5.1 Representativity and descriptor

robustness to noise

Silouette extraction is still an active reseach ﬁeld. It
is well known that extraction is subjected to noise.
First point was to check our descriptors robustness to
noise. For this, we conducted experiments with two
databases of simulated data for a human avatar with
different morphology and different actions. The ﬁrst
database contains 2925 training data with Gaussian
noise around the contour of the shape and the second
database contains 608 unlearning data. The aims of
this experience is to evaluate the capacity of shape de-
scriptors to encode various shapes with different value
of the standard deviation of Gaussian noise. Con-
sidering x0 = [0,0] the center of the silhouette, let
xi = [ρi,θi] the polar coordinates of a contour point.
The noise ∆σ is applied on ρi. ∆σ (cid:44)→ N (0,std) with
std = {0,1,2,3}. Example of noised silhouettes are
presented on ﬁgure 6.
VISAPP_HumanPoseEstimation