Fitting multiple projective models using clustering-based Markov chain Monte Carlo inference

An algorithm for fitting multiple models that characterize the projective relationships between point-matches in pairs of (or single) images is proposed herein. Specifically, the problem of estimating multiple algebraic varieties that relate the projections of 3 dimensional (3D) points in one or more views is predominantly turned into a problem of inference over a Markov random field (MRF) using labels that include outliers and a set of candidate models estimated from subsets of the point matches. Thus, not only the MRF can trivially incorporate the errors of fit in singleton factors, but the sheer benefit of this approach is the ability to consider the interactions between data points.