Model-Based Perceptual Grouping and Shape Abstraction

Abstract

Contour features are re-emerging in the categorization community as it moves from appearance back to shape. However, the classical assumption of one-to-one correspondence between an extracted image contour and a model contour constrains category models to be highly brittle, offering little abstraction between image and model. Moreover, todaypsilas contour-based models are category-specific, offering no mechanism for contour grouping and abstraction in the absence of an object prior. We present a novel framework for recovering a set of abstract parts from a multi-scale contour image. Given a user-specified part vocabulary and an image to be analyzed, the system covers the image with abstract part models drawn from the vocabulary. More importantly, correspondence between image contours and part contours is many-to-one, yielding a powerful shape abstraction mechanism. We illustrate the strengths and weaknesses of this work in progress on a set of anecdotal scenes.