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Multiple degree of freedom object recognition using optical relational graph decision netsMultiple-degree-of-freedom object recognition concerns objects with no stable rest position with all scale, rotation, and aspect distortions possible. It is assumed that the objects are in a fairly benign background, so that feature extractors are usable. In-plane distortion invariance is provided by use of a polar-log coordinate transform feature space, and out-of-plane distortion invariance is provided by linear discriminant function design. Relational graph decision nets are considered for multiple-degree-of-freedom pattern recognition. The design of Fisher (1936) linear discriminant functions and synthetic discriminant function for use at the nodes of binary and multidecision nets is discussed. Case studies are detailed for two-class and multiclass problems. Simulation results demonstrate the robustness of the processors to quantization of the filter coefficients and to noise.
Document ID
19880050916
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Casasent, David P.
(Carnegie-Mellon Univ. Pittsburgh, PA, United States)
Lee, Andrew J.
(Carnegie-Mellon University Pittsburgh, PA, United States)
Date Acquired
August 13, 2013
Publication Date
May 1, 1988
Publication Information
Publication: Applied Optics
Volume: 27
ISSN: 0003-6935
Subject Category
Cybernetics
Accession Number
88A38143
Distribution Limits
Public
Copyright
Other

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