Computational Networks in Early Vision: From orientation selection to optical flow

Orientation selection is the process of extracting the tangents to piecewise smooth curves from a two-dimensional image. The analysis of orientation selection begins by resolving the question of representation with reference to geometric, biological and computational constraints. The structure of a relaxation network is then derived from a discretization of the differential geometry of curves in the plane, and considerations about endstopped neurons suggest a robust method for estimating curvature. Experimental results from a simulation are presented. In addition to its uses in computational vision, the relaxation network can be interpreted as a rough model of some of the interactive circuitry underlying orientation selection in the early visual cortex at about the resolution of receptive fields.