Lightness models, gradient illusions, and curl

Arend, Lawrence E.; Goldstein, Robert B.

doi:10.3758/bf03211515

Cited by 33 publications

(35 citation statements)

References 23 publications

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“…Although this explanation is now believed to be deficient on the grounds that it predicts Mach bands to be strongest at a step edge where in fact none are observed (Fiorentini, 1972;Ross, Holt & Johnstone, 1981;Ratliff, 1984), it nevertheless anticipated themes central to our current understanding of vision. More recently, brightness illusions have been studied in the context of linear systems analysis (Campbell, Howell & Robson, 1971;Campbell, Howell & Johnstone, 1978;Sullivan & Georgeson, 1977;Dooley & Greenfield, 1977) have been predicted in a number of computational models of both spatial vision (Watt & Morgan, 1985;Morrone & Burr, 1988) and reflectance recovery (Horn, 1974;Land, 1986;Hurlbert & Poggio, 1988;Blake, 1985) and have been the principal subject of models explicitly concerned with brightness coding (Arend, Buehler & Lockhead, 1971;Arend & Goldstein, 1987;Hamada, 1984;Cohen & Grossberg, 1984;Grossberg & Todorovic, 1988;.…”

Section: Introductionmentioning

confidence: 99%

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A multi-channel approach to brightness coding

1992

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A model of brightness coding is presented which is shown to predict the appearance of a number of classical brightness phenomena. The model is known as MIDAAS which stands for Multiple Independent Descriptions Averaged Across Scale. In common with many other approaches to brightness perception MIDAAS imputes to local feature detectors a central role in the computation of brightness. It also explicitly recognises the crucial importance to brightness perception of feature detectors operating at different spatial scales. The unique and definitive feature of the model however is the supposition that each scale of spatial filtering operates as if to generate its own description of the pattern of brightness relationships in the image. The final percept is then provided by the composite of those individual brightness descriptions. It is shown that MIDAAS provides a good account of a variety of Mach band phenomena, the conditions under which the Missing Fundamental illusion is observed, the effect of occluding bars on the apparent contrast of step edges, the Chevreul illusion, simultaneous brightness contrast and the non-linear appearance of high contrast sinusoidal gratings. The advantages of MIDAAS over other approaches to brightness perception is discussed, as well as its current limitations.

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Section: Introductionmentioning

confidence: 99%

“…1974;Blake, 1985;Arend & Goldstein, 1987). although they appear not to be able to do so with any quantitative precision ).…”

Section: Lightness Integration Modelsmentioning

confidence: 99%

A multi-channel approach to brightness coding

1992

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“…lightness distributions by integration ofluminance ratios at edges (Arend & Goldstein, 1987a;Gilchrist, Delman, & Jacobsen, 1983;Land & McCann, 1971) produce correct relative lightnesses only if no illumination edges are included. To test the coplanar ratio hypothesis, Gilchrist (1977) placed two gray papers against the far wall of a brightly illuminated room, and another one on the wall of a closer, dimly illuminated room.…”

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Illumination change at a depth edge can reduce lightness constancy

Schirillo

Arend

1995

Perception & Psychophysics

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Lightness constancy requires that a surface retain its lightness not only when the illumination is changed but also when the surface is moved from one background to another. Occlusion of one surface by another frequently results in a retinal juxtaposition of patches under different illuminations. At such edges, retinal luminance ratios can be much higher than in scenes with a single illumination. Wedemonstrate that such retinal adjacencies can produce failures of lightness constancy. Weargue that they are responsible for departures from perfect lightness constancy in two prior experiments that examined the effects of depth relations on lightness constancy.

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“…According to several theories, brightnesses and lightnesses in complicated images are derived from signals specifying the luminance contrasts of edges (Arend & Goldstein, 1987a;Blake, 1985;Horn, 1974;Land & McCann, 1971). If so, Whittle and Challands's (1969) data indicate that lightnesses of surfaces might also vary substantially as a function of mean luminance.…”

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Mesopic lightness, brightness, and brightness contrast

Arend

1993

Perception & Psychophysics

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At mesopic mean luminances, a fixed luminance contrast produces less brightness contrast than it does at photopic luminances. This suggests that lightnesses of surfaces might also be altered at low luminances. I measured lightness, brightness, and brightness contrast in CRT simulations of achromatic paper patchworks. The illuminance of the standard pattern was fixed, producing 0.12, 1.2, or 12 cd/m'. The illuminance on the test pattern was varied in a lightness constancy paradigm. Constant brightness contrast required more luminance contrast at lower mean luminances. Failures of lightness constancy occurred at the lowest mean luminances, but they were minor in comparison with the loss of brightness contrast in the same pattern. These results have implications for imaging applications. Often, image content falls in both the photopic and the mesopic ranges. Our results indicate that brightness contrast may decrease substantially in low-luminance regions without large changes of surface lightness.

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Lightness models, gradient illusions, and curl

Cited by 33 publications

References 23 publications

A multi-channel approach to brightness coding

A multi-channel approach to brightness coding

Illumination change at a depth edge can reduce lightness constancy

Mesopic lightness, brightness, and brightness contrast

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