We propose a contour operator, called CORF, inspired by
the properties of simple cells in visual cortex. It combines, by a weighted
geometric mean, the blurred responses of difference-of-Gaussian operators
that model cells in the lateral geniculate nucleus (LGN). An operator
that has gained particular popularity as a computational model of a simple
cell is based on a family of Gabor Functions (GFs). However, the GF
operator short-cuts the LGN, and its effectiveness in contour detection
tasks, which is assumed to be the primary biological role of simple cells,
has never been compared with the effectiveness of alternative operators.
We compare the performances of the CORF and the GF operators using
the RuG and the Berkeley data sets of natural scenes with associated
ground truths. The proposed CORF operator outperforms the GF operator
(RuG: t(39)=4.39, p<10−4 and Berkeley: t(499)=4.95, p<10−6).peer-reviewe
