Bridging the representation gap between models and exemplars
Sven Dickinson · 2001
The recognition community has long avoided bridging the representational gap between traditional, low-level image features and generic models. Instead, the gap has been artificially eliminated by either bringing the image closer to the models, using simple scenes containing idealized, textureless objects, or by bringing the models closer to the images, using 3-D CAD model templates or 2-D appearance model templates. In this paper, we begin by examining this trend and track its evolution over the last 30 years. We argue for the need to bridge (not eliminate) this representational gap, and review our recent progress for the domain of model acquisition. Specifically, we address the problem of automatically acquiring a generic 2-D view-based class model from a set of images, each containing an exemplar object belonging to that class. We introduce a novel graphtheoretical formulation of the problem, and demonstrate the approach on real imagery. 1.