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[ abstract | experimental
results | paper ]
Abstract:
Many algorithms in computer vision assume diffuse only reflections and
deem specular reflections to be outliers. However, in the real world,
the presence of specular reflections is inevitable, since there are
many dielectric inhomogeneous objects which have both diffuse and
specular reflections. To resolve this problem, we present a method to
separate the two reflection components. The
method is principally based on the distribution of specular and diffuse
points in a two-dimensional maximum chromaticity-intensity
space. We found that, by utilizing the space and known illumination
color, the problem of reflection component separation can be simplified
into the problem of identifying diffuse maximum
chromaticity. To be able to identify the diffuse maximum
chromaticity correctly, an analysis of the noise is required, since
most real images suffer from it. Unlike existing methods, the proposed
method can separate the reflection components robustly for any kind of
surface roughness and light direction.
Experimental
Results:
[Evaluation | Real Images
]
"Do not use the images in this website for testing your
code.
The images are compressed images whose brightness might
not be linear to the flux of incoming light.”
I.
Evaluation:
1. Head
Model
  
(a) input image
(b) diffuse component (polarizing filters) (c) estimated diffuse component
Comparison
of (a) and (b):
  
Comparison
of (b) and (c):
  
(d)
error in R-channel
(e) error in G-channel
(f) error in B-channel
2.
  
(a)
input image (b)
diffuse component (polarizing filters) (c)
estimated diffuse component
Comparison
of (a) and (b):
  
(d)
difference in R-channel
(e) difference in
G-channel (f)
difference in B-channel
Comparison
of (b) and (c):
  
(g)
difference in R-channel
(h) difference in
G-channel
(i) difference in B-channel
III.
Real Images:
1. Head Model (a single illumination)
2. Head model lit with multiple light sources:
3. A green plastic object with rough surface:
4. Green sandal (high specularity):
5. Toy (low specularity)
[ abstract | experimental
results | paper ]
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