What makes viewpoint invariant properties perceptually salient?: A computational perspective

Many perceptually salient image properties identified by the Gestalt psychologists, such as collinearity, parallelism, and good continuation, are viewpoint invariant. That is, there exist scene structures that always produce images with these properties regardless of viewpoint, while other scene structures virtually never produce these properties. This has suggested that the perceptual salience of viewpoint invariants is due to the leverage they provide for inferring 3-D properties of objects and scenes. However, we show that viewpoint invariance is not sufficient to distinguish these Gestalt properties; one can define an infinite number of viewpoint invariant properties that are not perceptually salient. We then show that generally, the perceptually salient viewpoint invariant properties are minimal, in the sense that they can be derived using less image information than non-salient properties. Computations that use minimal properties are more tractable than those requiring higher order properties. This provides support for the hypothesis that the biological relevance of an image property is determined both by the extent to which it provides information about the world and by the ease with which this property can be computed.