Model of visual adaptation and contrast detection

Sperling, George

doi:10.3758/bf03210193

Cited by 138 publications

(31 citation statements)

References 37 publications

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“…In the classical proposal, the center and surround contributions combine linearly to determine the cell's response. The present approach adopts a multiplicative, or shunting, formalism (Furman, 1965;Grossberg, 1970;Hodgkin, 1964;Sperling, 1970;Sperling & Sondhi, 1968), in which center and surround contributions generate a nonlinear, normalized-difference measure proportional to the luminance ratios at borders.…”

Section: Stage 1: Contrast Measurementmentioning

confidence: 99%

Lightness from contrast: A selective integration model

Ross

Pessoa

2000

Perception & Psychophysics

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As has been observed by Wallach(1948), perceived lightness is proportional to the ratio between the luminances of adjacent regions in simple disk-annulus or bipartite scenes. This psychophysical fmding resonates with neurophysiological evidence that retinal mechanisms of receptor adaptation and lateral inhibition transform the incoming illuminance array into local measures of luminance contrast. In many scenic configurations, however, the perceived lightness of a region is not proportional to its ratio with immediately adjacent regions. In a particularly striking example of this phenomenon, called White's illusion, the relationship between the perceived lightnesses of two gray regions is the opposite of what is predicted by local edge ratios or contrasts. This paper offers a new treatment of how local measures of luminance contrast can be selectively integrated to simulate lightness percepts in a wide range of image configurations. Our approach builds on a tradition of edge integration models (Hom, 1974;Land & McCann, 1971)and contrast/filling-in models (Cohen & Grossberg, 1984;Gerrits & Vendrik 1970;Grossberg & Mingolla, 1985a, 1985b. Our selective integration model (SIM)extends the explanatory power of previous models, allowing simulation of a number of phenomena, including White's effect, the Benary Cross, and shading and transparency effects reported by Adelson (1993), as well as aspects of motion, depth, haploscopic, and Gelb induced contrast effects. Wealso include an independently derived variant of a recent depthful version of White's illusion, showing that our model can inspire new stimuli.In everyday experience, surface color constancy is an effortless achievement of the visual system. That is, despite variations in lighting and movement or displacement of objects across visual contexts, object color appears to a large extent to remain constant. For example, consider the appearance of a teapot in a familiar kitchen scene. Large daily variations in the illumination of the kitchen, including shadowing, do not alter the apparent surface color of the teapot (Type I, or illumination-independent constancy). What is more, the teapot remains the same apparent color, despite being placed at different locations, whether on a red table cloth or on a white counter (Type II, or background-independent constancy). Color constancy refers, then, to the fact that surface color remains largely constant, despite changes in the intensity and composition of the light reflected to the eyes from both the object itself and from surrounding objects.' This paper is specifically concerned with the achromatic, or black-to-white, dimension of perceived surface color (perceived reflec-L.P.was funded by CNPq/Brazil Grant 520419/96-0. W.D.R.was supported in part by the Whitaker Foundation. The authors thank Alan Gilchrist, Robert O'Shea, and the other reviewers for valuable discussions concerning the presentation of the model. Correspondence concerning this article should be addressed to W. D. Ross, Machine Intelligence Group, MIT Li...

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Section: Stage 1: Contrast Measurementmentioning

confidence: 99%

Lightness from contrast: A selective integration model

Ross

Pessoa

2000

Perception & Psychophysics

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“…Using a point spread function, which would provide a clearer picture in that aspect the discrepancies found (for disks) are even larger. Rd -3.6 min of arc One approach to deal with this partial disaccordance has been provided by among others : Sperling (1970). Thomas (1970).…”

