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Proceedings Paper

Model-based shape classification using shape-transformation-invariant descriptors
Author(s): Samuel C. Lee; Yiming Wang; Elisa T. Lee
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Paper Abstract

The shape classification methods derived from similarity measures based on the shape-transformation-variant descriptors often require shape normalization/standardization that involves complicated computations and contour or code matching schemes. In this paper, we introduce a quantitative similarity measure and a new model-based shape classification method which uses exclusively the shape-transformation-invariant descriptors. This method eliminates all possible variations and potential problems caused by shape transformation, and complicated contour matching and/or shape normalization/standardization procedures.

Paper Details

Date Published: 18 January 2006
PDF: 5 pages
Proc. SPIE 6066, Vision Geometry XIV, 606604 (18 January 2006); doi: 10.1117/12.643555
Show Author Affiliations
Samuel C. Lee, Univ. of Oklahoma (United States)
Yiming Wang, Univ. of Oklahoma (United States)
Elisa T. Lee, Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 6066:
Vision Geometry XIV
Longin Jan Latecki; David M. Mount; Angela Y. Wu, Editor(s)

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