Image Processing Reference
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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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