Abstract
A central theme of perceptual theory, from the Gestaltists to the present, has been the integration of local and global image information. While neuroscience has traditionally viewed perceptual processes as beginning with local operators with small receptive fields before proceeding on to more global operators with larger ones, a substantial body of evidence now suggests that supposedly later processes can impose decisive influences on supposedly earlier ones, suggesting a more complicated flow of information. We consider this problem from a computational point of view. Some local processes in perceptual organization, like the organization of visual items into a local contour, can be well understood in terms of simple probabilistic inference models. But for a variety of reasons nonlocal factors such as global “form” resist such simple models. In this talk I'll discuss constraints on how form- and region-generating probabilistic models can be formulated and integrated with local ones. From a computational point of view, the central challenge is how to embed the corresponding estimation procedure in a locally-connected network-like architecture that can be understood as a model of neural computation.
Meeting abstract presented at VSS 2012