  The skeleton is an essential shape characteristic providing a compact
representation of the studied shape. Its computation on the image grid raises
many issues. Due to the effects of discretization, the required properties of
the skeleton - thinness, homotopy to the shape, reversibility, connectivity -
may become incompatible. However, as regards practical use, the choice of a
specific skeletonization algorithm depends on the application. This allows to
classify the desired properties by order of importance, and tend towards the
most critical ones. Our goal is to make a skeleton dedicated to shape matching
for recognition. So, the discrete skeleton has to be thin - so that it can be
represented by a graph -, robust to noise, reversible - so that the initial
shape can be fully reconstructed - and homotopic to the shape. We propose a
linear-time skeletonization algorithm based on the squared Euclidean distance
map from which we extract the maximal balls and ridges. After a thinning and
pruning process, we obtain the skeleton. The proposed method is finally
compared to fairly recent methods.
