  We present a parametric deformable model which recovers image components with
a complexity independent from the resolution of input images. The proposed
model also automatically changes its topology and remains fully compatible with
the general framework of deformable models. More precisely, the image space is
equipped with a metric that expands salient image details according to their
strength and their curvature. During the whole evolution of the model, the
sampling of the contour is kept regular with respect to this metric. By this
way, the vertex density is reduced along most parts of the curve while a high
quality of shape representation is preserved. The complexity of the deformable
model is thus improved and is no longer influenced by feature-preserving
changes in the resolution of input images. Building the metric requires a prior
estimation of contour curvature. It is obtained using a robust estimator which
investigates the local variations in the orientation of image gradient.
Experimental results on both computer generated and biomedical images are
presented to illustrate the advantages of our approach.
