The variational classification model we propose is implemented through a fast and
efficient algorithm. The tests we conducted are at
least as good as the ones obtained from the stochastic model exposed,
but with an average computational time five times faster.
Further work will concern : classification according to texture
features, multispectral data, reconstruction (deblurring), and the non
supervised case i.e. the estimation of the class parameters.