Anzeige
Erschienen in:
2008 | OriginalPaper | Buchkapitel
Functional Object Class Detection Based on Learned Affordance Cues
verfasst von : Michael Stark, Philipp Lies, Michael Zillich, Jeremy Wyatt, Bernt Schiele
Erschienen in: Computer Vision Systems
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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 (
affordance cues
). 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.