Joint recognition and discrimination in nonlinear feature space

Ashit Talukder; David P. Casasent

doi:10.1117/12.290291

26 September 1997 Joint recognition and discrimination in nonlinear feature space

Ashit Talukder, David P. Casasent

Proceedings Volume 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling; (1997) https://doi.org/10.1117/12.290291
Event: Intelligent Systems and Advanced Manufacturing, 1997, Pittsburgh, PA, United States

Abstract

A new general method for linear and nonlinear feature extraction is presented. It is novel since it provides both representation and discrimination while most other methods are concerned with only one of these issues. We call this approach the maximum representation and discrimination feature (MRDF) method and show that the Bayes classifier and the Karhunen- Loeve transform are special cases of it. We refer to our nonlinear feature extraction technique as nonlinear eigen- feature extraction. It is new since it has a closed-form solution and produces nonlinear decision surfaces with higher rank than do iterative methods. Results 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 to the classification and pose estimation of two similar objects (representation and discrimination).

Citation Download Citation

Ashit Talukder and David P. Casasent "Joint recognition and discrimination in nonlinear feature space", Proc. SPIE 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, (26 September 1997); https://doi.org/10.1117/12.290291

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