Image Processing Reference
In-Depth Information
updating of weights is continued until the network stabilizes. The method is said
stable
(the
learning stops) when:
where
E
(
t
) is the method fuzziness computed at the
t
th iteration,
γ
is a prefixed very small
positive quantity and . After convergence, the pixels
j
with
o
j
>
θ
are con-
sidered to constitute the skin map of the image; they are set to take value 255, in contrast with
the remaining which will constitute the background (value 0).
Figure 5
shows, with a 3D rep-
resentation, the segmentation process (b) and (c) performed by the MS-RNN method on the
input image depicted in (a).
FIGURE 5
(a) Original images, (b)
L
component, (c)
a
component, and (d) skin map (SM).
4 Skin Map Segmentation
We transformed the image model into CEI
Lab
and normalized the luminance component
L
and the chrominance components
a
in the range of [0, 255]. To realize multiclass image seg-
mentation, the
CRC
must satisfy a homogeneity constraint, i.e., the difference between
c
i
,
j
1
and
c
i
,
j
2
must be less than or equal to a prefixed threshold. In such a case, the region is seen as uni-
form and becomes
RC
otherwise, the
CRC
is split into four newly defined
CRC
, letting
w
be
w
/2. The parameters of the preprocessing subnetwork have been set to the following values:
•
w
0
= 8,
w
t
=
w
t
− 1/2
(
t
denotes iteration)
•
θ
= 50
The output of the preprocessing subnetwork normalized in the [0, 1] range is fed to a clus-
tering subnetwork. The parameters of the clustering subnetwork have been set to the follow-
ing values:
•
η
= 0.2 (learning rate)
•
γ
= 0.001 (convergence rate)
The reason for these choices resides in a most successful skin-detection system, both for fed
tecting skin and suppressing noise, while requiring the minimum amount of computation or,
equivalently, minimum number of iterations to converge.
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