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Proceedings Paper

Three-dimensional model-based object recognition and pose estimation using probabilistic principal surfaces
Author(s): Kui-yu Chang; Joydeep Ghosh
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Paper Abstract

A novel scheme using spherical manifolds is proposed for the simultaneous classification and pose estimation of 3D objects from 2D images. The spherical manifold imposes a local topological constraint on samples that are close to each other, while maintaining a global structure. Each node on the spherical manifold also corresponds nicely to a pose on a viewing sphere with 2 degrees of freedom. The proposed system is applied to aircraft classification and pose estimation.

Paper Details

Date Published: 14 April 2000
PDF: 12 pages
Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000); doi: 10.1117/12.382913
Show Author Affiliations
Kui-yu Chang, Univ. of Texas at Austin (United States)
Joydeep Ghosh, Univ. of Texas at Austin (United States)

Published in SPIE Proceedings Vol. 3962:
Applications of Artificial Neural Networks in Image Processing V
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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