Use of colour information in Bayesian estimation of 2-D motion

An extension of the Bayesian estimation of 2-D motion in spatiotemporally sampled image sequences is presented by incorporating the color cue into the estimation process. Instead of scalar image intensity, three-component vector representation of color images is used, thus allowing Y-C1-C2, RGB, or other formats. The maximum a posteriori probability estimation is shown to result in a three-term energy minimizationj. A white Gaussian noise model is used for the displaced pel differences of each image component, and a coupled vector-binary Markov random field model is used for displacement and discontinuity fields. The resulting criterion is optimized using the method of discrete state-space simulated annealing. Improvements in the quality of estimated displacement fields due to additional color information are demonstrated through several experimental results