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Proceedings Paper

Geometric features in images of polyhedra
Author(s): Raashid Malik; Hyeon-June Kim
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Paper Abstract

In model-based object recognition, the features used to describe a model often represent various geometric properties of the model. But there is a difficulty in recognizing a 3D object when the orientation of the object or the view angle of the camera in three space is unknown. This occurs because measurements of the geometric features of an object in a 2D image vary with different view directions. The variations of measured features may be expressed using probability density functions. These densities may be used to completely characterize the observed variations. In this paper we introduce a recognition scheme based on the probabilistic analysis of view variations of geometric features. Our previous work quantified the view variations of a certain pair of features (referred to as quadrature line ratios) for planar surfaces which were scale invariant and image rotation invariant. That work is now extended to a complete 3D convex polyhedral object recognition scheme. We derive the joint density of two pairs of features which are measured from two non-coplanar faces of an object. Likelihood functions based on this density have been developed for each aspect of a polyhedral object and used in the recognition scheme. Experiments have been conducted to verify the efficiency of the proposed scheme.

Paper Details

Date Published: 3 October 1995
PDF: 12 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222701
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. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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