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Proceedings Paper

Model-based surface classification
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Paper Abstract

A suite of model-driven techniques for identification of 3-D quadric surfaces (cones, cylinders, and spheres) in segmented range imagery is presented. These techniques use range data, surface normal calculated on that data, knowledge of geometric characteristics of the various surfaces, and known model parameters to perform the classification. Second derivative quantities such as curvature, which are unreliable in the presence of noise, are avoided. Model information such as radii and vertex angles are used to guide the classification. Hough-based techniques are employed for extraction of spherical and cylindrical parameters, while conic parameters are presented for numerous scenes of both real and synthetic objects including part jumbles, objects in many poses, and noiseless and noisy synthetic objects. Empirical tests reveal that these methods have advantages (e.g. they appear to be very accurate) over previous methods.

Paper Details

Date Published: 1 September 1991
PDF: 12 pages
Proc. SPIE 1570, Geometric Methods in Computer Vision, (1 September 1991); doi: 10.1117/12.48429
Show Author Affiliations
Timothy S. Newman, Michigan State Univ. (United States)
Patrick J. Flynn, Washington State Univ. (United States)
Anil K. Jain, Michigan State Univ. (United States)


Published in SPIE Proceedings Vol. 1570:
Geometric Methods in Computer Vision
Baba C. Vemuri, Editor(s)

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