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Abstraction and Generalization of 3D Structure for Recognition in Large Intra-Class Variation

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Computer Vision – ACCV 2010 (ACCV 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6494))

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Abstract

Humans have abstract models for object classes which helps recognize previously unseen instances, despite large intra-class variations. Also objects are grouped into classes based on their purpose. Studies in cognitive science show that humans maintain abstractions and certain specific features from the instances they observe. In this paper, we address the challenging task of creating a system which can learn such canonical models in a uniform manner for different classes. Using just a few examples the system creates a canonical model (COMPAS) per class, which is used to recognize classes with large intra-class variation (chairs, benches, sofas all belong to sitting class). We propose a robust representation and automatic scheme for abstraction and generalization. We quantitatively demonstrate improved recognition and classification accuracy over state-of-art 3D shape matching/classification method and discuss advantages over rule based systems.

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Author information

Authors and Affiliations

  1. Video/Image Modeling and Synthesis (VIMS) Lab, Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA

    Gowri Somanath & Chandra Kambhamettu

Authors
  1. Gowri Somanath
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  2. Chandra Kambhamettu
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Editor information

Editors and Affiliations

  1. Technion – Israel Institute of Technology, Department of Computer Science, 32000, Haifa, Israel

    Ron Kimmel

  2. The University of Auckland, 37 Kohimarama Road , Mission Bay, 1071, Auckland, New Zealand

    Reinhard Klette

  3. National Institute of Informatics, Chiyoda, 1018430, Tokyo, Japan

    Akihiro Sugimoto

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Somanath, G., Kambhamettu, C. (2011). Abstraction and Generalization of 3D Structure for Recognition in Large Intra-Class Variation. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_38

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  • DOI: https://doi.org/10.1007/978-3-642-19318-7_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19317-0

  • Online ISBN: 978-3-642-19318-7

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