Share Email Print
cover

Proceedings Paper

Information-theoretic bounds on target recognition performance
Author(s): Avinash Jain; Pierre Moulin; Michael I. Miller; Kannan Ramchandran
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This paper derives bounds on the performance of statistical object recognition systems, wherein an image of a target is observed by a remote sensor. Detection and recognition problems are modeled as composite hypothesis testing problems involving nuisance parameters. We develop information- theoretic performance bounds on target recognition based on statistical models for sensors and data, and examine conditions under which these bounds are tight. In particular, we examine the validity of asymptotic approximations to probability of error in such imaging problems. Applications to target recognition based on compressed sensor image data are given. This study provides a systematic and computationally attractive framework for analytically characterizing target recognition performance under complicated, non-Gaussian models, and optimizing system parameters.

Paper Details

Date Published: 17 August 2000
PDF: 12 pages
Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); doi: 10.1117/12.395580
Show Author Affiliations
Avinash Jain, Qualcomm Inc. (United States)
Pierre Moulin, Univ. of Illinois/Urbana-Champaign (United States)
Michael I. Miller, Johns Hopkins Univ. (United States)
Kannan Ramchandran, Univ. of California/Berkeley (United States)


Published in SPIE Proceedings Vol. 4050:
Automatic Target Recognition X
Firooz A. Sadjadi, Editor(s)

© SPIE. Terms of Use
Back to Top