The dissertation uses the facet model technique to compute the optic flow field directly from a time sequence of image frames. Two techniques, an iterative and a non-iterative one, determine 3D motion parameters and surface structure (relative depth) from the computed optic flow field. Finally we discuss a technique for the image segmentation based on the multi-object motion using both optic flow and its time derivative.

The facet model technique computes optic flow locally by solving over-constrained linear equations obtained from a fit over 3D (row, column, and time) neighborhoods in an image sequence. The iterative technique computes motion parameters and surface structure using each to update the other. This technique essentially uses the least square error method on the relationship between optic flow field and rigid body motion. The non-iterative technique computes motion parameters by solving a linear system derived from the relationship between optic flow field and rigid body motion and then computes the relative depth of each pixel using the motion parameters computed. The technique also estimates errors of both the computed motion parameters and the relative depth when the optic flow is perturbed.