  Muon tomographic visualization techniques try to reconstruct a 3D image as
close as possible to the real localization of the objects being probed.
Statistical algorithms under test for the reconstruction of muon tomographic
images in the Muon Portal Project are here discussed. Autocorrelation analysis
and clustering algorithms have been employed within the context of methods
based on the Point Of Closest Approach (POCA) reconstruction tool. An iterative
method based on the log-likelihood approach was also implemented. Relative
merits of all such methods are discussed, with reference to full Geant4
simulations of different scenarios, incorporating medium and high-Z objects
inside a container.
