To obtain a sample of each region large enough to statistically model its behaviour, initial seeds have to be placed completely inside the regions. Boundary information allows us to extract these positions in the ``core'' of regions by looking for places far away from contours.
Boundaries between colour texture regions, which are combination
of colour edges and texture edges can be considered as perceptual
edges, because a human has the ability to detect both ones. The
problem of texture edge detection is considered as a classical
edge detection scheme in the multidimensional set of texture
features which are used to represent the region
characteristics [18]. Meanwhile, the extraction of
colour boundaries implies a major difficulty since the use of an
edge detector over a colour image produces the apparition of
microedges inside a textured region. Our approach, is based on the
perception of textures as homogeneous colour regions when they are
seen from a long distance [16]. A smoothing process is
progressively performed starting from the original image until
textures looks homogeneous, as we would look the texture from far
away. Then, the application of an edge detector allows to obtain
the colour edges. Figure 2 shows the effect of
smoothing a textured image; regions which were originally textured
are appreciated as homogeneous colour regions.
The union of texture and colour edges provides the perceptual edges of the image. Nevertheless, due to the inherently non-local property of texture and the smoothing process performed, the result of this method are inaccurate and thick contours (see Figure 2.c). However, this information is enough to perform the seed placement in the ``core'' of regions, which allows to model the characteristics of regions.
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