Nombre: THALES DE OLIVEIRA GONÇALVES

Fecha de publicación: 21/07/2017
Supervisor:

Nombre	Papel
PATRICK MARQUES CIARELLI	Advisor *

Junta de examinadores:

Nombre	Papel
FABIAN TADEU DO AMARAL	External Examiner *
KARIN SATIE KOMATI	External Examiner *
PATRICK MARQUES CIARELLI	Advisor *

Sumario: Somatotype is a metric that tells us about human body shape and composition. It is
important in many applications, especially in the physical education and health areas.
However, obtaining the somatotype nowadays, besides being a very time-consuming
procedure, demands several anthropometric devices, some of which are not very portable,
and an expert of the area to take various measurements directly on the person’s body. The
proposal of this work is to obtain somatotype of bodybuilders by their body images in
different positions, based on image processing and machine learning techniques. Due to
the difficulty of references of other works with similar proposals, a database needed to be
builded by our own for the development of the proposed system. A set of measurements
that are possible to be extracted from the individual’s images are proposed and a feature
selection chooses a very small subset of relevant measurements to estimate the somatotype.
With the assist of a segmentation technique and morphological image processings, the
individual is segmented and it is proposed an algorithm to extract each of the selected
relevant measurements. Finally, the body measurements taken from the individual’s images
are mapped on their somatotypes based on regression techniques. The results obtained
shows that the somatotype of bodybuilders can be estimated reasonably based only on
their images, which is a less expensive option to obtain this metric.

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