Perception of surface slant and edge labels from optical flow: a computational approach.

I present here two results from investigations of a computational theory of how the human visual system may obtain information about the physical environment directly from optically sensed velocity fields (optical flow). Previous work has shown how optical flow arises when an observer moves through the static environment. The inverse problem is investigated here: how to recover intrinsic characteristics of the environment from optical flow patterns. The first result is a method for computing the local slant of surfaces relative to the moving observer. The second result is a method for detecting and discriminating among the five types of edges that form the boundaries of surfaces. The results have been formalised as mathematical equations, a physiological model, and a computer program. Properties and predictions resulting from these formal models are discussed with regard to experimental findings in psychophysics.