The human visual system has a remarkable ability to interpret smooth patterns of light on a surface in terms of 3-D surface geometry. Classical studies of shape-from-shading perception have assumed that surface irradiance varies with the angle between the local surface normal and a collimated light source. This model holds, for example, on a sunny day. One common situation in which this model fails to hold, however, is under diffuse lighting such as on a cloudy day. Here we report on the first psychophysical experiments that address shape-from-shading under a uniform diffuse-lighting condition. Our hypothesis was that shape perception can be explained with a perceptual model that “dark means deep”. We tested this hypothesis by comparing performance in a depth-discrimination task to performance in a brightness-discrimination task, using identical stimuli. We found a significant correlation between responses in the two tasks, supporting a dark-means-deep model. However, overall performance in the depth-discrimination task was superior to that predicted by a dark-means-deep model. This implies that humans use a more accurate model than dark-means-deep to perceive shape-from-shading under diffuse lighting.
To solve the ill-posed problem of shape-from-shading, the visual system often relies on prior assumptions such as illumination from above or viewpoint from above. Here we demonstrate that a third prior assumption is used--namely that the surface is globally convex. We use complex surface shapes that are realistically rendered with computer graphics, and we find that performance in a local-shape-discrimination task is significantly higher when the shapes are globally convex than when they are globally concave. The results are surprising because the qualitative global shapes of the surfaces are perceptually unambiguous. The results generalise findings such as the hollow-potato illusion (Hill and Bruce 1994 Perception 23 1335-1337) which consider global shape perception only.
As the orientation or illumination of an object changes so does its appearance. This paper considers how observers are nonetheless able to recognize objects that have undergone such changes. In particular the paper tests the hypothesis that observers rely on temporal correlations between different object views to decide whether they are views of the same object or not. In a series of experiments subjects were shown a sequence of views representing a slowly transforming object. Testing revealed that subjects had formed object representations which were directly influenced by the temporal characteristics of the training views. In particular, introducing spurious correlations between views of different people's heads caused subjects to regard those views as being of a single person. This rapid and robust overriding of basic generalization processes supports the view that our recognition system tracks the correlated appearance of views of objects across time. Such view associations appear to allow the visual system to solve the view invariance problem without recourse to complex illumination models for extracting 3D form, or the use of the image plane transformations required to make appearance-based comparisons.
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