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ACCV
2010
Springer

Abstraction and Generalization of 3D structure for recognition in large intra-class variation

8 years 11 months ago
Abstraction and Generalization of 3D structure for recognition in large intra-class variation
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 c 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 di erent 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/classi cation method and discuss advantages over rule based systems.
Gowri Somanath, Chandra Kambhamettu
Added 03 Nov 2010
Updated 02 Mar 2011
Type Conference
Year 2010
Where ACCV
Authors Gowri Somanath, Chandra Kambhamettu
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