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Proceedings Paper

Fusion of symbolic and feature information for high-level object recognition
Author(s): Neelima Shrikhande; Jim Getzinger
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Paper Abstract

In this paper, we present an algorithm which uses symbolic as well as physical labels on the edges and surfaces to constrain the scene-model matching process. Symbolic labels are used to distinguish between curved and planar objects, occluding edges, background surface, etc. These are used along with physical labels such as distances and angles to prune the matching graph. This paper describes a real time object recognition environment that integrates the pruning method described above with low level image processing and high level object recognition algorithms. Results are reported for synthetic and real range images. Our results show that inclusion of symbolic labels improves the accuracy and efficiency of matching.

Paper Details

Date Published: 20 April 1993
PDF: 7 pages
Proc. SPIE 1827, Model-Based Vision, (20 April 1993); doi: 10.1117/12.143060
Show Author Affiliations
Neelima Shrikhande, Central Michigan Univ. (United States)
Jim Getzinger, Central Michigan Univ. (United States)


Published in SPIE Proceedings Vol. 1827:
Model-Based Vision
Hatem N. Nasr; Rodney M. Larson, Editor(s)

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