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Proceedings Paper

Synthetic Discriminant Functions For Three-Dimensional Object Recognition
Author(s): David Casasent; B.V.K. Vijaya Kumar; Vinod Sharma
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Paper Abstract

The synthetic discriminant function concept together with its modifications of maximum common information filters and decorrelation transformations are reviewed. We then advance a unified procedure for determining the coefficients for such linear combination filters for recognition of objects in different orientations and from different aspect views. Our formulation utilizes only deterministic techniques and a correlation matrix observation space. This formulation is most attractive for the realization of shift-invariant filters for use in correlator architectures. We then advance the highlights of our initial results on the performance of this new type of generalized shift-invariant filter.

Paper Details

Date Published: 23 May 1983
PDF: 7 pages
Proc. SPIE 0360, Robotics and Industrial Inspection, (23 May 1983); doi: 10.1117/12.934095
Show Author Affiliations
David Casasent, Carnegie-Mellon University (United States)
B.V.K. Vijaya Kumar, Carnegie-Mellon University (United States)
Vinod Sharma, Carnegie-Mellon University (United States)

Published in SPIE Proceedings Vol. 0360:
Robotics and Industrial Inspection
David P. Casasent, Editor(s)

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