Asymptotic performance analysis of Bayesian target recognition

被引：23
作者：
Grenander, U [1 ]
Srivastava, A
Miller, MI
机构：
[1] Brown Univ, Div Appl Math, Providence, RI 02912 USA
[2] Florida State Univ, Dept Stat, Tallahassee, FL 32306 USA
[3] Johns Hopkins Univ, Dept Elect & Comp Engn, Baltimore, MD 21218 USA
来源：
IEEE TRANSACTIONS ON INFORMATION THEORY | 2000年 / 46卷 / 04期
关键词：
Bayesian ATR; deformable templates; Laplace's asymptoties; nuisance integration;
D O I：
10.1109/18.850712
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This correspondence investigates the asymptotic performance of Bayesian target recognition algorithms using deformable-template representations. Rigid computer-aided design (CAD) models represent the underlying targets; low-dimensional matrix Lie-groups (rotation and translation) extend them to particular instances. Remote sensors observing the targets are modeled as projective transformations, converting three-dimensional scenes into random images. Bayesian target recognition corresponds to hypothesis selection in the presence of nuisance parameters; its performance is quantified as the Bayes' error. Analytical expressions for this error probability in small noise situations are derived, yielding asymptotic error rates for exponential error probability decay.
引用
收藏
页码：1658 / 1665
页数：8
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