  Selfridge, along with Sutherland and Marr provided some of the earliest
proposals for how to program computers to recognize shapes. Their emphasis on
filtering for contour features, especially the orientation of boundary
segments, was reinforced by the Nobel Prize winning work of Hubel & Wiesel who
discovered that neurons in primary visual cortex selectively respond as a
function of contour orientation. Countless investigators and theorists have
continued to build on this approach. These models are often described as
neuromorphic, which implies that the computational methods are based on
biologically plausible principles. Recent work from the present lab has
challenged the emphasis on orientation selectivity and the use of neural
network principles. The goal of the present report is not to relitigate those
issues, but to provide an alternative concept for encoding of shape information
that may be useful to neuromorphic modelers.
