of (Fang and Chan, 2006), the use of invariant fea-
tures allows to avoid the registration step at the learn-
ing stage and the computation of PCA on the training
data in order the estimated the appropriate number of
clusters in a low dimensional feature subspace. The
final detection results are given by Fig.11.
Figure 11: Detection of partially occluded human shapes in
video images. First row shows the detection results obtained
by the traditional edge-based active contours and the Sec-
ond row shows the result obtained by our proposed method.
6 CONCLUSIONS
New method of geometric active contours with shape
prior is presented in this research. This approach uses
the registration by phase correlation and a set of in-
variant descriptors to define prior knowledge. Exper-
iments have shown the ability of the new added term
to improve the robustness of the detection process in
presence of missing parts and partial occlusions of the
target objects. The addition of shape prior has not
increased significantly the execution time given that
the proposed approach does the registration only once
and it is done by the Fast Fourier Transform unlike
(Foulonneau et al., 2006; Charmi et al., 2008) where
at each iteration shape descriptors are calculated for a
given order which has to be set empirically. In fact, a
small order gives unsatisfactory results and a big one
increase significantly the execution time. As future
perspectives, we are working on applying our model
in the context of medical application where the shape
of reference is given by medical atlas in order to aid in
the diagnosis. Also, we plan to extend this approach
to more general transformations such as affine trans-
formations.
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