Simultaneous motion-disparity estimation and segmentation from stereo

Both motion/structure estimation from monocular video and disparity estimation from still-frame stereo are known to be ill-posed problems. Further, because disparity varies by depth, and motion parameters are different for independently moving objects, they can both benefit from scene segmentation. To this effect, we present a framework for simultaneous motion and disparity estimation including scene segmentation in stereo video. In this formulation, pairs of disparity and motion parameter vector values are segmented into K regions, where within each region a single set of motion parameters is defined, and the disparity field is allowed to vary smoothly. The algorithm iterates between computing the maximum a posteriori probability (MAP) estimates of the disparity and segmentation fields conditioned on the present motion parameter estimates, and the maximum likelihood (hit) estimates of the motion parameters via simulated annealing (SA). Simulation results are provided