Colour in Context
Research group Computer Vision Center |
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The
segmentation method proposed in this paper is based on the observation
that a single physical re°reflectance can have many different image
values.
We call the set of all these values a dominant colour. These
variations are caused by shadows, shading and highlights and due to
varying object geometry. There is a family of segmentation
methods
focused in the detection of these shapes: the physics-based
segmentation methods, of which the dichromatic reflection model,
introduced in 1985, has been the most relevant. Nonetheless, this
model takes to many assumptions, and as a consequence, is not able to
find this structures in a theoretical framework, but lacks of
adaptability in real images. In our approach, we take the idea that
this structures form well-connected ridges in the chromatic
histogram.
To capture them, we
propose a new Ridge based Distribution Analysis (RAD) to Find the set
of ridges representative of the dominant colour. To find them we apply,
first, a multilocal
creaseness technique, (MLSEC-ST), which has as a main
advantatge that
is not affectec by little irregularities and gaps in the ridges.
Afterwards, we apply a simple ridge extraction algorithm. Finally, a
flooding procedure is performed to find
the dominant colours in the histogram.
Qualitative results illustrate
the ability of our method to obtain excellent results in the presence
of shadow and highlight edges. Quantitative results obtained on the
Berkeley data set show that our method outperforms state-of-the-art
segmentation methods at low computational cost.
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