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[ PAMI Paper ]
textured surfaces | specular-free images
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.
Experimental Results:
"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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