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Proceedings Paper

Using angle densities for 3D recognition
Author(s): Raashid Malik; Taegkeun Whangbo
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Paper Abstract

Using computer vision to recognize 3-D objects is complicated by the fact that geometric features vary with view orientation. The key in designing recognition algorithms is therefore based on understanding and quantifying the variation of certain cardinal features. The features selected for study in the research reported in this paper are the angles between landmarks in a scene. The spatial arrangement of landmarks on an object may constitute a unique characteristic of that object. As an example the angles between the wing tips and the nose cone of an aircraft may be adequate in distinguishing amongst a given class of aircraft. In a class of polyhedral objects the angles at certain vertices may form a distinct and characteristic alignment of faces. For many other classes of objects it may be possible to identify distinctive spatial arrangements of some readily identifiable landmarks. In this paper we derive the two dimensional joint density function of two angles in a scene given an isotropic view orientation and an orthographic projection. This analytic expression is useful in deriving likelihood functions which may be used to obtain measures of the likelihood of angle combinations in images of known objects or scenes. These likelihood functions allow us to establish statistical decision schemes to recognize objects. Experiments have been conducted to evaluate the usefulness of the proposed methods.

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: 10.1117/12.189099
Show Author Affiliations
Raashid Malik, Stevens Institute of Technology (United States)
Taegkeun Whangbo, 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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