A new physical non-stochastic N-d model for target discrimination is presented. The model is based on Tensor Invariants and overrides usual stochastic procedure limitations problems characterized by FP and FN. The computational model is related directly to physical world, and it offers three major operational advantages over previous methods. at least. The first advantage is progressive automatic model generation of the Complete Minimum Set of Tensor Invariants. The second one is the reduced computational power requirements over traditional method. Finally. target precision drives automatic model generation trough subsequent steps. In fact model precision is increased at each step. Robust discrimination or machine number representation saturation ends the computational process. Machine number representation saturation state suggests more power computational resource requirements for critical mission achievement. The general approach is tested on selected 2-D image database and preliminary results are presented.

Tensor invariant model for target discrimination

DACQUINO, GIANFRANCO;FIORINI, RODOLFO;
2002-01-01

Abstract

A new physical non-stochastic N-d model for target discrimination is presented. The model is based on Tensor Invariants and overrides usual stochastic procedure limitations problems characterized by FP and FN. The computational model is related directly to physical world, and it offers three major operational advantages over previous methods. at least. The first advantage is progressive automatic model generation of the Complete Minimum Set of Tensor Invariants. The second one is the reduced computational power requirements over traditional method. Finally. target precision drives automatic model generation trough subsequent steps. In fact model precision is increased at each step. Robust discrimination or machine number representation saturation ends the computational process. Machine number representation saturation state suggests more power computational resource requirements for critical mission achievement. The general approach is tested on selected 2-D image database and preliminary results are presented.
Proceedings of SPIE Vol. 4718 (2002) © 2002
Tensor Invariants. Progressive Model Generation. Automatic Classification. Physical Computational Model
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/573665
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