  One of the purposes of perception is to bridge between sensors and conceptual
understanding. Marr's Primal Sketch combined initial edge-finding with multiple
downstream processes to capture aspects of visual perception such as grouping
and stereopsis. Given the progress made in multiple areas of AI since then, we
have developed a new framework inspired by Marr's work, the Hybrid Primal
Sketch, which combines computer vision components into an ensemble to produce
sketch-like entities which are then further processed by CogSketch, our model
of high-level human vision, to produce both more detailed shape representations
and scene representations which can be used for data-efficient learning via
analogical generalization. This paper describes our theoretical framework,
summarizes several previous experiments, and outlines a new experiment in
progress on diagram understanding.
