In this talk I will present some work in progress. We consider the problem of recognizing what parts of an image are perceived as being in the foreground. We use a variant of the Pao-Geiger-Rubin model, which uses an energy dissipation approach to the problem. The model is surface-based, rather than contour-based. Specifically, the edges of the image are not viewed as isolated contours, but are viewed as bounding a surface. Each local edge has a local hypothesis; for example, a north-south edge might think "the region immediately to the left of me is part of the figure". The model then uses energy dissipation methods to seek assignments of local hypotheses that are mutually agreeable, yielding a segmentation that might be perceived. We test the model on various images to address questions like: does the model "perceive" smaller objects to be figure? convex objects to be figure? how does it perform on images that viewers report to have two different segmentations? (Images like the famous faces-vs-vase optical illusion.)