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Proceedings Paper

3D Object Recognition By Scale Space Feature Tracking And Subtemplate Matching
Author(s): H. T. Tsui; K. C. Chu
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Paper Abstract

A method to recognize 3D objects by detecting features at multiple scales and subtemplate matching is proposed. Since surface reconstruction is not required, great save in computing time is possible. Depth map data of an object is first smoothed by Gaussian filtering at the coarsest scale and the Gaussian curvature at each point is computed. Extremal points are determined and an extremal point region(EPR) associated with each extremal point is defined. A spherical window which is invariant with rotation in 3-space is used to extract a surface patch around each extremal point for subtemplate matching. Processing and subtemplate matching are repeated at next finer scale to resolve ambiguities. Since the EPRs at different scales form an organized tree, computing effort is saved at this step by applying 2D scale-space tracking to limit the search regions for extremal points. If necessary, this step may be repeated at still finer scales until the effect of noise is significant, or the finest of resolution is reached. The method is suitable for irregular-shaped object recognition and early experiments using real data are very encouraging.

Paper Details

Date Published: 27 March 1989
PDF: 9 pages
Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); doi: 10.1117/12.960322
Show Author Affiliations
H. T. Tsui, The Chinese University of Hong Kong (China)
K. C. Chu, The Chinese University of Hong Kong (China)

Published in SPIE Proceedings Vol. 1002:
Intelligent Robots and Computer Vision VII
David P. Casasent, Editor(s)

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