MAP-based probabilistic diffusion method for correspondence and line field estimation

A new maximum a posteriori (MAP)-based probabilistic diffusion method is proposed for the estimation of dense correspondence and line fields. The proposed algorithm reflects the joint probabilistic distributions of the neighbourhood in the Markov random field, and implements them by using a plane configuration model. The probabilistic diffusion scheme showed fast convergence and improved object boundaries in the estimated fields.