Tesis y Tesistas 2020 - Postgrado - Fac. de Informática - UNLP

english
Esp. Cesar Armando Estrebou
with the conversion formulae to transform the different
colour spaces from the colour pattern RGB (standard format
for image representation). The second point is the survey
of segmentation algorithms applied to the different colour
patterns and the efficacy results presented by the authors
of the works studied.
Last but not least, the third point to highlight is the survey
of skin dataset that appear in the study articles as well as
in the articles chosen by the author of this document. This
originated three skin dataset that contain features that
enable to make a precise comparison of the results obtained
from the different skin segmentation algorithms. This is
very important to make an exact evaluation of the colour
patterns efficacy, a difficult task to carry out with the results
obtained from the revised articles. Another contribution
of this work is the analysis of the skin distribution in the
colour representation systems. Hence, as a conclusion we
can say that, in some systems, the skin pixel distribution is
narrower and more compact. For example, the models HSV
and YcbCr present one of the most compressed distributions
and it is parallel to the axis of the representation space.
There is not a convincing answer that indicates the most
appropriate colour pattern for skin segmentation. After a
survey with a lot of publications and analysing the results
presented by the authors, it seems that there is not a colour
pattern outstanding from the rest. In the articles, the YCbCr
colour pattern is mostly used individually and the HSV,
YCbCr and RGB patterns are mostly used combined, but in
general the authors do not justify the option of one model
over the others.
Although it is true that the different colour spaces have
some advantages such as the colour wheel, being more
compact, more uniform and dividing brightness better,
these are not vital features to verify if a pixel in an image
is human skin or not.
Apparently, a successful classification is due to the capacity
of the classification algorithms used rather than the colour
pattern chosen.
In general, some authors have been successful with
different combinations of colour representation patterns
and segmentation algorithms. However, there seems to be
a tendency that indicates that the results of the articles
are better when combinations of components of two or
more patterns of colour representation are used. In order
to see how the colour pattern choice influences the skin
classification algorithm, it is believed that a colour system
that fits better the skin colour distribution mitigates the
limitation of those algorithms without a robust adaptation
or covering the RGB colour space (the widest of all). As an
example, on the one hand, we can mention those algorithms
based on thresholds, less robust than others, and where
their application is less effective in the RGB colour pattern
segmentation than in the rest of the patterns. On the other
hand, from the experiment carried out by this author on RCE
neuronal networks (more robust than the threshold-based
patterns) shows that there are no relevant differences on
skin segmentation when using this type of networks in
different colour patterns.
Future Research Lines
There is still too much to do to get a definite answer to the
question: what is the best colour pattern to represent human
skin? The first task is the implementation of segmentation
algorithms with each of the variations presented in the
study papers. The second task is the application of each
algorithm to every colour representation pattern in order
to determine the success of the segmentation. Here, it
is necessary to highlight the importance of the surveyed
information that claim the availability of segmented skin
masks (ground-truth) to make a precise comparison and get
reproducible results.
Another interesting point is to estimate the computational
cost for the transformations between colour representation
patterns and the segmentation algorithm used, as well as
the final cost for the combination of both.
This will enable the association to the segmentation
efficacy, an important aspect that in general the authors
do not take into account or, at least, they do not mention
in their writings.
Once all the above-mentioned tasks are performed, it is
possible to make a wider, exhaustive and exact analysis
to verify, on the one hand, the most convenient colour
representation pattern for skin segmentation applications
and, on the other, how these algorithms influence the
segmentation algorithms.
129 TESIS Y TESISTAS 2020