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Proceedings Paper

Heuristic edge detector for noisy range images
Author(s): Kung Chris Wu
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Paper Abstract

This paper presents a heuristic edge detector for extracting wireframe representations of objects from noisy range data. Jump and roof edges were detected successfully from range images containing additive white Gaussian noise with a standard deviation equal to as high as 1.2% of the measured range values. This represents an appreciable amount of noise since approximately 5% of the errors are greater than 12 cm and 32% of errors are greater than 6 cm at a distance of 5 meters. The noise insensitive characteristic of the heuristic edge detector enables low cost range scanners to be used for practical industrial applications. The availability of low cost active vision systems greatly broadens the horizon of integrating robotics vision systems to manufacturing automation.

Paper Details

Date Published: 13 October 1994
PDF: 8 pages
Proc. SPIE 2354, Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision, (13 October 1994); doi: 10.1117/12.189096
Show Author Affiliations
Kung Chris Wu, Univ. of Texas/El Paso (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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