Color-opponent mechanisms for local hue encoding in a hierarchical framework
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Various aspects of color have been extensively studied in science and the arts for the simple reason that it conveys information and attracts attention. In perception, color plays an important role. It helps in tasks such as object recognition. In humans, color perception starts with cones in the retina. Studies in the primary visual cortex show opponent mechanisms for color representation. While single-opponent cells encode local hue, double-opponent neurons are sensitive to color boundaries. This paper introduces a biologically plausible computational model for color representation. We present a hierarchical model of neurons that successfully encodes local hue. Our proposed model benefits from studies on the visual cortex and builds a network of single-opponent and hue-selective neurons. We show that our model hue-selective neurons, at the top layer of our network, can achieve the goal of local hue encoding by combining inputs from single-opponent cells, and that they resemble hue-selective neurons in V4 of the primate visual system. Moreover, with a few examples, we present the possibility of spanning the infinite space of physical hues from the hue-selective neurons in our model.
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