Model Of Human Color Vision Research Articles

Visualizing Visual Adaptation.

Many techniques have been developed to visualize how an image would appear to an individual with a different visual sensitivity: e.g., because of optical or age differences, or a color deficiency or disease. This protocol describes a technique for incorporating sensory adaptation into the simulations. The protocol is illustrated with the example of color vision, but is generally applicable to any form of visual adaptation. The protocol uses a simple model of human color vision based on standard and plausible assumptions about the retinal and cortical mechanisms encoding color and how these adjust their sensitivity to both the average color and range of color in the prevailing stimulus. The gains of the mechanisms are adapted so that their mean response under one context is equated for a different context. The simulations help reveal the theoretical limits of adaptation and generate "adapted images" that are optimally matched to a specific environment or observer. They also provide a common metric for exploring the effects of adaptation within different observers or different environments. Characterizing visual perception and performance with these images provides a novel tool for studying the functions and consequences of long-term adaptation in vision or other sensory systems.

Retinex at 50: color theory and spatial algorithms, a review

Retinex Imaging shares two distinct elements: first, a model of human color vision; second, a spatial-imaging algorithm for making better reproductions. Edwin Land’s 1964 Retinex Color Theory began as a model of human color vision of real complex scenes. He designed many experiments, such as Color Mondrians, to understand why retinal cone quanta catch fails to predict color constancy. Land’s Retinex model used three spatial channels (L, M, S) that calculated three independent sets of monochromatic lightnesses. Land and McCann’s lightness model used spatial comparisons followed by spatial integration across the scene. The parameters of their model were derived from extensive observer data. This work was the beginning of the second Retinex element, namely, using models of spatial vision to guide image reproduction algorithms. Today, there are many different Retinex algorithms. This special section, “Retinex at 50,” describes a wide variety of them, along with their different goals, and ground truths used to measure their success. This paper reviews (and provides links to) the original Retinex experiments and image-processing implementations. Observer matches (measuring appearances) have extended our understanding of how human spatial vision works. This paper describes a collection very challenging datasets, accumulated by Land and McCann, for testing algorithms that predict appearance.

Prediction of Visibility for Color Scheme with a Model of Human Color Vision on the Web Browser

Prediction of Visibility for Color Scheme with a Model of Human Color Vision on the Web Browser

Advances in Color Science: From Retina to Behavior

Color has become a premier model system for understanding how information is processed by neural circuits, and for investigating the relationships among genes, neural circuits, and perception. Both the physical stimulus for color and the perceptual output experienced as color are quite well characterized, but the neural mechanisms that underlie the transformation from stimulus to perception are incompletely understood. The past several years have seen important scientific and technical advances that are changing our understanding of these mechanisms. Here, and in the accompanying minisymposium, we review the latest findings and hypotheses regarding color computations in the retina, primary visual cortex, and higher-order visual areas, focusing on non-human primates, a model of human color vision.

Relative absorption model of color vision

AbstractOpponent processing is widely accepted as providing a general framework for the standard model of human color vision. After the cones' responses are transmitted to second stage neurons, however, there is no consensus on exactly how synaptic connections are organized. The Relative Absorption Model introduced here is an explicit neural network that generates neural correlates of color vision. The model makes detailed predictions of known color and neural phenomena, including familiar aspects of color perception. Until now these phenomena have not had an explicit neural explanation. The model's simplicity shows that color does not require complex processing of spectral information. The network receives excitatory and inhibitory input from three classes of spatially proximate photoreceptors with different spectral sensitivities. Four second stage neurons provide symmetric input to four third stage neurons, whose outputs are correlates of red, green, blue, and yellow. These color cells identify which receptor type has the greatest absorption of photons and which has the least. Their response intensities correspond to the differences between those absorptions and the middle absorption. A single second stage neuron computes violet and purple information that is then transmitted through the red and blue channels, the only channels in the network capable of conveying the information. Five additional neurons produce correlates of black and white. The white cell's response intensity measures the smallest of the three absorptions, and the black response measures how far the largest absorption is from full saturation. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 252–264, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20121