Translating Optical Flow Into Token Matches And Depth From Looming

Motion in the Environment Manifests Itself in Changes of Many Kinds, Not Just Image Plane Velocities, Yet These Are All That an Optical Flow Field Makes Explicit. We View Optical Flow as a Useful Low-Level Representation From Which a Symbolic Description of Change, in the Form of Token Matches, Can Be Computed. The Tokens of Interest to Us Are Those Produced by Perceptual Organization Processes, and Are More Abstract Than Edges or Interest Points. We Demonstrate a Working System for Matching Line Tokens Which Uses the Optical Flow Field in a Heuristic Manner to Limit the Search for the `Minimal Bipartite Cover'' of the Set of Tokens From Each Frame. As an Example Application, We Demonstrate a Technique for Computing Distance to Environmental Surfaces Suitable for Obstacle Recognition by a Mobile Robot. Accurate Knowledge of the Camera Motion Parameters Is Not Required. We Describe How Motion in Depth Manifests Itself in the Projected Lengths and Areas of Environmental Surfaces Whose Extent in Depth Is Small Relative to Their Distance From the Camera. Results on Two Sequences Taken by a Mobile Robot Are Presented to Demonstrate the Accuracy of the Method.