Section: Experiments 2 Testing the Lineariry Hypothesismentioning

confidence: 99%

The foveal point spread function as a determinant for detail vision

Blommaert¹,

Roufs²

1981

Vision Research

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Abstract-A point spread function, chosen to link contrast sensitivity and stimulus dimensions. can be obtained from measured thresholds by assuming small-signal-linearity and peak detection for the visual system. To that end a special case of summation of subthreshold signals (perturbation) is used. taking specific measures against the effect of sensitivity drift. The basic assumptions are tested simuitaneousl~-and confirmed. Other provisional assumptions tike radial symmetry and homogeneity were evaluated along a horizontal and a vertical meridian through the fovea. In the fovea no deviation from radial symmetry was found. The effect of inhomogeneity within the central fovea. seems to be too small to cause a significant change in the point spread function. The validity for predicting thresholds of stimuli exposing larger areas is tested. Annuli with varying radii show no significant aberration if probability summation is taken into account. Predicted disk thresholds, however, show a large discrepancy with experiment for radii larger than 2min arc. A possible extension of the model with multiple-detection units having tuned sizes is evaluated. INTRODUCfIONIn order to investigate characteristics of detail vision stimuli like Landolt C's, lines or gratings are generally used. Sometimes, radially symmetrical stimuli like disks and annuli are used to investigate lateral processing over short distances (Fiorentini and Maffei, 1970;Westheimer, 1967). The spatial processing of the visual system is rather inhomogeneously over the retina. This is demonstrated by data on visual acuity (Le Grand, 1967) and thresholds for disks (Kishto, 1970;Wilson, 1970). A review is made by van Doorn et al. (1972). The effect is also found with thresholds of lines (Hines, 1976;Wilson and Bergen, 1979; Limb and Rubinstein, 1977). Wilson and Bergen, ibid, stressing the importance of local measurement of the inhomogeneous system. used line sources instead of gratings as stimuli. However, line sources of the usual type still stimulate retinal parts with different properties. The use of point sources is an obvious further step, the more so, since it is not clear how much the discrepancy between measured thresholds and prediction based on a line spread function (e.g. Wilson and Bergen, 1979) is disguised by effects of the mentioned extension of the lines.A point spread or weighting function can be used to characterize the effect of a near-threshold-pointsource on the contrast sensitivity of neighbouring retinal points (e.g. van Meeteren, 1973;Kelly, 1975). However, the threshold changes which have to be measured to obtain such a function are very small. Consequently special measures have to be taken to eliminate the effects of unavoidable sensitivity drift in order to obtain the required precision.In this report the results of a technique to obtain a point spread function from threshold measurements is investigated and the predictive power is tested. This point spread function should represent the simple processing of the visual system, thus ...

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“…First, inhibitory projections are wide ranging in the WTA models described. Global inhibition can be softened by adopting a Mexican hat network connectivity profile, whereby a point in the network sends spatial inhibitory connections extending over a longer range than excitatory connections (Somers et al, 1995;Sperling, 1970) (see Figure 1A). However, inhibitory neurons in neocortex are mostly limited in their lateral extent, making projections either vertically between cortical layers or proximal to their somata (Lund, 1987;Lund & Wu, 1997;Lund & Yoshioka, 1991;Lund, Hawken, & Parker, 1988;DeFelipe, 2002;Markram et al, 2004.…”

Section: Introductionmentioning

confidence: 99%

Anatomical Constraints on Lateral Competition in Columnar Cortical Architectures

Muir

Cook

2014

Neural Computation

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Competition is a well-studied and powerful mechanism for information processing in neuronal networks, providing noise rejection, signal restoration, decision making and associative memory properties, with relatively simple requirements for network architecture. Models based on competitive interactions have been used to describe the shaping of functional properties in visual cortex, as well as the development of functional maps in columnar cortex. These models require competition within a cortical area to occur on a wider spatial scale than cooperation, usually implemented by lateral inhibitory connections having a longer range than local excitatory connections. However, measurements of cortical anatomy reveal that the spatial extent of inhibition is in fact more restricted than that of excitation. Relatively few models reflect this, and it is unknown whether lateral competition can occur in cortical-like networks that have a realistic spatial relationship between excitation and inhibition. Here we analyze simple models for cortical columns and perform simulations of larger models to show how the spatial scales of excitation and inhibition can interact to produce competition through disynaptic inhibition. Our findings give strong support to the direct coupling effect-that the presence of competition across the cortical surface is predicted well by the anatomy of direct excitatory and inhibitory coupling and that multisynaptic network effects are negligible. This implies that for networks with short-range inhibition and longer-range excitation, the spatial extent of competition is even narrower than the range of inhibitory connections. Our results suggest the presence of network mechanisms that focus on intra-rather than intercolumn competition in neocortex, highlighting the need for both new models and direct experimental characterizations of lateral inhibition and competition in columnar cortex. D.R.M. is now at

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Model of visual adaptation and contrast detection

Abstract: A three-component model of spatial vision is proposed, consisting of (1)

Cited by 138 publications

References 37 publications

Lightness from contrast: A selective integration model

Lightness from contrast: A selective integration model

The foveal point spread function as a determinant for detail vision

Anatomical Constraints on Lateral Competition in Columnar Cortical Architectures

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