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General methodology for simultaneous representation and discrimination of multiple object classes

[+] Author Affiliations
Ashit Talukder, David Casasent

Carnegie Mellon University, Department of Electrical and Computer Engineering, Laboratory for Optical Data Processing, Pittsburgh, Pennsylvania?15213

Opt. Eng. 37(3), 904-913 (Mar 01, 1998). doi:10.1117/1.601925
History: Received June 3, 1997; Revised Aug. 4, 1997; Accepted Aug. 10, 1997
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Abstract

We address a new general method for linear and nonlinear feature extraction for simultaneous representation and classification. We call this approach the maximum representation and discrimination feature (MRDF) method. We develop a novel nonlinear eigenfeature (NLEF) extraction technique to represent data with closed-form solutions and use it to derive a nonlinear MRDF algorithm. Results of the MRDF method on synthetic databases are shown and compared with results from standard Fukunaga-Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem (discrimination) and for classification and pose estimation of two similar objects under 3-D aspect angle variations (representation and discrimination). © 1998 Society of Photo-Optical Instrumentation Engineers.

© 1998 Society of Photo-Optical Instrumentation Engineers

Citation

Ashit Talukder and David Casasent
"General methodology for simultaneous representation and discrimination of multiple object classes", Opt. Eng. 37(3), 904-913 (Mar 01, 1998). ; http://dx.doi.org/10.1117/1.601925


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