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Proceedings Paper

Shape feature variation for recognition
Author(s): Raashid Malik; Hyeon-June Kim
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Paper Abstract

Geometric features of an object in an image vary with the view angle of the camera or with the orientation of the object. The variation in measured features is often expressed using probability density functions. The probabilistic bases of this approach arise from assumptions concerning the unconstrained pose of the object and the consequent that the two orientation angles are random variables with a known joint density. In this paper we concentrate on recognizing the faces of polyhedral surfaces. We start by quantifying the minimal features in a face that are scale invariant and rotation invariant (about the optical axis). Two features we found to be analytically tractable were the normalized area between two edges and the normalized innerproduct of two edge vectors. We refer to the features as quadrature line ratios. The joint density of these measured features in orthographic images has been derived. The variation of these features in images are analyzed and plotted. Likelihood functions based on this density have been developed and used in distinguishing and recognizing faces of polyhedra. Experiments with real and simulated data have been conducted to verify the efficacy of the proposed schemes and the results show that the method is promising.

Paper Details

Date Published: 13 October 1994
PDF: 12 pages
Proc. SPIE 2354, Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision, (13 October 1994); doi: 10.1117/12.189101
Show Author Affiliations
Raashid Malik, Stevens Institute of Technology (United States)
Hyeon-June Kim, Stevens Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2354:
Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision
David P. Casasent, Editor(s)

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