global

41. Kitchen Sink: Volumetric Scattering
Chandrasekar 50, Ishimaru 78
Direct
Global

42. Peppers: Subsurface Scattering
Direct
Global

43. Hand Direct Global Skin: Hanrahan and Krueger 93,
Uchida 96, Haro 01, Jensen et al. 01,
Cula and Dana 02, Igarashi et al. 05, Weyrich et al. 05
Direct
Global

44. Face: Without and With Makeup
Without Makeup
Global
Direct
With Makeup

45. Blonde Hair Direct Global Hair Scattering: Stamm et al. 77,
Bustard and Smith 91, Lu et al. 00
Marschner et al. 03
Direct
Global