Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrics and analysis of experimental data

Vladimir G. Krasilenko; Alexander I. Nikolsky; Yuriy A. Bozniak

doi:10.1117/12.458380

6 March 2002 Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrics and analysis of experimental data

Vladimir G. Krasilenko, Alexander I. Nikolsky, Yuriy A. Bozniak

Author Affiliations +

Proceedings Volume 4731, Sensor Fusion: Architectures, Algorithms, and Applications VI; (2002) https://doi.org/10.1117/12.458380
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

The given paper suggest recognition algorithms of multilevel images of multicharacter identification objects. These algorithms are based on application of linear (nonlinear) equivalent (nonequivalent) space-dependent similarity means of normalized matrix data as criterial (discriminant) functions. The results of modeling and experimental results have shown that such nonlinear-equivalent algorithms process higher discriminant properties and operating characteristics, especially in case of considerable (up to 50 %) noise level content of images.

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Vladimir G. Krasilenko, Alexander I. Nikolsky, and Yuriy A. Bozniak "Recognition algorithms of multilevel images of multicharacter identification objects based on nonlinear equivalent metrics and analysis of experimental data", Proc. SPIE 4731, Sensor Fusion: Architectures, Algorithms, and Applications VI, (6 March 2002); https://doi.org/10.1117/12.458380

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