Progress on a computational model of achromatic color processing

Abstract

This paper reports further progress on a computational model of human achromatic color perception first presented at Human Vision and Electronic Imaging VI. The model predicts the achromatic colors of regions within 2D images comprising arbitrary geometric arrangements of luminance patches separated by sharp borders (i.e., Land Mondrian patterns). The achromatic colors of regions of homogeneous luminance are computed from the log luminance ratios at borders. Separate lightness and darkness induction signals are generated at the locations of borders in a cortical representation of the image and spread directionally over several degrees of visual angle. The color assigned to each point in the image is a weighted sum of all of the lightness and darkness signals converging on that point. The spatial convergence of induction signals can be modeled as a diffusive color filling-in process and realized in a neural network. The model has previously been used to predict lightness matches in psychophysical experiments conducted with stimuli consisting of disks and surrounding rings. Here a formal connection is made between the model equations used to predict lightness matches in these experiments and Stevens' power law model of the relationship between brightness and physical intensity. A neural mechanism involving lateral interactions between neurons that detect borders and generate spreading achromatic color induction signals is proposed to account for observed changes in the parameters of the model, including the brightness law exponent, with changes in surround size.