Color Constancy Using Double-Opponency.

IEEE Trans Pattern Anal Mach Intell

Published: October 2015

AI Article Synopsis

  • The study focuses on the role of double-opponent (DO) cells in the primary visual cortex (V1) and proposes a new model for color constancy based on their properties.
  • The double-opponency based color constancy (DOCC) model estimates the color of light sources by analyzing the responses of DO cells to color-biased images, using a pooling mechanism.
  • Evaluations on various datasets indicate that the DOCC model achieves competitive results compared to current methods, while being simpler to implement and not needing extensive fine-tuning for different datasets.

Article Abstract

The double-opponent (DO) color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. In this work we propose a new color constancy model by imitating the functional properties of the HVS from the single-opponent (SO) cells in the retina to the DO cells in V1 and the possible neurons in the higher visual cortexes. The idea behind the proposed double-opponency based color constancy (DOCC) model originates from the substantial observation that the color distribution of the responses of DO cells to the color-biased images coincides well with the vector denoting the light source color. Then the illuminant color is easily estimated by pooling the responses of DO cells in separate channels in LMS space with the pooling mechanism of sum or max. Extensive evaluations on three commonly used datasets, including the test with the dataset dependent optimal parameters, as well as the intra- and inter-dataset cross validation, show that our physiologically inspired DOCC model can produce quite competitive results in comparison to the state-of-the-art approaches, but with a relative simple implementation and without requiring fine-tuning of the method for each different dataset.

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Source
http://dx.doi.org/10.1109/TPAMI.2015.2396053DOI Listing

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