Human observers can reliably and accurately estimate the reflectance of surfaces in the presence of either strongly visible multiplicative illumination, or additive veiling light. In the presence of sufficient information about scene layout, this ability is only mildly affected by the sharpness of a shadow, and remains intact for sharp transparent overlays and spotlights. Using a Bayesian approach, the authors have sought to explore optimal limits to this ability. The first step is to construct the posterior probability of the reflectance and illumination maps conditional on the image. The second step is to locate a maximum, preferably the global maximum, of the posterior probability. In the final step, the authors compare the simulations to human estimation and perception of reflectance in a world of piece-wise constant reflectances and illuminants.
|Original language||English (US)|
|Number of pages||1|
|Issue number||1 SUPPL|
|State||Published - Jan 1 1988|
|Event||International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA|
Duration: Sep 6 1988 → Sep 10 1988