Multimodal motion estimation and segmentation using Markov random fields

Abstract

A multimodal approach to the problem of velocity estimation is presented. It combines the advantages of the feature-based and gradient-based methods by making them cooperate in a single global motion estimator. The theoretical framework is based on global Bayesian decision associated with Markov random field models. The proposed approach addresses, in parallel, the problem of velocity estimation and segmentation. Results on synthetic as well as on real-world image sequences are presented. Accurate motion measurement and detection of motion discontinuities with a surprisingly good quality have been obtained.< >