Bayesian Models for Seeing Shapes and Depth We

review coniputational models of shape and deprh perception and relate them to visual psychophysics. The Bayesian approach to vision provides a fruitful the-oretical framework both for modeling individual modules, such ils stcrco, shading, tcxturc, and o c c l u ~ i ~ n. and for intcgrating thcir informatiom. In this formalism we represent depth by one, o r more, surfaces with prior probabilities for surface shape, corresponding to natural constraints, in order to avoid the ill-posedness of vision. O n theoretical grounds. the less information available to the module (and the less accurate i t is), then the more important the priors bccomc. This suggcsrs that visual illusions, and biascd perceptions, will arise for scenes for which the priors are not appropriate. We describe psychophysical experiments that are consistcnt with these ideas. For integration of different modules we advocate strong coupling, so that the modules can interact during computation and the priors can be modified. This framework is rich enough to accommodate straightforwardly both consonant and contradictory cue integration and different psychophysical experiments can be understood within the Bayesian approach