Abstract
We address here the resolution of the so-called
inverse problem for the iterated functions system
(IFS). This problem has already been widely considered,
and some studies have been performed for the affine
IFS, using deterministic or stochastic methods
(simulated annealing or genetic algorithm). In dealing
with the nonaffine IFS, the usual techniques do not
perform well unless some a priori hypotheses on the
structure of the IFS (number and type of functions) are
made. In this work, a genetic programming method is
investigated to solve the ``general'' inverse problem,
which allows the simultaneous performance of a numeric
and a symbolic optimization. The use of a ``mixed IFS''
may enlarge the scope of some applications, for
example, image compression, because it allows a wider
range of shapes to be coded.
Users
Please
log in to take part in the discussion (add own reviews or comments).