Biomedical Engineering Reference
In-Depth Information
p
←
Z
(
i
,
j
)
−
Z
(
i
,
j
−
1)
q
←
Z
(
i
,
j
)
−
Z
(
i
−
1
,
j
)
end do
end do
Normalize
(
Z
(
x
,
y
)
,
Z
max
,
Z
min
)
Out put Z
(
x
,
y
)
The subfunction
Normalize
is a standard math function used in signal and
image processing.
We now demonstrate this method by using the following example.
Example 6.
Reconstruct the surface of a synthetic vase using the Zheng-
Chellappa method.
The experiments are based on the synthetic images that are generated us-
ing true depth maps. Figure 5.4(a) shows the same synthetic vase as in the
previous section and the reconstruction results using Pentland's algorithm.
The light is from above at (x
=
0
,
y
=
0
,
z
=
1
). The input image is showed
in Fig. 5.4(a). The surface, shown in Figs. 5.4(b), (c), and (d), is the re-
constructed depth map from three different directions. Zheng-Chellappa al-
gorithm produces reasonable results as expected for the vase. However, some
errors can be seen around the boundary of the vase. In general, the experi-
ment shows that Zheng-Chellappa's algorithm can reasonably reconstruct the
object on the surface. The most important advantage of Zheng-Chellappa's
algorithm is that the optimization approach is not limited to the situation
where the reflectance map changes linearly with respect to the surface shape.
Example 7.
Reconstruct the surface of a synthetic Mozart using Zheng-
Chellappa's method.
The experiments are also based on the synthetic images that are generated
using true depth maps. Figure 5.5(a) shows the synthetic Mozart and the
reconstruction results using Zheng-Chellappa's algorithm. The light is from
above at (x
=
0
,
y
=
0
,
z
=
1
). The input image is showed in Fig. 5.5 (a). The
result image, shown in Figs. 5.5(b), (c), and (d), is the reconstructed depth
map from three different directions. The recovered surface is well outlined as
expected for the human's head. However, the details of Mozart cannot be accu-
rately recovered using their approach. In our opinion, this is due to the rapid
changes and complexity of the input image. Although the results can be im-
proved by prefiltering and smoothing the input image, in general, we conclude
