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Biomedical subjects

D H Brainard

Publications and source records attributed to D H Brainard.

24 records · Page 2Linked to original sources
PubMed1993

Efficiency in detection of isoluminant and isochromatic interference fringes.

We examined the limitations imposed by neural factors on spatial contrast sensitivity for both isochromatic and isoluminant gratings. We used two strategies to isolate these neural factors. First, we eliminated the effect of blurring by the dioptrics of the eye by using interference fringes. Second, we corrected our data for additional sensitivity losses up to and including the site of photon absorption by applying an ideal-observer analysis described by Geisler [J. Opt. Soc. Am. A 1, 775 (1984)]. Our measurements indicate that the neural visual system modifies the shape of the contrast-sensitivity functions for both isochromatic and isoluminant stimuli at high spatial frequencies. If we assume that the high-spatial-frequency performance of the neural visual system is determined by a low-pass spatial filter followed by additive noise, then the visual system has a spatial bandwidth 1.8 times lower for isoluminant red-green than for isochromatic stimuli. On the other hand, we find no difference in bandwidth or sensitivity of the neural visual system for isoluminant red-green and S-cone-isolated stimuli.

Color Perception Tests↗
PubMed1992

Asymmetric color matching: how color appearance depends on the illuminant.

We report the results of matching experiments designed to study the color appearance of objects rendered under different simulated illuminants on a CRT monitor. Subjects set asymmetric color matches between a standard object and a test object that were rendered under illuminants with different spectral power distributions. For any illuminant change, we found that the mapping between the cone coordinates of matching standard and test objects was well approximated by a diagonal linear transformation. In this sense, our results are consistent with von Kries's hypothesis [Handb. Physiol. Menschen 3, 109 (1905) [in Sources of Color Vision, D. L. MacAdam, ed. (MIT Press, Cambridge, Mass., 1970)]] that adaptation simply changes the relative sensitivity of the different cone classes. In addition, we examined the dependence of the diagonal transformation on the illuminant change. For the range of illuminants tested, we found that the change in the diagonal elements of the linear transformation was a linear function of the illuminant change.

Color Perception↗
PubMed1990

Surface characterizations of color thresholds.

We evaluate how well three different parametric shapes, ellipsoids, rectangles, and parallelograms, serve as models of three-dimensional detection contours. We describe how the procedures for deriving the best-fitting shapes constrain inferences about the theoretical visual detection mechanisms. The ellipsoidal shape, commonly assumed by vector-length theories, is related to a class of visual mechanisms that are unique only up to orthogonal transformations. The rectangle shape is related to a unique set of visual mechanisms, but since the rectangle is not invariant with respect to linear transformations the estimated visual mechanisms are dependent on the stimulus coordinate frame. The parallelogram is related to a unique set of visual mechanisms and can be derived by methods that are independent of the stimulus coordinate frame. We evaluate how well these shapes approximate detection contours, using 2-deg test fields with a long (1-sec) Gaussian time course. Two statistical tests suggest that the parallelogram model is too strong. First, we find that the ellipsoid and rectangle shapes fit the data with the same precision as the variance in repeated threshold measurements. The parallelogram model, which has more free parameters, fits the data with more precision than the variance in repeated threshold measurements. Second, although the parallelogram model provides a slightly better fit of our data than the other two shapes, it does not serve as a better guide than the ellipsoidal model for interpolating from the measurements to thresholds in novel color directions.

Calibration↗
PubMed1989

Black light: how sensors filter spectral variation of the illuminant.

Visual sensor responses may be used to classify objects on the basis of their surface reflectance functions. In a color image, the image data are represented as a vector of sensor responses at each point in the image. This vector depends both on the surface reflectance function and on the spectral power distribution of the ambient illumination. Algorithms designed to classify objects on the basis of their surface reflectance functions typically attempt to overcome the dependence of the sensor responses on the illuminant by integrating sensor data collected from multiple surfaces. In machine vision applications, we show that it is often possible to design the sensor spectral responsivities so that the vector direction of the sensor responses does not depend upon the illuminant. We state the conditions under which this is possible and perform an illustrative calculation. In biological systems, where the sensor responsivities are fixed, we show that some changes in the illumination cause no change in the sensor responses. We call such changes in illuminant black illuminants. It is possible to express any illuminant as the sum of two unique components. One component is a black illuminant. We call the second component the visible component. The visible component of an illuminant completely characterizes the effect of the illuminant on the vector of sensor responses.

Artificial Intelligence↗
PubMed1986

Analysis of the retinex theory of color vision.

If color appearance is to be a useful feature in identifying an object, then color appearance must remain roughly constant when the object is viewed in different contexts. People maintain approximate color constancy despite variation in the color of nearby objects and despite variation in the spectral power distribution of the ambient light. Land's retinex algorithm is a model of human color constancy. We analyze the retinex algorithm and discuss its general properties. We show that the algorithm is too sensitive to changes in the color of nearby objects to serve as an adequate model of human color constancy.

Algorithms↗