
This modules selects contrasted level lines from a Fimage.
It brings new features to the former \verb+ll_boundaries+ module.
(these two modules will probably be merged in a future release).
\index{meaningful!boundaries}
\index{edge detection}

\medskip

Because of gradient quantization effect, it may be very useful to slightly 
smooth the image before applying the method. A gaussian with standard deviation
0.5 is the default kernel. 

\medskip
In the original algorithm, the gradient distribution is computed on the whole 
image (see \cite{desolneux.moisan.ea:edge}), sometimes yielding what is called the ``blue sky effect'':
there are too many detections in some regions because the image contains a very
flat part. This can be counter-balanced by using the following method (detailed
in \cite{cao.muse.sur:shape}): since meaningful boundaries are closed curves, they sever the plan
into two connected components. It is then possible to re-estimate the gradient
distribution in each connected component and apply the same method as
above. Use the \verb+-L+ flag for this local research. 


\medskip
Notice that contrary to the \verb+ll_edges+ detection, this 
module is constrained to keep full (ie closed) level lines and 
cannot break them into parts. As a consequence, the detected boundaries still
enjoy the original tree structure and are the level lines of a function. 
Meaningful boundaries thus allow to define a ``connected operator'' (following
the mathematical morphologists terminology). The reconstructed image can be
obtained with the \verb+-o+ option, while the corresponding tree can be
saved with the \verb+-k+ option. 


\medskip

The result is a collection of curves stored in a Flists structure.

