Constraint-Based Inference from Image Motion

We deal with the inference of environmental information (position and velocity) from a sequence of images formed during relative motion of an observer and the environment. A simple method is used to transform relations between environmental points into equations expressed in terms of constants determined from the images and unknown depth values. This is used to develop equations for environmental inference from several cases of rigid body motion, some having direct solutions. Also considered are the problems of non-unique solutions and the necessity of decomposing the inferred motion into natural components. Inference from optic flow is based upon the analysis of the relative motions of points in images formed over time. Here we deal with environmental inferences from optic flow for several cases of rigid body motion and consider extentions to linked systems of rigid bodies. Since locality of processing is very important, we attempt to determine the smallest number of points necessary to infer environmental structure for different types of motion.