Maximum Likelihood Mosaics

TL;DR

This paper introduces a maximum likelihood approach to panoramic image reconstruction that optimally aligns multiple partial views, reducing errors common in pairwise registration methods, and computes the best overall panorama.

Contribution

It develops an efficient algorithm to compute the maximum likelihood estimate of all image alignments and the panorama, improving over traditional pairwise registration methods.

Findings

Provides a maximum likelihood framework for panorama construction

Reduces propagation errors in image registration

Offers an efficient algorithm for optimal panorama estimation

Abstract

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.