Contour Grouping and Abstraction Using Simple Part Models

Abstract. 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

Spatiotemporal Contour Grouping using Abstract Part Models

Abstract. In recent work [1], we introduced a framework for modelbased perceptual grouping and shape abstraction using a vocabulary of parts, the framework grouped image contours whose abstract shape was consistent with one of the part models. While the results showed promise, the representational gap between the actual image contours that make up an exemplar shape and the contours that make up an abstract part model is significant, and an abstraction of a group of image contours may be consistent with more than one part model; therefore, while recall of ground-truth parts was good, precision was poor. In this paper, we address the precision problem by moving the camera and exploiting spatiotemporal constraints in the grouping process. We introduce a novel probabilistic, graph-theoretic formulation of the problem, in which the spatiotemporal consistency of a perceptual group under camera motion is learned from a set of training sequences. In a set of comprehensive experiments, we demonstrate (not surprisingly) how a spatiotemporal framework for part-based perceptual grouping significantly outperforms a static image version.