Belief decision support and reject for textured images characterization
  The textured images' classification assumes to consider the images in terms
of area with the same texture. In uncertain environment, it could be better to
take an imprecise decision or to reject the area corresponding to an unlearning
class. Moreover, on the areas that are the classification units, we can have
more than one texture. These considerations allows us to develop a belief
decision model permitting to reject an area as unlearning and to decide on
unions and intersections of learning classes. The proposed approach finds all
its justification in an application of seabed characterization from sonar
images, which contributes to an illustration.
