# Active Contours without Edges

### Abstract

In this talk, I will present a new model for active contours to detect
objects in a given image. The model is based on techniques of curve
evolution,
Mumford-Shah functional for segmentation, and the level set method of S.
Osher and J. Sethian. The model can detect objects whose
boundaries are not necessarily defined by gradient. We minimize an
energy which
can be seen as a particular case of the so-called minimal partition
problem. In
the level set formulation, the problem becomes a ``mean-curvature
flow''-like
evolving the active contour, which will stop on the desired boundary.
However,
the stopping term does not depend on the gradient of the image, as in
the
classical active contour models, but it is instead related to a
particular
segmentation of the image. Finally, I will present various experimental
results
and in particular some examples for which the classical snakes methods
based
on the gradient are not applicable. We will also see that interior
contours are
automatically detected.

Maintained by
Alexandre R.J. FRANÇOIS