Motion from Images: Image Matching, Parameter Estimation and Intrinsic Stability

This paper first p m t s an image matching algorithm that uses multiple anributes associated with a pixel to yield a generally overdetermined system of constraints, taking into account possible structural discontinuities and occlusions. Both topdown and bottom-up data flows are used in multi-resolution computational structure. The matching algorithm computes dense displacement fields and the associated occlusion maps. The motion and structure parameters are estimated through optimal estimation (e.g., maximal liielihood) using the solution of a linear algorithm as an initial guess. To investigate the intrinsic stability of the problem in the presence of noise, a theoretical lower bound on e m r variance of the estimates, Cramtr-Rao bound, is determined for motion parameters. Experiments showed that the performance of our algorithm has essentially reached the bound. In addition, the bounds show that, intrinsically, motion estimation from two perspective views is a fairly stable problem if the image disparities are rclatively large, but is unstable if the disparities are very small (as required by optical flow approaches).