Gray Anchoring: a New Computational Theory for Biological Color Constancy

TL;DR

This paper introduces gray-anchoring, a novel computational theory inspired by human visual processing, to improve color constancy in computer vision by identifying gray surfaces and estimating illumination.

Contribution

The work proposes a new gray-anchoring rule for color constancy, supported by neural implementation analysis and simulation results demonstrating its effectiveness.

Findings

Gray surfaces can be identified within complex scenes using concentric double-opponent cells.

Gray-anchoring enables higher-level cortices to estimate scene illumination accurately.

The proposed theory offers an efficient solution to computational color constancy.

Abstract

It is still challenging for computer vision to imitate human color perception, e.g., color constancy, which is a fundamental perceptual ability in humans to perceive, interpret and interact with their surroundings. Among others, the anchoring theory provides impressive insights for human lightness perception, yet the specific anchoring rules underlying color constancy have remained contentious for decades. In this work, we introduced a novel computational theory - gray-anchoring (GA) theory - to explain how the early stage of visual system contributes to color constancy and demonstrate how our GA rule applies to the chromatic domain by identifying gray surfaces within complex scenes. Furthermore, we also demonstrate the potential neural implementation of gray-anchoring by quantitatively analyzing the computational flows of concentric double-opponent (DO) cells in V1. The simulational results show that the concentric DO cells have the ability to identify gray surfaces within color-biased scenes and these gray surfaces can then be used by the higher-level cortices to easily estimate the illuminant. This finding offers not only a clear functional explanation of the concentric DO receptive fields of this cell type in the visual system but also an effective and efficient solution to computational color constancy for computer vision.