  The majority of the approaches to the automatic recovery of a panoramic image
from a set of partial views are suboptimal in the sense that the input images
are aligned, or registered, pair by pair, e.g., consecutive frames of a video
clip. These approaches lead to propagation errors that may be very severe,
particularly when dealing with videos that show the same region at disjoint
time intervals. Although some authors have proposed a post-processing step to
reduce the registration errors in these situations, there have not been
attempts to compute the optimal solution, i.e., the registrations leading to
the panorama that best matches the entire set of partial views}. This is our
goal. In this paper, we use a generative model for the partial views of the
panorama and develop an algorithm to compute in an efficient way the Maximum
Likelihood estimate of all the unknowns involved: the parameters describing the
alignment of all the images and the panorama itself.
