Current approaches to visual object class detection mainly focus on the recognition of basic level categories, such as cars, motorbikes, mugs and bottles. Although these approaches have demonstrated impressive performance in terms of recognition, their restriction to these categories seems inadequate in the context of embodied, cognitive agents. Here, distinguishing objects according to functional aspects based on object affordances is important in order to enable manipulation of and interaction between physical objects and cognitive agent.
In this paper, we propose a system for the detection of functional object classes, based on a representation of visually distinct hints on object affordances (
). It spans the complete range from tutor-driven acquisition of affordance cues, learning of corresponding object models, and detecting novel instances of functional object classes in real images.