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  Functional Object Class Detection Based on Learned Affordance Cues

Stark, M., Lies, P., Zillich, M., Wyatt, J., & Schiele, B. (2008). Functional Object Class Detection Based on Learned Affordance Cues. In A. Gasteratos, M. Vincze, & J. Tsotsos (Eds.), Computer Vision Systems: 6th International Conference, ICVS 2008 Santorini, Greece, May 12-15, 2008 (pp. 435-444). Berlin, Germany: Springer.

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 Creators:
Stark, M, Author
Lies, P1, Author              
Zillich , M, Author
Wyatt, J, Author
Schiele, B, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Current approaches to visual object class detection mainly focus on the recognition of abstract object categories, such as cars, motorbikes, mugs and bottles. Although these approaches have demonstrated impressive performance in terms of recognition, their restriction to abstract categories seems artificial and inadequate in the context of embodied, cognitive agents. Here, distinguishing objects according to functional aspects based on object affordances is vital for a meaningful human-machine interaction. In this paper, we propose a complete 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 cycle from tutor-driven acquisition of affordance cues, one-shot learning of corresponding object models, and detecting novel instances of functional object classes in real images.

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 Dates: 2008-05
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-79547-6_42
BibTex Citekey: 5092
 Degree: -

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Title: 6th International Conference on Computer Vision Systems
Place of Event: Santorini, Greece
Start-/End Date: 2008-05-12 - 2008-05-15

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Title: Computer Vision Systems: 6th International Conference, ICVS 2008 Santorini, Greece, May 12-15, 2008
Source Genre: Proceedings
 Creator(s):
Gasteratos, A, Editor
Vincze, M, Editor
Tsotsos, JK, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 435 - 444 Identifier: ISBN: 978-3-540-79546-9

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 5008 Sequence Number: - Start / End Page: - Identifier: -