  A biologically plausible computational model for color representation is
introduced. We present a mechanistic hierarchical model of neurons that not
only successfully encodes local hue, but also explicitly reveals how the
contributions of each visual cortical layer participating in the process can
lead to a hue representation. Our proposed model benefits from studies on the
visual cortex and builds a network of single-opponent and hue-selective
neurons. Local hue encoding is achieved through gradually increasing
nonlinearity in terms of cone inputs to single-opponent cells. We demonstrate
that our model's single-opponent neurons have wide tuning curves, while the
hue-selective neurons in our model V4 layer exhibit narrower tunings,
resembling those in V4 of the primate visual system. Our simulation experiments
suggest that neurons in V4 or later layers have the capacity of encoding unique
hues. Moreover, with a few examples, we present the possibility of spanning the
infinite space of physical hues by combining the hue-selective neurons in our
model.

Computer Vision and Pattern Recognition