Predicting color from gray: the relationship between achromatic adjustment and asymmetric matching

Speigle, Jon M.; Brainard, David H.

doi:10.1364/josaa.16.002370

Cited by 38 publications

(46 citation statements)

References 23 publications

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“…In addition, categorical color constancy was as good for the chromatic as for the achromatic categories, with the exception of the black surfaces for which consistency was 100%. This is in agreement with Speigle and Brainard (1999), who showed that asymmetric matches for chromatic stimuli could be predicted from achromatic settings as long as the viewing conditions were held constant. Our data do point to a slight advantage for category prototypes in terms of classification consistency, but this issue needs to be investigated further with an experiment focused on category prototypes.…”

Section: Color Classification As a Methods For Measuring Color Constancysupporting

confidence: 92%

“…Such high constancy indices are in line with data collected for monitor simulations in full-field viewing conditions (Hansen et al, 2007;Murray et al, 2006;Olkkonen et al, 2009;Rinner & Gegenfurtner, 2000) and for achromatic settings for real stimuli (Speigle & Brainard, 1999) but are higher than indices reported for asymmetric matching experiments in either simulated or real scenes (e.g., Arend et al, 1991;Brainard et al, 1997).…”

Section: Color Constancy For the Achromatic Pointsupporting

confidence: 87%

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Categorical color constancy for real surfaces

et al. 2010

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In everyday experience, perceived colors of objects remain approximately constant under changes in illumination. This constancy is helpful for identifying objects across viewing conditions. Studies on color constancy often employ monitor simulations of illumination and reflectance changes. Real scenes, however, have features that might be important for color constancy but that are in general not captured by monitor displays. Here, we investigate categorical color constancy employing real surfaces and real illuminants in a rich viewing context. Observers sorted 450 Munsell samples into the 11 basic color categories under a daylight and four filtered daylight illuminants. We additionally manipulated illuminant cues from the local surround. Color constancy as quantified both with a classification consistency index and a standard color constancy index was high in both cue conditions. Observers generally classified colors with the same precision across different illuminants as across repetitions for the daylight illuminant. Moreover, the pattern of classification consistency in terms of stimulus hue, value, and chroma was similar when comparing different observers for the daylight illuminant and when comparing individual observers across different illuminants. We conclude that color categorization is robust under illuminant changes as well as across observers, thus potentially serving both object identification and communication.

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Section: Color Classification As a Methods For Measuring Color Constancysupporting

confidence: 92%

Section: Color Constancy For the Achromatic Pointsupporting

confidence: 87%

Categorical color constancy for real surfaces

et al. 2010

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“…Speigle and Brainard (1999) showed that measurements of what object appears achromatic under different illuminants may be used to predict how the appearance of othercolored objects will be affected across the same illumination changes.…”

Section: Methods Overviewmentioning

confidence: 99%

Does human color constancy incorporate the statistical regularity of natural daylight?

2004

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The chromaticities of natural daylights cluster around the blackbody locus. We investigated whether the mechanisms that mediate human color constancy embody this statistical regularity of the natural environment, so that constancy is best when the illuminant change is one likely to occur. Observers viewed scenes displayed on a CRT-based stereoscope and adjusted a test patch embedded in the scene until it appeared achromatic. Scenes were rendered using physics-based graphics software (RADIANCE) coupled with custom extensions that ensured colorimetric accuracy. Across conditions, both the simulated illuminant and the simulated reflectance of scene objects were varied. Achromatic settings from paired conditions were used to compute a constancy index (CI) that characterizes the stability of object appearance across the two illuminants of the pair. Constancy indices were measured for four illuminant changes from a Neutral illuminant (CIE D65). Two of these changes (Blue and Yellow) were consistent with the statistics of daylight, whereas two (Green and Red) were not. The results indicate that constancy was least across the Red change, as one would expect for the statistics of natural daylight. Constancy for the Green direction, however, exceeded that for the Yellow illuminant change and was comparable to that for the Blue. This result is difficult to reconcile with the hypothesis that mechanisms of human constancy incorporate the statistics of daylights. Some possible reasons for the discrepancy are discussed.

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“…Although sometimes presented as measuring colour constancy, this method in fact provides data only on the appearance of neutral (i.e. greyscale) surfaces: indirect arguments can be made about its implications for asymmetric colour matching, but they require significant additional assumptions [38]. Insofar as subjects regard the method as adjusting surface colour, it effectively records their estimate of illuminant colour at that point in the scene (Figs.…”

Section: Judging Whitementioning

confidence: 99%