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Proceedings Paper

Symbolic Surface Descriptors For 3-Dimensional Object Recognition
Author(s): Ramesh Jain; Thawach Sripradisvarakul; Nancy O'Brien
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Paper Abstract

We are studying classification of symbolic surface de-scriptors in classes that will allow fast approaches for 3-D object recognition. In our approach for object recognition, we will use features to hypothesize objects using parallel distributed approach, and then use models of objects to find objects that are present in a scene. Symbolic surface descriptors represent global features of an object and do not change when the object is partially occluded, while local features (such as corners or edges) may disappear en-tirely. We have developed a technique to segment surfaces and compute their polynomial surface descriptors. In this paper we present results of our study to determine which different types of surface descriptors (such as cylindrical, spherical, elliptical, hyperbolic, etc) can be reliably recovered from biquadratic equation models of various surfaces.

Paper Details

Date Published: 21 August 1987
PDF: 10 pages
Proc. SPIE 0754, Optical and Digital Pattern Recognition, (21 August 1987); doi: 10.1117/12.939971
Show Author Affiliations
Ramesh Jain, The University of Michigan (United States)
Thawach Sripradisvarakul, The University of Michigan (United States)
Nancy O'Brien, The University of Michigan (United States)

Published in SPIE Proceedings Vol. 0754:
Optical and Digital Pattern Recognition
Hua-Kuang Liu; Paul S. Schenker, Editor(s)

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