Fitting multiple projective models using clustering-based Markov chain Monte Carlo inference ?
Assignment of projective models becomes a problem of probabilistic inference through clustering. A Markov network formulation that models data points in terms of projective relationships in two views is introduced. An algorithm that fits multiple varieties to data points is specified using MCMC based inference. Use of a global energy measure to capture the quality of convergence. Comparative results indicate less susceptibility to parameter tuning and increased accuracy of convergence.
clustering markov chain monte carlo multiple model fitting two-view geometry markov random field