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3 October 1995 Geometric features in images of polyhedra
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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.
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Raashid Malik and Hyeon-June Kim "Geometric features in images of polyhedra", Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995);


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