Perceived transparency was studied as a constancy problem. In the episcotister (E-) model of scission, luminances are partitioned into layer and background components; four luminances determine values of two layer parameters that specify constancy of a transparent layer on different backgrounds. The E-model was tested in an experiment in which 12 Ss matched 24 pairs of four-luminance patterns by adjusting two luminances of the comparison pattern. Both the standard and the comparison were perceived as a transparent layer on a checkerboard. The E-model predicts matches when layer values are identical in the two patterns. One parameter was constant, constraining the adjustment along the second dimension. Obtained values corresponded well with E-predictions. Alternative models based on local luminance or average contrast ratios accounted for less variability. Results indicate that transparency models should utilize luminance, not reflectance, as the independent variable.
Robot-vision models of color constancy that are based on a linear approximation of illuminant and reflectance spectra are often generalized to the human visual system. Dannemiller's1 computational approach to color constancy is a good example. In this paper it is shown that such a procedure to estimate illuminant and reflectance spectra is less plausible for the human visual system than is implicitly assumed in robot-vision. The resemblance of the modeled hypothetical visual systems to the human visual system is misleading since it implies that surface reflectance is the illuminant-invariant object color descriptor the human visual system uses to achieve color constancy. However, an alternative type of descriptor is available that is not used to recover reflectance spectra. It has the advantage of allowing an interpretation that is preferable from the human point of view.
One of the peculiar phenomena occurring in two color pictures is a severe loss of cohesiveness when figure(s) and ground become isoluminous, whereas when both luminance and color differences exist between figures and background grouping of similarly colored parts is just a very dominating effect. In this paper we will present a quantitative method to measure this effect quantitatively. In a four-alternative-forced choice procedure pictures, disturbed by noise are presented. We will also discuss the possibility to use this method for measuring combined color and brightness differences.
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