Contour Grouping and Abstraction Using Simple Part Models

We address the problem of contour-based perceptual grouping using a user-defined vocabulary of simple part models. We train a family of classifiers on the vocabulary, and apply them to a region oversegmentation of the input image to detect closed contours that are consistent with some shape in the vocabulary. Given such a set of consistent cycles, they are both abstracted and categorized through a novel application of an active shape model also trained on the vocabulary. From an image of a real object, our framework recovers the projections of the abstract surfaces that comprise an idealized model of the object. We evaluate our framework on a newly constructed dataset annotated with a set of ground truth abstract surfaces.